# Enquire — Full Site Content

> Machine-readable snapshot of every public page plus all published blog posts. Generated from the live site.
> Generated: 2026-10-08T13:44:53.031Z

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## Home — https://enquire.ai/

# Better Questions. Better Insights. Better Decisions.
The best analysts don't outperform because they work harder. They outperform because they investigate differently.
Enquire helps organizations apply institutional-grade research methodologies to every strategic question: combining rigorous analytical frameworks, continuously evolving research, and expert insight to help teams ask better questions, uncover better insights, and make better decisions.
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Platform Overview Request a demo

The workflow

## Four stages, one continuous research cycle.
Every engagement moves through the same arc, powered by Research Center and Expert Network working in concert.

01

### Discover
Map the landscape, surface the debates, orient fast on companies, industries, and themes.
Powered by
Research Center

02

### Interrogate
Pressure test assumptions with targeted expert queries, AI-led interviews, and live calls.
Powered by
Expert Network

03

### Validate
Triangulate evidence across analysis and expert input to confirm what you need to believe.
Powered by
Research Center + Expert Network

04

### Decide
Move with conviction, and feed every insight back into a compounding knowledge base.
Powered by
Research Center

Powered by
Research Center

Powered by
Research Center + Expert Network

### Discover
Map the landscape, surface the debates, orient fast on companies, industries, and themes.

### Interrogate
Pressure test assumptions with targeted expert queries, AI-led interviews, and live calls.

### Validate
Triangulate evidence across analysis and expert input to confirm what you need to believe.

### Decide
Move with conviction, and feed every insight back into a compounding knowledge base.

Powered by
Expert Network

Powered by
Research Center

Research Center, structured analysis and frameworks
Expert Network, human insight on demand

Research Center

## Generate clarity before the market reaches consensus .
Research Center helps teams understand companies, industries, themes, and emerging risks through structured analysis designed to mirror how experienced researchers think.
Rather than delivering another collection of documents, Research Center helps teams identify key debates, evaluate assumptions, surface missing evidence, and understand what they need to believe.

Explore Research Center

- Company and industry intelligence

- Key debate identification

- Competitive and market analysis

- Risk and catalyst assessment

- Monitoring and signal tracking

- Decision-ready research workflows

- Targeted expert matching

- AI-led expert interviews

- Written expert responses

- Traditional expert calls

- Persistent knowledge capture

- Research-linked expert workflows
Expert Network

## Turn expert access into proprietary insight.
Most expert networks deliver isolated conversations. Enquire transforms expert insight into a persistent research asset.
Connect with vetted experts through structured queries, AI-led interviews, and traditional calls, all tied directly to the research questions that matter most.

Explore Expert Network

## Most platforms help you find answers. Enquire helps you ask better questions .
Anyone can summarize information; the difficult part is knowing what matters, what doesn't, what's missing, which assumptions deserve scrutiny, and what questions should come next.
Enquire applies the same structured investigative approaches used by institutional investors, private market investors, strategy teams and consulting firms to help organizations move beyond information gathering toward disciplined analysis and decision making.

## Build perspective faster .
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---

## Research Center — https://enquire.ai/research-center

Research Center

# Institutional Grade Research. Available on Demand .
Today's organizations face increasingly complex decisions.
But few have the time, or the research bench, to investigate every opportunity like top investment research teams.
Enquire makes institutional-grade analytical thinking accessible whenever important decisions need to be made.

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Overview

## Your Partner from initial inquiry to confident decision making
01

### Executive Investment Brief
A structured, defensible view on any entity, synthesized in under an hour, not weeks.

02

### Central Debates
The five debates actually moving the name, mapped against consensus and evidence.

03

### Probability-Weighted Scenarios
Bull, base, and bear cases with the assumptions, drivers, and signposts behind each.

04

### Model-Ready Value Drivers
A structured driver block you can drop straight into your model. We do the drivers. You own the model.

05

### Investment-committee Ready Reports
Ready to use tearsheets, primers and risk assessments.

06

### Citation-Grounded
Every claim is sourced. Every output is audit-trailed. Nothing to defend on faith.

Compliant by design. Every output is audit-trailed and citation-grounded, nothing you need to defend on faith.

"For a stock I don't know at all, I could definitely see this being my first stop before going deeper elsewhere."
Founder, Public Markets Investor

See it on your coverage

## See how our research frameworks uncover the key debates, evidence, and insights behind what you're evaluating today.
Request a Demo

---

## Expert Network — https://enquire.ai/expert-network

Expert Network

# Harness Human Intelligence, At Scale
Enquire sources the most relevant experts and engages them through direct interviews, AI-moderated asynchronous discussions, and network pulse approaches. Every engagement is grounded in company, industry, and investment context, enabling sharper questions and more valuable insights. Those insights are then MNPI-screened, distilled, and delivered in an analysis-ready format.
Request a Demo Become an Expert

- Traditional Expert Calls

- AI Led Expert Calls

- Network Pulses

- Expert Surveys

Traditional Expert Calls

## Client-led interviews with vetted operators and specialists.
Book one-on-one conversations with the operators, investors, and specialists closest to your question. Enquire sources the most relevant experts, briefs them on your context, and coordinates scheduling so your team can focus on the conversation itself. Every call is compliance-screened to reduce MNPI risk.

AI Led Expert Calls

## An informed AI moderator, not a generic call guide.
Our AI moderator is informed, injected with the Research Center's knowledge on each company, so it asks what a senior analyst would ask, probes when an expert knows something the public doesn't, and can run 20 interviews in parallel. No scheduling. No embargoes. Every transcript compliance-screened to reduce MNPI risk.

Network Pulses

## Curated Expert Opinions, Synthesized and Delivered within 24-48 Hours
Instead of a single call and a single view, we distill short form insights from multiple experts, delivered to you within 24-48 hours.

Expert Surveys

## Quantified expert signal at scale.
Field structured surveys to vetted operator and specialist panels to quantify what the call-by-call work only hints at. Designed by senior analysts, fielded in days, and delivered with the underlying respondent data, not just a summary.

Every expert interview runs through a stringent compliance process to reduce MNPI risk. Transcripts are screened; outputs are audit-trailed.

## Get expert insight moving at the speed of your process.
Bring a question, a company, or a theme. We'll show you what 24 hours of vetted expert signal looks like.
Request a Demo Explore Research Center

---

## Segments — https://enquire.ai/markets

Segments

# Built for teams operating in complex, high-stakes environments.

## Consulting
Deliver recommendations with greater confidence.
Explore

## Investor Relations
Be ready before the questions get asked.
Explore

## Private Credit
Underwrite the downside with confidence before you commit the capital.
Explore

## Private Equity
Commit with conviction.
Explore

## Public Equities
Deeper research. Sharper questions.
Explore

## Venture Capital
Diligence the private market with public-market discipline.
Explore

---

## Public Equities — https://enquire.ai/markets/public-equities

Public Equities

# Deeper research. Sharper questions. Recommendations you can defend .

## What's broken today, and how Enquire solves it.

01 Problem

### Speed, depth, or expertise, pick two.
Every tool solves a sliver. LLMs are fast but hallucinate and can't be cited. Traditional data platforms don't provide a view. Expert networks bring people but nothing compounds, the knowledge evaporates when the call ends. No single tool gives you all three, so conviction gets taxed on every name.

01 Enquire's Solution

### Speed, depth, and expertise in one compounding platform.
Enquire pairs a Research Center that runs proprietary analytical frameworks, the way an analyst actually builds a view, with an Expert Network of AI-moderated interviews and network pulse. You stop picking two. And every cycle compounds: the platform gets smarter, and the edge widens.

02 Problem

### Getting the right key debate is the hard part.
The outcome turns on a handful of questions the active investors are actually arguing about. Miss the real debate and you model the wrong thing with precision. Finding it is manual, slow, and, as analysts say themselves, "always hard."

02 Enquire's Solution

### Nail the key debate before you commit.
Enquire surfaces the central debates moving the stock, calibrates each into a more optimistic and a more skeptical cluster, shows the evidence each side keeps citing, and marks the signposts where beliefs re-rate, so you're arguing the questions that drive the outcome, not the noise.

03 Problem

### Every cold name starts from zero.
Initiating or extending coverage means days of reconstruction before you have a defensible view, reading transcripts, filings, and notes just to reach a starting point, with no clean first stop that cuts through the volume.

03 Enquire's Solution

### Your first stop on any name, in under an hour.
An Executive Investment Brief plus Consensus Debate Narrative on any entity, public or private, orients you fast: what the company is, what the market believes, and the controversies that matter. The one-pager that gets you from cold to a working view before you go deeper.

04 Problem

### Conviction gets built in an echo chamber.
Under deadline, it's easy to get missile-locked on your own thesis or blindsided by what you didn't stress-test. Confirmation bias creeps in, crowded positions hide unwind risk, and when a debate shifts you lose the thread of what actually changed.

04 Enquire's Solution

### Pressure-test the thesis, continuously.
Thesis Stress Test, Devil's Advocate, Pre-Mortem, and Red Team generate more analysis on what can go wrong than what can go right. Positioning and crowding read tells you unwind risk. A Material Change Detector keeps an audit trail of conviction, what's new, what's stable, where belief should shift.

05 Problem

### Coverage keeps expanding while the team shrinks.
AUM grows, analyst headcount doesn't, and alpha is compressing. Meanwhile the names that matter most, small caps, private companies, off-radar themes, are exactly where sell-side coverage is thin or nonexistent. Human attention doesn't scale to the footprint you're now asked to cover.

05 Enquire's Solution

### An analyst team you can assign coverage to.
Scale analytical capacity without adding headcount, accelerate junior analysts launching coverage, and get sourced, audit-trailed, citation-grounded work, on the small caps, private names, and themes traditional coverage overlooks. Swap coverage anytime your book moves.

Proof "We weren't looking for another data source. We needed to understand how investor expectations were forming and where the real uncertainties were. Enquire helped us pressure-test our thesis with expert perspective and track how the debate was evolving, which gave us much more confidence going into the decision."
Portfolio Manager, Multi-Strategy Fund

## Request a Demo
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## Private Equity — https://enquire.ai/markets/private-equity

Private Equity

# Diligence deeper. Decide faster. Commit with conviction .

## What's broken today, and how Enquire solves it.

01 Problem

### Taking control of a company with Information Gaps
Private targets have no analyst coverage, no consensus, and often aren't in any structured dataset at all. Speed, depth, or expertise, you can pick two, but on a private name you're usually reconstructing the whole picture by hand, right when the stakes of getting it wrong are highest.

01 Enquire's Solution

### Institutional-grade intelligence on any private company or market.
Enquire's Research Center stands up deep, sourced analysis on companies, themes, and markets with no coverage, pairing proprietary analytical frameworks with an Expert Network of AI-moderated interviews. It's the private-company depth already validated by an onboarded Top-5 global investment bank. Speed, depth, and expertise, on a name no one else can underwrite.

02 Problem

### The thesis lives or dies on the moat and the management team.
A buy-out thesis only works if the advantage is durable and the operators can execute for years. Separating a real moat from a good quarter, and a compounder from a fragile operator, takes depth that a data room and a management presentation won't give you.

02 Enquire's Solution

### Underwrite the moat and the management team.
Strategic Moat Analysis scores where advantage is real, durable, or eroding. Management Quality distinguishes compounders from fragile operators and flags behavioral risk early. Competitive Position maps where profits are won or lost and where the attack paths are, so you know what has to hold through the hold.

03 Problem

### Diligence runs on a clock.
Inside an exclusivity window, a small deal team reconstructs the market, the competitive set, and the risk landscape from scratch, then has to get to an IC-ready view before the clock runs out and does it again on the next target.

03 Enquire's Solution

### Jumpstart diligence with ready made reports.
An Executive Investment Brief plus the central debates orients the deal team fast: what the company is, what's actually contested, and what must be true for the thesis to work. Expand analytical capacity without adding to the team, and get to conviction before exclusivity runs out.

04 Problem

### One blindside can be defining.
A single missed downside, a supply-chain dependency, a regulatory overhang, a demand assumption that doesn't hold, can define a fund.

04 Enquire's Solution

### Stress-test the downside before you commit capital.
Thesis Stress Test, Pre-Mortem, Gray Swan, and Red Team put more scrutiny on what can go wrong than what can go right, surfacing the second-order exposures and reading cross-market signals like credit spreads that move earlier. Deliberate downside analysis gives you warning signs you might otherwise miss.

05 Problem

### Sourcing and value creation don't scale across the portfolio.
Proprietary deal flow means finding adjacencies and bolt-ons before competitors do. Post-close, operating partners are asked to drive value across every holding at once. Diligence work dies in a data room, the team is small, and human attention doesn't scale to the surface area.

05 Enquire's Solution

### Sourcing and value creation that compound.
PE Opportunities and Acquisition/Divestiture modules surface bolt-ons and transaction targets in a company's ecosystem. Weekly-cadence monitoring tracks what changed across the hold. It's an independent research team you can assign across the whole portfolio, operating leverage without headcount.

Proof "We weren't looking for another data source. We needed to understand how investor expectations were forming and where the real uncertainties were. Enquire helped us pressure-test our thesis with expert perspective and track how the debate was evolving, which gave us much more confidence going into the decision."
Portfolio Manager, Multi-Strategy Fund

## Request a Demo
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## Private Credit — https://enquire.ai/markets/private-credit

Private Credit

# Underwrite the downside with confidence before you commit the capital.

## What's broken today, and how Enquire solves it.

01 Problem

### You're lending into the dark.
Borrowers are private, often unrated, with no analyst coverage and thin public disclosure. Speed, depth, or expertise, you can pick two, but on a private credit you're underwriting cash flows and a capital structure you can barely see from the outside.

01 Enquire's Solution

### Institutional-grade underwriting on any private borrower.
Enquire's Research Center stands up deep, sourced analysis on private, unrated companies with no coverage, triangulating filings, transcripts, conference remarks, suppliers, and customers into a coherent view even where a formal consensus doesn't exist. Paired with an Expert Network of AI-moderated interviews, it's the private-company depth already validated by an onboarded Top-5 global investment bank.

02 Problem

### Your upside is capped; your downside isn't.
Credit is asymmetric. The best case is you get your coupon and your principal back, so the entire job is pricing what can go wrong: cash-flow durability, refinancing risk, and where you actually sit in the stack. Misjudge the downside and the return math breaks.

02 Enquire's Solution

### Underwrite the downside first.
A probability-weighted Risks & Uncertainties Map, Downside Case Pre-Mortem, Thesis Failure Map, and Gray Swan reconstruct plausible failure paths before you commit. Value Chain surfaces the dependencies that threaten cash flows, and Management Quality separates durable operators from fragile ones, so you know what has to hold for the credit to perform.

03 Problem

### New borrower, new sponsor, new sector — how to gain same conviction.
The credit team reconstructs the business, the competitive position, and the risk picture from scratch under a deal timeline, then does it all again on the next facility, with nothing carrying forward.

03 Enquire's Solution

### Credit-committee-ready analysis in a fraction of the time.
An Executive Investment Brief plus the central debates orient the team fast: what the business is, what's actually contested, and what must stay true for the borrower to service the debt. Expand underwriting capacity without adding to the team, and get to a defensible view before the deadline.

04 Problem

### Deterioration shows up late.
By the time a covenant trips or a rating moves, the story has usually already changed. Other parts of the capital structure, credit spreads, CDS, often move first, but reading those signals across an entire book by hand doesn't scale.

04 Enquire's Solution

### See stress before the covenant trips.
Cross-Market Signals reads the whole capital structure, credit spreads, CDS, options-implied values, for what other markets are saying about a name, catching the early warning that often shows up before the fundamentals do. Material-change tracking then flags exactly what shifted since your last review.

05 Problem

### Monitoring a book of positions doesn't scale.
You hold to maturity. Every borrower needs ongoing surveillance for years, but the team is small, the book keeps growing, and human attention doesn't stretch to continuous coverage of every name until it pays off.

05 Enquire's Solution

### Continuous surveillance across the whole book.
Weekly-cadence monitoring re-runs the analysis and isolates what materially changed on every position, an independent credit team you can assign across the entire portfolio, holding coverage from origination through maturity, without adding headcount.

Proof "Enquire reads across the full capital structure, credit spreads, CDS, and options-implied values, for what other markets are already signaling about a borrower. As the founders put it in demos, the CDS market is often the canary in the coal mine: when something is stressed, it can show up there before it surfaces anywhere else. That same private-company depth is validated by an onboarded Top-5 global investment bank, and private-equity and banking teams already run the platform for diligence today."
Capability demonstrated in live product demos

## Request a Demo
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## Venture Capital — https://enquire.ai/markets/venture-capital

Venture Capital

# Diligence the private market with public-market discipline .

## What's broken today, and how Enquire solves it.

01 Problem

### The private market runs dark.
The companies you diligence have no analyst coverage, no consensus view, and are often absent from vendor lists entirely. Speed, depth, or expertise, public investors get to pick two. On a private name, you rarely get even one.

01 Enquire's Solution

### Institutional-grade coverage on any private name.
Enquire's Research Center stands up an analyst-grade knowledge base on the companies, themes, and markets the rest of the world doesn't cover, pairing proprietary analytical frameworks with an Expert Network of AI-moderated interviews. Speed, depth, and expertise, on a name no one else can see.

02 Problem

### The founder's narrative is the only narrative.
Every deck tells a clean story. Separating durable insight from the pitch, the real moat, the real competitive set, the real risk, means weeks of expert calls you don't have in a hot round, so conviction leans on the story you were told.

02 Enquire's Solution

### Pressure-test the pitch against reality.
Enquire maps the real competitive set, scores the moat, and surfaces the underappreciated surprises the founder's deck leaves out, grounded in evidence and sources, not narrative. It tells you where a crowded space still has room for a winner and where the profit pool gets competed away.

03 Problem

### Diligence starts from a blank page, every time.
New sector, new geography, new company. You reconstruct the landscape from scratch under deal-clock pressure, then do it all over again on the next opportunity, with nothing carrying forward.

03 Enquire's Solution

### Your first stop on any deal, in under an hour.
An Executive Investment Brief plus the central debates orients you fast: what the company is, what's actually contested, and what has to be true for the thesis to work. Blank page to a working view before the next partner meeting.

04 Problem

### Conviction forms in a competitive sprint.
Rounds move fast and FOMO is real. It's easy to get missile-locked on the thesis you want to be true, or blindsided by the risk no one on the deal team pressure-tested before the wire went out.

04 Enquire's Solution

### Build conviction with discipline, not FOMO.
Thesis Stress Test, Devil's Advocate, Pre-Mortem, Gray Swan, and Red Team put more scrutiny on what can go wrong than what can go right, probability-weighted, with the signposts that would change your mind mapped before you commit.

05 Problem

### The work doesn't compound, and the portfolio doesn't scale.
Diligence memos die in a folder. Your team is small, coverage keeps widening across sectors and stages, and the operating help portfolio companies need outruns the hours you have. Human attention doesn't scale to the surface area you're now asked to cover.

05 Enquire's Solution

### Coverage and portfolio support that compound.
Enquire is an independent research team you can assign coverage to, across every sector and stage, that preserves debates, assumptions, and evidence over time and gets smarter each cycle. Analytical and operating capacity for the whole portfolio, without adding headcount.

Proof "A venture investor tried to break the platform with an obscure private software company, repo-collateral software, roughly 30 competitors, zero analyst coverage. Enquire surfaced a real, underappreciated surprise the consensus had missed entirely: a Euroclear embedding and Tier-1 bank wins with Citi and Nomura, signals invisible everywhere else because the company was simply absent from vendor lists. The investor was shocked it worked."
Capability demonstrated in a recorded product demo

## Request a Demo
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## Consulting — https://enquire.ai/markets/consulting

Consulting

# An integrated platform to research deeper, ask more intelligent questions, and deliver recommendations with greater confidence .

## What's broken today, and how Enquire solves it.

01 Problem

### Every engagement starts with the same race against the clock.
Consulting teams spend valuable time gathering background information, reviewing fragmented sources, and building baseline knowledge before higher-value analysis can even begin.

01 Enquire's Solution

### Turn weeks of research into hours.
The Research Center rapidly synthesizes public information across companies, industries, technologies, competitors, and markets into structured, evidence-backed research. Consultants can begin engagements with a comprehensive understanding of the landscape and focus their time on strategic analysis rather than information gathering.

02 Problem

### Great recommendations require the right questions, not just more information.
Knowing what to investigate is often harder than finding data. Critical issues, assumptions, and blind spots can remain hidden without experienced subject matter expertise.

02 Enquire's Solution

### Research guided by expert thinking.
Enquire helps consultants approach unfamiliar industries and companies like experienced specialists by identifying important themes, surfacing likely areas for deeper investigation, and generating expert-level lines of inquiry that help teams ask better questions throughout an engagement.

03 Problem

### Public information only tells part of the story.
Commercial diligence, operational assessments, and strategic engagements often depend on insights that are unavailable in reports, filings, or news coverage.

03 Enquire's Solution

### Connect public intelligence with proprietary insight.
Enquire's Research Center automatically develops interview guides informed by its research findings, enabling AI or human moderators to conduct focused conversations with relevant industry experts. The result is a more efficient path from public evidence to differentiated market intelligence.

04 Problem

### Scaling research usually means scaling people.
As projects multiply, firms often face difficult tradeoffs between hiring additional researchers, overloading existing teams, or limiting the number of simultaneous engagements they can support.

04 Enquire's Solution

### Expand capability, not headcount.
Enquire enables consultants to investigate multiple companies, markets, competitors, or technologies simultaneously, extending the analytical capacity of existing teams without expanding the research bench. Firms gain greater flexibility to scale project workloads while keeping staffing lean.

05 Problem

### Finding the right experts slows every engagement.
Recruiting, vetting, and scheduling subject matter experts is often a separate workflow that delays projects and introduces unnecessary operational overhead.

05 Enquire's Solution

### Research and experts, already connected.
Enquire combines a continuously available Expert Network with institutional-grade research in a single workflow. Consultants can move seamlessly from identifying knowledge gaps to engaging relevant experts, accelerating insight generation without rebuilding an expert network for every engagement.

## Sharper intelligence, more confident decisions.
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## Investor Relations — https://enquire.ai/markets/investor-relations

Investor Relations

# Be ready before the questions get asked .

## What's broken today, and how Enquire solves it.

01 Problem

### Data everywhere, clarity nowhere.
Transcripts, filings, ownership, sell-side notes, and investor feedback all live in silos. The gap isn't information, it's synthesis.

01 Enquire's Solution

### One intelligence layer for capital markets.
Enquire unifies transcripts, filings, ownership, sell-side, peer signals, and investor feedback into a single synthesized view built for IR, not another dashboard.

02 Problem

### The market forms its view before you hear the question.
Investor debates build weeks before earnings, conferences, or guidance. By the time the CFO gets the question, the narrative has already moved.

02 Enquire's Solution

### See the debates before the call.
Enquire maps the controversies, expectation shifts, and questions shaping the market's view so leadership walks into every meeting already ahead of them.

03 Problem

### Earnings prep runs on manual work and late nights.
Every quarter, IR teams read transcripts, track peers, draft Q&A, and brief executives under deadline pressure, starting from scratch each time.

03 Enquire's Solution

### Briefings, not fire drills.
Enquire turns deep analysis into executive pre-reads, likely-Q&A sets, scripts, talking points, and board-ready summaries on demand, every cycle.

04 Problem

### The equity story is under constant pressure.
Investors weigh your narrative against peers, consensus, capital allocation, margins, and emerging risks, continuously, not just at earnings.

04 Enquire's Solution

### Pressure-test the equity story, continuously.
Enquire tracks peers, disclosures, guidance changes, and market reactions in real time, flagging where your narrative is strong, exposed, or unconvincing.

05 Problem

### Every investor meeting still feels generic.
Tailoring by thesis, ownership, and prior concerns takes hours IR teams don't have, so most conversations default to the same script.

05 Enquire's Solution

### Personalize every investor conversation.
Enquire connects investor context, thesis fit, ownership history, prior debates, to the company-specific messages that matter most to each audience.

Proof "Most IR teams already have the raw inputs, but I was impressed at how quickly this turned fragmented signals into a coherent outside-in view of the debate, the pressure points, and the questions leadership needed to be ready for."
Investor Relations Lead, Fortune 500 Company

## Request a Demo
Start free, or book a guided demo on a name you cover.
Request a Demo

---

## About — https://enquire.ai/about

# About Our Team

## Barry Hurewitz
Chief Executive Officer
Chief Executive Officer of Enquire. Former UBS Group Managing Director who founded UBS Evidence Lab and Morgan Stanley's AlphaWise; Harvard MBA and Baker Scholar.
Read full bio →

## Fatih Orhan
Chief Financial Officer
Chief Financial Officer of Enquire, leading finance and capital management. Completed Columbia Business School's CFO program; former Director at the U.S. Chamber of Commerce.
Read full bio →

## Isaac Esseku, CFA
Chief Operating Officer
Chief Operating Officer of Enquire, CFA. Founding member and COO of UBS Evidence Lab, which he scaled from five people to 200+; former Deutsche Bank. Wharton MBA.
Read full bio →

## Cemil Kor
Chief Product Officer
Chief Product Officer of Enquire, where he has built the product for nearly a decade, pairing proprietary AI with a global expert network, rising from design leadership to CPO.
Read full bio →

## Arnaud Jaspart
Chief Technology Officer
Chief Technology Officer of Enquire, leading its AI and platform engineering for nearly six years. Former VP of Technology at New Frontier Data; built capital-markets systems at SunGard (now FIS).
Read full bio →

## Tugba Colpan
Chief Commercial Officer
Chief Commercial Officer of Enquire. Spent seventeen years as Global Head of Sales at Medley Global Advisors; earlier at Vanda Research and Eurasia Group. LSE master's.
Read full bio →

## Michael Afshar
Chief Compliance Officer
Chief Compliance Officer of Enquire, with seven years building its Expert Network. Former US Country Head at Lynk and founding equity partner at Aviate Global, acquired by Northern Trust.
Read full bio →

---

## Security — https://enquire.ai/security

Security & Compliance

# Compliant by design.
Audit-trailed. Citation-grounded. MNPI-screened. Enquire is built for institutional buyers who need to defend every research output they use.

## Audit trail on every output
Every analysis is timestamped, versioned, and traceable to the inputs that produced it, defensible in front of compliance teams and IC.

## Citation-grounded by design
Outputs cite their sources. Every claim ties back to the document, transcript, or data point it came from.

## MNPI screening on interviews
Expert interviews, moderated by AI or 1-on-1, run through a stringent compliance process to reduce material non-public information risk.

## Data handling
Customer data is logically isolated. Documents you upload are used for your analysis only and are never used to train shared models.

## Access controls
Role-based access at the workspace level. Single sign-on available for enterprise pilots.

## Maintained by Enquire
This page is maintained by Enquire to answer common security and compliance questions. For specific contractual terms or a security review, contact us.

---

## Compliance — https://enquire.ai/compliance

Compliance

# Compliance Overview
The Enquire compliance approach is designed to provide clients with a platform in which they can monitor and manage compliance in a real-time and active manner. Client use of the platform services are auditable, transparent, and designed to protect both clients and experts.
Our systems and compliance tools help experts identify and manage conflicts and allow our clients to customize project criteria to meet their internal requirements and protocols. Enquire appreciates compliance is an integral component of the platform's use and we are committed to the highest standards of ethical and trustworthy consultation. Should you have any questions or want to learn more about our evolving compliance structure, please email compliance@enquire.ai .

## Experts Agreement
As a condition of eligibility for participation on the Enquire platform, Enquire requires experts to sign, and annually re-sign, our Terms of Service. By signing, experts agree that they:

- Are not restricted by any agreements or policies to which they are subject and are permitted to participate as an expert on Enquire's platform;

- Will decline to participate in any project that would violate those agreements or any conflicts with third parties;

- Will not provide confidential (including material non-public) information to clients in any form;

- Will always keep client interests and information confidential as per the platform's Privacy Policy; and

- Will immediately terminate any project in which client requests subject matters they cannot discuss, such as confidential information (in which case, if the expert notifies us, they are entitled to payment for time spent).
View the current Terms of Service

## Training
In addition to our on-boarding and eligibility requirement of signing our Terms of Service, experts must take and pass a compliance test to measure their understanding and knowledge of what is permissible on the platform. Areas of focus include conflicts, confidentiality and privacy, restricted subject matter, communications between experts and clients, the role of the expert and work product and content rights.
The Terms of Service explains what constitutes “confidential information,” highlights to experts any confidentiality obligations they may have and emphasizes that our clients do not want and will be harmed if they receive confidential information. The Terms of Service agreement also identifies the nature of conflicts that may exist. This guidance helps experts review potential conflicts prior to each engagement and is used to determine if an expert is disqualified from a project.

## For Clients
We are aware and focused on the need for clients to have an active role in compliance oversight. We encourage clients to customize these parameters for use to reflect their individual compliance features, provide real time monitoring of projects, and post project reviews of usage and content.
Our web-based compliance tools allow clients to customize control features such as pre-clearance of calls, restrict content or companies, limit number of contacts with a specific expert and include their own questions to screen expert selection for a project.

## For Employers
Enquire understands employees of companies may need to be treated differently than independent consultants. First, employed experts are prohibited from participating in projects about their employer. Additionally, companies may have policies that prohibit outside consulting.
Enquire actively reviews publicly available corporate policies to identify such companies. We then screen experts during on boarding for eligibility and enforce company policies that prohibit employee consultation. Employers can inform us of their policies regarding outside consulting restrictions by emailing compliance@enquire.ai.

---

## Become an Expert — https://enquire.ai/become-an-expert

Become an Expert

# Lend your expertise to the questions that move markets .
Enquire connects operators, investors, and industry specialists with research teams who need real-world perspective, on their schedule, on their terms.

## Apply to the network
A quick profile is all we need to get started.

Name *

LinkedIn *

Email *

Compensation range Select a range (optional) Less than $250 / hour $250 - $500 / hour $500 - $750 / hour $750 - $1,000 / hour $1,000 - $1,500 / hour More than $1,500 / hour
Your preferred hourly rate for calls or written responses.

What kind of experience can you bring to our network? * A few sentences is plenty.
0/2000

Submit application By applying you agree to our vetting and compliance review process.
Why experts join
- Focused engagements
AI-moderated interviews and written pulses replace generic 60-minute calls with sharper, faster exchanges.

- Compliance built in
Every transcript is screened. Clear guardrails around MNPI and current-employer topics.

- You control availability
Say yes to the queries that fit your expertise. Skip the rest.

---

## Contact — https://enquire.ai/contact

# Contact us.

First name *
Last name *

Work email *
Company *
Role
Tell us more about how Enquire can help you
Send

## Prefer email?
Reach us directly at hello@enquire.ai .

---

## Privacy Policy — https://enquire.ai/privacy

Privacy

# Privacy Policy

### Privacy Policy

Last Updated: May 20, 2020

This Privacy Policy explains how Enquire, Inc. (“ Enquire “, “ our “, “ us “, and “ we “) collects, uses, and discloses information about you through our website located at www.enquire.ai (“ Site “) and related services (collectively, the “ Platform Services “) or when you otherwise interact with us.

We may change this Privacy Policy from time to time. If we make changes, we will notify you by revising the date at the top of the policy and, in some cases, we may provide you with additional notice (such as adding a statement to our homepage or sending you a notification). We encourage you to review this Privacy Policy before you use or submit any information on or through the Platform Services to stay informed about our information practices and the choices you available to you.

Collection of Information:

Information You Provide Directly to Us.

We collect information you provide directly to us, for example, we collect information when you create an account on the Site, provide payment and billing information, provide identification verification, participate in any interactive features of the Platform Services, fill out a form, perform project and topic searches, post bids for projects, request customer support or otherwise communicate with us. The types of information we may collect include your name, phone number(s), email address, postal address, credit card or other billing information, date of birth, taxpayer ID, and any other information you choose to provide. We may combine such information with information we already have about you. We use a third-party payment processor to bill Clients and disburse fees to Experts through a payment account linked to your Enquire account for payment of any applicable project fees. The processing of payments will be subject to the terms, conditions, and privacy policies of the payment processor in addition to this Privacy Policy and our Terms of Service.

Information About Your Use of the Platform Services.

Automatically Collected Information

When you access or use the Platform Services, we automatically collect information about you, including:

-
Log Information: We collect log information about your use of the Platform Services, including the type of browser you use, access times, pages viewed, your IP address, the page you visited before navigating to the Platform Services, and Project bids.

-
Device Information: We collect information about the computer or mobile device you use to access the Platform Services, including the hardware model, operating system and version, unique device identifiers and mobile network information.

-
Information Collected by Cookies and Other Tracking Technologies: We use different technologies to collect information, including cookies and web beacons. Cookies are small data files stored on your hard drive or in device memory that help us improve the Platform Services and your experience, see which areas and features of the Platform Services are popular and count visits. Web beacons are electronic images that may be used in the Platform Services or emails and help deliver cookies, count visits and understand usage and campaign effectiveness. For more information about cookies, and how to disable them, please see “Cookies” below.

Information We Collect from Other Sources.

We may obtain information from other sources and combine that with information we collect through the Platform Services. For example, we may collect information about you from third parties, including but not limited to identity verification services, credit bureaus, mailing list providers, publicly available sources, and prohibited persons lists.

How We Use Your Information:

We use the information we collect to provide, maintain, and improve the Platform Services, such as to administer your account. We may also use the information we collect to:

-
Provide our services, process transactions, and send you related information, including confirmations and invoices;

-
Match Enquire clients with users of the Site;

-
Send you technical notices, updates, security alerts, and support and administrative messages;

-
Ensure compliance with our Terms of Service, including but not limited to identifying and preventing misuse, inappropriate behavior, fraud, or any criminal activity;

-
Respond to your comments, questions and requests and provide customer service;

-
Communicate with you about products, services, offers, and events offered by Enquire and others, and provide news and information we think will be of interest to you;

-
Monitor and analyze trends, usage and activities in connection with the Platform Services;

-
Personalize and improve the Platform Services and provide advertisements, content or features that match user account profiles or interests; and

-
Carry out any other purpose described to you at the time the information was collected

How We Share Your Information:

We will not share information about you except in the following circumstances:

-
With other users of the Site. Enquire shares the biographical and work history information of Experts with Enquire Clients. If you are a Expert, you agree that you will notify Enquire in advance of your engagement by Enquire as a consultant if there is any special sensitivity regarding the handling or sharing of your personal information;

-
With vendors, consultants, third party payment processors and other service providers who need access to such information to carry out work on our behalf;

-
In response to a request for information if we believe disclosure is in accordance with, or required by, any applicable law, regulation or legal process; including lawful requests by public authorities to meet national security or law enforcement requirements;

-
If we believe your actions are inconsistent with our user agreements or policies, or to protect the rights, property and safety of Enquire or others;

-
In connection with, or during negotiations of, any merger, sale of company assets, financing or acquisition of all or a portion of our business by another company;

-
Between and among Enquire and our current and future parents, affiliates, subsidiaries and other companies under common control and ownership; and

-
With your consent or at your direction.

We may also share aggregated or de-identified information, which cannot reasonably be used to identify you.

Social Sharing Features:

The Platform Services may offer a social network third party registration feature to create and build your Enquire profile from a third-party website such as LinkedIn. If you choose to use this feature, Enquire will use the personal information you have input to such social network to build your Enquire account profile (i.e. name, email, work experience and education). The disclosure, access, and collection of any of your information transferred from such social network will be subject to the terms and conditions of that site. We will use, store, and disclose any of your imported information in accordance with this Privacy Policy. It is your sole responsibility to review and correct any information extracted from any third-party website for which we add registration functionality in the future. By using the social network third party registration feature, you certify that you are authorized to access and use the social media account without violating any terms and conditions of that site. Connecting your social media account will in no way change or affect the relationship between you and the social media network and your relationship will be solely governed by any agreements you have with the social media network. You can end the connection between the Enquire Site and your social media account in your settings on the Site. If you terminate your relationship with a social media network, Enquire will stop receiving information from the account.

Advertising and Analytics Services:

We may allow others to provide analytics services and serve advertisements on our behalf across the Internet and in applications. These entities may use cookies, web beacons, device identifiers and other technologies to collect information about your use of the Platform Services and other websites and applications, including your IP address, web browser, mobile network information, pages viewed, time spent on pages or in apps, links clicked and conversion information. This information may be used by Enquire and others to, among other things, analyze and track data, determine the popularity of certain content, deliver advertising and content targeted to your interests on the Platform Services and other websites and better understand your online activity. For more information about interest-based ads, or to opt out of having your web browsing information used for behavioral advertising purposes, please visit www.aboutads.info/choices . and/or http://www.youronlinechoices.eu if you are located in the EEA (as defined below). You may also opt-out of having your information used for advertising purposes by logging into your Enquire account and updating your account settings.

Data Retention:

The time periods for which we retain your personal information will depend on the purposes for which we use it. We will retain your personal information for so long as is necessary to provide the Platform Services, unless applicable law requires we either dispose of it or keep it longer. We may also continue to store your personal information to allow us to resolve disputes, enforce our agreements, comply with legal obligations and/or for other legally permissible purposes consistent with this Privacy Policy and our Terms of Service.

Transfer of Information to the U.S.:

Enquire is based in the United States and we process and store information in the U.S. As such, we and our service providers may transfer your information to, or store or access it in, jurisdictions that may not provide equivalent levels of data protection as your home jurisdiction. We will take steps to ensure that your personal data receives an adequate level of protection in the jurisdictions in which we process it.

Residents of the European Economic Area:

If you are a resident of the European Economic Area (“EEA”), you have certain rights and protections under the law regarding the processing of your personal data.

Legal Basis for Processing

If you are a resident of the EEA, when we process your personal data we will only do so in the following situations:

-
We need to use your personal data to perform our responsibilities under our contract with you (e.g., processing payments for and providing the Enquire services you have requested).

-
We have a legitimate interest in processing your personal data. For example, we may process your personal data to send you marketing communications, to communicate with you about changes to the Platform Services, and to provide, secure, and improve the Platform Services.

-
We have your consent to do so.

Data Subject Requests

If you are a resident of the EEA, you have the right to access personal data we hold about you and to ask that your personal data be corrected, erased, or transferred. You may also have the right to object to, or request that we restrict, certain processing. If you would like to exercise any of these rights, you may contact us as indicated below.

Questions or Complaints

If you are a resident of the EEA and have a concern about our processing of personal data that we are not able to resolve, you have the right to lodge a complaint with the data privacy authority where you reside. For contact details of your local Data Protection Authority, please see: http://ec.europa.eu/justice/data-protection/article-29/structure/data-protection-authorities/index_en.htm

Your Choices

Account Information:

You have the right to review, change and delete information about you at any time. You can do so by editing your profile on the Site or emailing us at support@enquire.ai . Deleting all of your information may result in account deactivation. You can choose to have your account deleted from the system by emailing support@enquire.ai , but note that we may retain certain information as required by law or for legitimate business purposes. We may also retain cached or archived copies of information about you for a certain period of time.

Cookies:

Most web browsers are set to accept cookies by default. If you prefer, you can usually choose to set your browser to remove or reject browser cookies. Please note that if you choose to remove or reject cookies, this could affect the availability and functionality of the Platform Services.

Marketing E-Mails:

We may send you periodic promotional or informational e-mails. You may opt-out of such communications by following the opt-out instructions contained in the e-mail. Please note that it may take up to 5 business days for us to process opt-out requests. If you opt-out of receiving e-mails about recommendations or other information we think may interest you, we may still send you e-mails about your account on the Site or the Platform Services.

Links to Third-Party Websites:

There may be links to other websites on the Site, but the inclusion of these links does not represent our endorsement, authorization or affiliation with the other websites. This Privacy Policy and our Terms of Service do not cover any third-party websites. You should consult the terms and conditions of any third-party websites to understand their privacy policies and how they collect and use your information.

Your California Privacy Rights:

If you are a California resident, California law may provide you with additional rights regarding our use of your personal information. California’s “Shine the Light” law (Civil Code Section § 1798.83) permits users of the Platform Services that are California residents to request certain information regarding our disclosure of personal information to third parties for their direct marketing purposes. We do not share your personal information with third parties for these purposes.

Do Not Track:

Some web browsers have a “Do Not Track” feature. This feature lets you tell websites you visit that you do not want to have your online activity tracked. As these features are not yet uniform across browsers so the Platform Services do not currently honor these “Do Not Track” signals.

Contact us

If you have any questions about this Privacy Policy, please contact us at:

Enquire, Inc.

1233 20th Street NW

Suite 470

Washington, DC 20036

support@enquire.ai

---

## Client Terms — https://enquire.ai/terms-client

Client Terms

# Client Terms of Service

### Terms of Service

Client

Last Updated: [February 18, 2023]

Client Terms of Service

These Terms of Service (these “ Terms ”) are a contract between you, in your individual capacity or the business entity you represent (“ you ” or “ Client ”), and Enquire AI, Inc. (collectively, “ Company ”, “ we ”, or “ us ”). “ You ” and “ your ” as used herein will refer and apply to that entity and the persons that can access the Platform Services (as defined herein) on its behalf. Please read this entire agreement carefully. You must read, agree to, and accept these terms and conditions in full in order to use the website located at www.enquire.ai (or any successor sites or URLs) (the “ Website ”) and related software and services (collectively, the “ Platform Services ”). For more information about our collection of your information, please visit our privacy policy (or any successor link) (“ Privacy Policy” ) as may be in effect and modified by us from time to time.

BY CLICKING [“ I ACCEPT ”] OR BY USING THE PLATFORM SERVICES, YOU AGREE TO THESE TERMS. BY AGREEING TO THESE TERMS, EXCEPT FOR IN (A) CERTAIN TYPES OF DISPUTES DESCRIBED IN SECTION 26, (B) WHERE YOU EXERCISE YOUR RIGHT TO OPT OUT OF ARBITRATION AS DESCRIBED IN SECTION 26, OR (C) TO THE EXTENT PROHIBITED BY LAW, DISPUTES BETWEEN YOU AND COMPANY WILL BE RESOLVED SOLELY ON AN INDIVIDUAL BASIS AND NOT AS A CLASS ARBITRATION, CLASS ACTION, ANY OTHER KIND OF REPRESENTATIVE PROCEEDING, OR BY JURY TRIAL. IF YOU DO NOT AGREE TO THESE TERMS, DO NOT USE THE PLATFORM SERVICES.

Subject to the conditions set forth herein, Company may, in its sole discretion, amend these Terms by posting a revised version on the Website. The “Last Updated” date above indicates when these Terms were last changed. Company will provide reasonable advance notice of any amendment that includes a material change, by posting the updated Terms on the Website, providing notice on the Website, and/or sending you notice by email. Unless we say otherwise in Company’s notice, any changes to these Terms will take effect on the last updated date therein and your continued use of the Platform Services after we provide such notice will confirm your acceptance of the changes. If you do not agree to the amended Terms, you must immediately stop using the Platform Services. We may indicate that different or additional terms, conditions, guidelines, policies, or rules apply in relation to some of the Platform Services (“ Supplemental Terms ”). Any Supplemental Terms become part of your agreement with us if you use the applicable Platform Services, and if there is a conflict between these Terms and the Supplemental Terms, the Supplemental Terms will control for that conflict. Any changes to these Terms will not apply to any dispute between you and us arising prior to the date on which we posted the updated Terms incorporating such changes or otherwise notified you of such changes.

If you agree to these Terms on behalf of an entity, or in connection with providing or receiving services on behalf of an entity, you represent and warrant that you have the authority to bind that entity to these terms and agree that you are binding both you and that entity to these terms. In that event, “ you ” and “ your ” will refer and apply to you and that entity.

1. Services Overview

Company provides a technology-enabled marketplace that economically and efficiently matches sophisticated global affairs professionals offering professional services (the “ Expert Services” ), with enterprising investors and decision-makers across a variety of sectors using the Platform Services to procure such Expert Services (“ Clients ”) for a variety of projects (“ Projects ”). Under these Terms, Company provides the Platform Services to you as a Client, which include curating Experts and Projects, and functioning as the payment processor for you and Experts. Experts and Clients must register for an account to use the Platform Services (each, a “ User ”). Unless otherwise set forth in a separate and mutually agreed writing, Company’s Services are provided to you only for your internal and non-commercial business use and not for the benefit or use of any third party.

2. Eligibility

By creating an account on the Website and making use of the Platform Services, you represent and warrant that you: (a) are a legal entity or a person who is at least eighteen (18) years old (or have otherwise reached the age of majority in the jurisdiction in which you conduct business) and capable of forming legally binding contracts under Law (as defined below); (b) are authorized to represent and act on behalf of any entity for which you are employed by or solicit Projects for; (c) are not identified on the U.S. General Services Administration’s Excluded Parties List, the U.S. Department of Commerce’s Denied Persons or Entity List, the U.S. Department of the Treasury’s Specially Designated Nationals or Blocked Persons Lists, or the Department of State’s Debarred Parties List, or any similar list of individuals limited by U.S. export control laws and regulations or other economic sanctions of the US, other government or multilateral organization; (d) are not considered to be a “Politically Exposed Person”; (e) have not been convicted or charged, or pled guilty or nolo contendere to any felony or any misdemeanor involving securities fraud or investment-related business; and (f) are not and have not been the subject of any regulatory or enforcement proceeding brought by any securities, commodities or other financial regulatory authority, self-regulatory organizations or other law enforcement body, including the U.S. Securities and Exchange Commission and the Financial Industry Regulatory Authority.

3. Account Registration

To sign up to use the Platform Services, you must create an account profile and provide accurate and complete information. This includes providing us with your legal or business name, address, billing/payment information, contact information, including the name and contact information of a representative authorized to act on your entity’s behalf, and any other relevant information to your use of the Platform Services as determined in Company’s sole discretion. It is your responsibility to keep your account information updated. Should your eligibility for a Company account as outlined above change during your time using the Platform Services, you must notify Company within a reasonable amount of time, but no later than thirty (30) days after such change by emailing support@enquire.ai . You shall not use the name or email of (a) another person with the intent to impersonate that person, or (b) an entity without appropriate authorization. We reserve the right, in our sole discretion, to refuse, suspend, or revoke your account via the Platform Services upon discovery that any account information provided is untrue, inaccurate, or incomplete, or such information or other conduct otherwise violates these Terms, or for any other reason or no reason in our sole discretion.

4. Restrictions

(a) You agree that: (i) you will not use the Platform Services if you are not fully able and legally competent to agree to these Terms; (ii) you will only use the Platform Services in full compliance with all applicable statutes, laws, ordinances, regulations, rules, codes, orders, constitutions, treaties, common laws, judgments, decrees, other requirements or rules of law of any federal, state, local or foreign government or political subdivision or agency thereof (collectively, “ Law ” or “ Laws ”) and these Terms; and (iii) you will not use the Platform Services for fraudulent purposes or to engage in any offensive, indecent or objectionable conduct. Further, you will not, in connection with the Platform Services:

-
Engage in any harassing, threatening, intimidating, predatory, or stalking conduct;

-
Use or attempt to use another user’s account or information without authorization from that user and us;

-
Copy, reproduce, distribute, publicly perform, or publicly display all or portions of the Platform Services, except as expressly permitted by us or our licensors in writing;

-
Modify the Platform Services, remove any proprietary rights notices or markings, or otherwise make any derivative works based upon the Platform Services;

-
Use the Platform Services in any manner that could interfere with, disrupt, negatively affect, or inhibit other users from fully enjoying the Platform Services or that could damage, disable, overburden, or impair the functioning of the Platform Services in any manner;

-
Reverse engineer any aspect of the Platform Services or do anything that might discover or reveal source code, or bypass or circumvent measures employed to prevent or limit access to any part of the Platform Services;

-
Use any data mining, robots, or similar data gathering or extraction methods designed to scrape or extract data from the Platform Services;

-
Develop or use any applications or software that interact with the Platform Services without our prior written consent;

-
Send, distribute, or post spam, unsolicited or bulk commercial electronic communications, chain letters, or pyramid schemes; or

-
Use the Platform Services for any illegal or unauthorized purpose, or engage in, encourage, or promote any activity that violates these Terms.

(b) You further agree that you may not, and may not cause or permit others to: (a) modify, make derivative works of, disassemble, decompile, reverse engineer, reproduce, republish, download, or copy any part of the Platform Services (including data structures or similar materials); (b) access or use the Platform Services to build or support, directly or indirectly, products or services competitive to Company; or (c) license, sell, transfer, assign, distribute, outsource, permit timesharing or service bureau use of, commercially exploit, or make available the Platform Services to any third party except as permitted by these Terms.

(c) Enforcement of this Section 4 is solely at our discretion, and failure to enforce this Section 4 in some instances does not constitute a waiver of our right to enforce it in other instances.

5. Marketplace Feedback

(a) You hereby acknowledge and agree that Users publish and request Company to publish on their behalf information through the Platform Services about the User, such as feedback, geographical location, or verification of identity or credentials. However, such information is based solely on unverified data that Experts or Clients voluntarily submit to Company and does not constitute and will not be construed as an introduction, endorsement, or recommendation by Company. Company provides such information solely for the convenience of Users.

(b) You acknowledge and agree that User feedback benefits the marketplace, all Users, and the efficiency of the Platform Services and you specifically request that Company post feedback about Users, including yourself, on User profiles and elsewhere on the Platform Services.

6. Ownership; Limited License

The Services and Platform Services, including, the text, graphics, images, photographs, videos, illustrations, and other content contained therein, and all intellectual property rights therein and thereto and anything developed or delivered by or on behalf of Company under these Terms (including Deliverables as such term is defined in the Expert Terms of Service), are owned by Company or our licensors and are protected under both United States and foreign laws. Except as explicitly stated in these Terms, all rights in and to the Platform Services and Services, including all intellectual property rights therein and thereto, are reserved by us or our licensors. Any use of the Platform Services other than as specifically authorized herein, without our prior written permission, is strictly prohibited and will terminate the license granted herein and violate our intellectual property rights.

Our Network Pulse™ tool allows Users to submit questions, which Experts may review and respond to. Subject to these Terms, Client is hereby granted the non-exclusive, worldwide, limited right to use and access the Network Pulse™ responses (together with all intellectual property rights therein and thereto) ordered by such Client during a ninety (90) day period or as otherwise set forth in a mutual writing between Client and Company and solely for the internal business operations of Client. Client may allow its employees to access the Network Pulse™ responses for this purpose, and Client is responsible for their compliance with these Terms.

7. User Content & Collective Content

(a) User Content. You represent and warrant that you have the right, power and authority or have valid licenses to all data, feedback, content, or other materials you upload, post, publish, submit, create, store, share, or display through the Platform Services (collectively, the “ User Content ”) and to grant the licenses specified below. When you post or otherwise share User Content on or through the Platform Services, you understand that your User Content and any associated information may be visible to others and is non-confidential and we have no confidentiality obligation of any kind with respect to such information. Company may collect, store, and post User Content (including Expert responses and responses to Projects) within the Platform Services on an Expert’s behalf based upon information provided by Expert or other publicly available sources, provided that if Company has informed Expert that payment will be made for such User Content before publication, Company will contact Expert and provide payment for such User Content.

(b) Collective Content Ownership & Use. As between the parties, you retain all ownership rights in any Collective Content (as defined below). You hereby grant Company a non-exclusive, worldwide, transferable, fully paid, royalty-free, perpetual, irrevocable right and license (with the right to sublicense through multiple tiers), through all media now known or hereafter created, to collect, access, use, reproduce, modify, adapt, publish, translate, distribute, publicly or otherwise perform and display, exploit, transmit, display, index, process, store, and create derivative works from the User Content, Feedback (as defined below), and any other data, content, or information you submit (“ Collective Content ”) for: (a) any purpose in connection with the Platform Services; (b) as authorized or instructed by you; (c) to respond to claims that any of your Collective Content violates the rights of third parties; and (d) as required by Law. Company shall own aggregated metrics and statistical data generated from the provision, operation or use of the Platform Services provided that Company will only disclose such data externally in an aggregated and/or anonymous form that shall not directly or indirectly identify you. To assist us in meeting business operations needs and to perform certain services and functions, we may share your Collective Content with vendors and service providers in various jurisdictions, including providers of hosting services, cloud services, and other information technology services providers, event management services, email communication software and email newsletter services, and web analytics services. Pursuant to our instructions, these parties will access, process, or temporarily store Collective Content in the course of performing their duties to us.

(c) Prohibited Collective Content. You are solely responsible for your Collective Content and you agree not to upload any Collective Content to the Website which violates any part of these Terms or Laws. You are solely responsible for your Collective Content and for ensuring it does not violate any part of these Terms or Laws. Company retains the right to investigate and take appropriate legal action against any User who fails to do so. It is the sole discretion of Company to determine what is objectionable and what constitutes a reason to restrict or terminate your use of the Platform Services. Should you have multiple end users authorized to access and receive the Platform Services through the Website, the Client will be responsible for any such end user who violates these Terms. Without limiting the foregoing, you will not upload, post, publish, submit, create, store, share, or display anything which:

-
Is “protected health information” under HIPAA

-
Infringes any proprietary, contractual, or privacy rights of any party;

-
Is deemed to be material nonpublic information that if disclosed would violate securities laws;

-
Contains software viruses, files, programs or any other computer code designed to interrupt, destroy or limit the functionality of any computer software or hardware or telecommunication equipment;

-
Constitutes unsolicited or unauthorized advertising, promotional materials, commercial activities (e.g. junk mail, spam, chain letters, pyramid schemes, contests, sweepstakes or other solicitations);

-
Is in violation of any Laws; or

-
Is inappropriate, vulgar, harassing, obscene, pornographic, threatening or is in any other way objectionable.

(d) Feedback. You may voluntarily post, submit comments or ideas, or otherwise communicate to Company any questions, comments, suggestions, ideas, original or creative materials, or other information about Company or the Platform Services, including about how to improve the Platform Services (collectively, “ Feedback ”). By submitting any Feedback, you agree that: (a) your disclosure is voluntary, free, unsolicited, and without restriction; (b) your Feedback does not contain the confidential or proprietary information of third parties; and (c) we are free to use the Feedback without any compensation to you and to disclose the Feedback on a non-confidential basis or otherwise to anyone, including to develop, copy, publish, or improve the Feedback or Platform Services, or to improve or develop new products, services, or the Platform Services in Company’s sole discretion. Company will exclusively own all improvements to, or new, Company products, services, or Platform Services based on any Feedback. You understand that Company may treat Feedback as nonconfidential.

Enforcement of this Section 7 is solely at our discretion, and failure to enforce this Section 7 in some instances does not constitute a waiver of our right to enforce it in other instances.

8. Repeat Infringer Policy; Copyright Complaints

(a) Our Policy. In accordance with the Digital Millennium Copyright Act (“ DMCA ”) and other Law, we have adopted a policy of terminating, in appropriate circumstances, the accounts of users who repeatedly infringe the intellectual property rights of others (our “ DMCA Policy ”).

(b) Reporting Claims of Copyright Infringement. If you believe that any content on the Platform Services infringe any copyright that you own or control, you may notify Company’s designated agent (your notification, a “ DMCA Notice ”) as follows:

Designated Agent: Enquire AI DMCA Agent

Address: 1233 20th Street NW, Suite 470

Washington, DC 20036

Telephone Number: (202) 495-0795

Email Address: support@enquire.ai

(c) Please see Section 512(c)(3) of the DMCA for the requirements of a proper notification. If you fail to comply with all of the requirements of Section 512(c)(3) of the DMCA, your notice may not be effective. If you knowingly materially misrepresent that any activity or material on Platform Services is infringing, you may be liable to Company for certain costs and damages.

9. Third-Party Content

(a) The Platform Services rely on or interoperate with third-party products and services, including, data storage services, communications technologies, IoT platforms, third-party app stores, and internet and mobile operators (collectively, “ Third-Party Materials ”). These Third-Party Materials are beyond our control, but their operation may impact, or be impacted by, the use and reliability of the Platform Services. You acknowledge that (a) the use and availability of the Platform Services is dependent on third-party product vendors and service providers and (b) these Third-Party Materials may not operate reliably 100% of the time, which may impact the way that the Platform Services operate.

(b) We may further provide information about or links to other third-party products, services, activities, or events, or we may allow third parties, to make their content, advice, and information available on or through the Platform Services (collectively, “ Third-Party Content ”). We provide Third-Party Content as a service to those interested in such content. Your dealings or correspondence with third parties and your use of or interaction with any Third-Party Content are solely between you and the third party.

(c) We have no obligation to monitor Third-Party Materials or Third-Party Content, and we may block or disable access to any Third-Party Materials or Third-Party Content (in whole or part) through the Platform Services at any time. Your access to and use of such Third-Party Content or Third-Party Materials may be subject to additional terms, conditions, and policies applicable to such Third-Party Content (including terms of service or privacy policies of the providers of such Third-Party Materials). You are responsible for obtaining and maintaining any computer hardware, equipment, network services and connectivity, telecommunications services, and other products and services necessary to access and use the Platform Services.

(d) You further acknowledge that no relationship (such as partnership, agent, joint venturer, or employee) is created between you and Company or between any user and Company by these Terms or by your participation on the Platform Services.

IT IS YOUR RESPONSIBILITY TO EVALUATE THE ACCURACY, COMPLETENESS AND USEFULNESS OF ANY OPINION, ANSWER OR OTHER CONTENT AVAILABLE THROUGH THE WEBSITE, FROM THIRD PARTIES OR OBTAINED FROM A LINKED SITE. PLEASE SEEK THE ADVICE OF PROFESSIONALS, AS APPROPRIATE, REGARDING THE EVALUATION OF ANY SPECIFIC OPINION, ANSWER, PRODUCT, SERVICE, OR OTHER CONTENT.

10. Fees and Payment

(a) Fees. Creating an account on the Website is free and there is no charge to post Projects. Instead, Company collects a percentage of all fees paid or payable for each Project completed and any Expert Services performed by Experts for Client (“ Service Fees ”) in addition to the fees due to an Expert by Client (“ Expert Fees ”, and collectively with Service Fees, the “ Fees ”). Company will function as the payment processor for Expert Fees through the Platform Services. Any additional information on Fees shall be posted on Company’s “Fees Page” (available at https://www.enquire.ai/app/fees or any successor link) and any additional questions on Fee collection should be directed to support@enquire.ai with a copy sent to accounting@enquire.ai .

(b) Payments. Except as otherwise set forth in a Project agreement, all payments under these Terms are due net fifteen (15) days from the invoice date sent to you by Company and shall be in United States dollars. Any payment not received from you by the due date for Fees may accrue, at Company’s discretion, late charges at the rate of 1.5% of the outstanding balance per month, or the maximum rate permitted by law, whichever is lower, from the date such payment was due until the date paid.

(c) Taxes. You agree that you shall be responsible for all applicable sales, use, value added, or similar taxes, if any, payable with respect to the Expert Services provided under these Terms or arising out of or in connection with these Terms whether at the time of invoicing or later determined by a taxing jurisdiction, provided that Company shall be responsible for all taxes imposed on Company’s net income or gross receipts, for any personal property taxes on property it owns or leases. You agree that Company may charge you any applicable taxes at the time of your payment of fees.

(d) Disputes. Should any dispute arise between Clients and Experts over the quality or completeness of work on a Project, Company may (but is not obligated to) hold any fees paid by Client while the dispute is resolved. Company reserves sole discretion to make determinations in such disputes and has the right to cancel and not return payment while the dispute is resolved pursuant to Section 11 below.

11. Disputes Between Users

Company encourages Clients and Experts to try and settle disputes amongst themselves, however, if this is not possible Company may (but is not obligated to) resolve disputes by obtaining information about the dispute between the Client and an Expert and making a resolution decision. If Company elects in its sole discretion to resolve a dispute, such resolution will be final and binding and, as further described in Section 26, Company will have no responsibility or liability for the consequences of such resolution.

12. Services Agreement

(a) In the event a Client and Expert wish to enter into a services agreement for Experts’ provision of certain professional services to Client (a “ Services Agreement ”), the parties acknowledge such Services Agreement is a contractual relationship directly between the Client and Expert. Client and Expert have complete discretion both with regard to whether to enter into a Services Agreement with each other and with regard to the terms of any Services Agreement. You acknowledge, agree, and understand that Company is not a party to any Services Agreement, that the formation of a Services Agreement between Users will not, under any circumstance, create an employment or other service relationship between Company and any User or a partnership or joint venture between Company and any User.

(b) With respect to any Services Agreement, Clients and Experts may enter into any written agreements that they deem appropriate (e.g., confidentiality agreements, ownership of deliverables provided to Client by Expert, invention assignment agreements, assignment of rights, etc.) provided that any such agreements do not conflict with, narrow, or expand Company’s rights and obligations under these Terms, including Sections 17and 18below. Clients will post any additional agreements (including any applicable Services Agreement) required to be executed by an Expert for a Project as an attachment to any posted Project requests. Once Client has selected an Expert(s) for a Project, Client will send any agreements required to be executed by Experts to compliance@enquire.ai .

13. Audit Rights

Clients will: (a) create and maintain records to document satisfaction of each party’s respective obligations under these Terms and any additional agreements between the parties (including any Services Agreement) (e.g. payment obligations and compliance with tax laws); and (b) provide copies of such records to Company upon request. Company maintains the right to audit each party’s operations and records to confirm compliance. However, this provision should not be construed as implying Company has the obligation to supervise an Expert’s or Client’s compliance with their internal policies, or monitor any services performed by Experts or delivered to Clients.

14. Company Role

(a) Acknowledgement. You expressly acknowledge and agree that: (a) the Website is merely a venue where Users may act as Experts or Clients; (b) Experts and Clients are solely responsible for negotiating, agreeing to, and executing any terms or conditions of Services Agreements; (c) Company does not, in any way, supervise, direct, or control the Expert Services delivered to Clients; (d) Company will not have any liability or obligations for any acts or omissions by you or any other Users related to the Platform Services; (e) your use of the Platform Services is subject to your acceptance of these Terms (which includes ensuring compliance with these Terms); and (f) Company makes no representations as to the reliability, capability, or qualifications of any Experts or the quality, security, or legality of any Expert or Expert Services, and Company disclaims any and all liability relating thereto.

(b) You acknowledge, agree, and understand that Company does not, in any way, supervise, direct, control, or evaluate Experts or their work and is not responsible for any Project, Project terms, or work product. Company makes no representations about and does not guarantee, and you agree not to hold Company responsible for, the quality, safety, or legality of the Expert Services; the qualifications, background, or identities of Users; the ability of Experts to deliver the Expert Services; the ability of Clients to pay for the Expert Services; user content and statements or posts made by users; or the ability or willingness of a Client or Expert to actually complete a transaction.

(c) You further acknowledge, agree, and understand that you are solely responsible for assessing whether to enter into a Services Agreement with another User and for verifying any information about another User. Company does not make any representations about or guarantee the truth or accuracy of any Expert’s or Client’s listings or other user content on the Website; does not verify any feedback or information provided by Users about Experts or Clients; and while Company screens Experts using a third-party vendor, Company does not perform background checks on or guarantee the work of Experts or Clients.

(d) Independent Contractor Relationship. Client and Expert each acknowledge and agree that their relationship is that of independent contractors. The Expert shall perform services as an independent contractor and nothing in these Terms shall be deemed to create a partnership, joint venture, agency, or employer-employee relationship between Expert and Client or between Company and any Client or Expert.

15. Confidentiality

(a) Confidential Information. As used in these Terms, “ Confidential Information ” means all confidential or proprietary information of a party (“ Disclosing Party ”) disclosed to the other party (“ Receiving Party ”), whether orally or in writing, that is either marked or designated as confidential or that a reasonable person would deem confidential or proprietary given the nature of the information and the circumstances under which it is disclosed. Confidential Information will include: (i) pricing and other terms and conditions reflected in Service Agreements or any other agreements between Users; (ii) User Content or work product; (iii) the Disclosing Party’s business and marketing plans, technology and technical information, product designs, or business processes; (iv) opinions expressed or work commissioned; (v) information about any actual or potential business, investment or trading decisions, or transactions of any Client; and (vi) any other nonpublic or proprietary information of Company. Confidential Information does not include any information that a Receiving Party can show: (A) is or becomes generally known to the public without breach of any obligation owed to the Disclosing Party; (B) was known to the Receiving Party prior to its disclosure by the Disclosing Party; (C) was independently developed by the Receiving Party without breach of any obligation owed to the Disclosing Party; or (D) is received from a third party without any duty or confidentiality obligation owed to the Disclosing Party.

(b) Confidentiality Obligations. To the extent Confidential Information is shared, the Receiving Party will protect the secrecy of Disclosing Party’s Confidential Information with the same degree of care as it uses to protect its own Confidential Information, but in no event with less than due care, and will: (i) not disclose or permit others to disclose Disclosing Party’s Confidential Information to anyone without first obtaining the express written consent of Disclosing Party; (ii) not use or permit the use of Disclosing Party’s Confidential Information, except as necessary for the performance of its obligations under these Terms (including, the storage or transmission of Confidential Information on or through the Website for use by an Expert); and (iii) limit access to Disclosing Party’s Confidential Information to Receiving Party’s personnel who need to know such information for the performance of the Receiving Party’s obligations under these Terms. A disclosure of information will be immune from prosecution or civil action under the Defend Trade Secrets Act, 18 U.S.C. section 1832, if it: (A) is made (1) in confidence to a Federal, State, or local government official, either directly or indirectly, or to an attorney; and (2) solely for the purpose of reporting or investigating a suspected violation of law; or (B) is made in a complaint or other document filed in a lawsuit or other proceeding, if such filing is made under seal. Additionally, Clients and Experts agree not to disclose or to attempt to use or personally benefit (financially or otherwise) from any Confidential Information that is disclosed or learned because of their activities through the Platform Services. The confidentiality obligations hereunder will remain in effect until the Confidential Information enters the public domain through no action of the Receiving Party or any other person under legal obligation not to disclose such information.

(c) Material Non-Public Information. Clients and Experts will not disclose or otherwise share with any person, including other Clients or Experts, material non-public information which, if used by such person, might constitute illegal insider trading. “ Material non-public information ” is defined as any information that there is a substantial likelihood that a reasonable investor would consider important in making his or her investment decisions, or information that is reasonably likely to have a substantial effect on the price of a company’s securities, and which has not been effectively communicated to the marketplace.

16. Termination

Subject to your obligation to pay for Services which you have received, you may cancel your account on the Website at any time by providing notice via email to support@enquire.ai and sending a copy to accounting@enquire.ai . For avoidance of doubt, cancelling your account shall not relieve you of any obligations under these Terms intended to survive termination, including, Sections 15(“Confidentiality”), 17(“Non-Circumvention”), 18(“Headhunter/Retainer Fees”), 20(“Warranty Disclaimer”), 21(“Indemnification”), 22(“Limitation of Liability”), 24(“Compliance with Law”).

17. Non-Circumvention

(a) You acknowledge and agree that a substantial portion of the compensation the Company receives for making the Website available to you is collected through the Fees described in Section 10 and that in exchange a substantial value to you is the relationships you make with other Users when you identify or are identified by another person through the Website or Platform Services (“ Company Relationship ”). The Company only receives a Service Fee when a Client and an Expert pay and receive payment through the Platform Services. Therefore, for 12 months from the later of: (a) the start of a Company Relationship; or (b) the date you complete your last project for a Client (the “ Non-Circumvention Period ”), you agree to use the Company and its Platform Services as your exclusive method to request, make, and receive all payments for work directly or indirectly with that person or arising out of your relationship with that person and not to circumvent the payment methods offered through the Platform Services unless you pay a Headhunter Fee or a Retainer Commission as set forth below.

(b) Examples of circumvention include: (i) accepting proposals from, receiving Expert Services from, or making payments to any Experts first identified through the Platform Services outside of Company; or (ii) paying or reporting on the Platform Services a payment amount lower than that actually agreed between you and an Expert through the Platform Services. You are not authorized to expand the scope of any Services or Project through the Platform Services unless Company has agreed in advance and in writing.

(c) You agree to not solicit or accept any offer which violates these Terms unless we provide express written permission, as outlined below, which we may withhold at our discretion. If you become aware of any breach or attempted breach of this Section 17, you must immediately inform Company at support@enquire.ai . Termination of your relationship with Company will not affect your obligations under this Section 17.

18. Headhunter/Retainer Fees

(a) Subject to Company’s prior express written permission, you may offer employment to an Expert introduced to you through the Platform Services during the Non-Circumvention Period. If this occurs within the Non-Circumvention Period, you agree to pay Company 25% of the hired Expert’s yearly Base Salary (as defined below) (“ Headhunter Fee ”). “ Base Salary ” is defined as the annual gross income of the hired Expert and does not include any additional bonuses or compensation, such as signing or discretionary bonuses, moving expenses, allowances or any other type of compensation. You agree to pay the Headhunter Fee to Company within thirty (30) days of the hired Expert’s start date of employment. If you extend an employment offer to an Expert after the Non-Circumvention Period, you will not be responsible for the payment of a Headhunter Fee.

(b) Subject to Company’s prior express written permission, you may procure services on a retainer basis from an Expert you were introduced to through the Platform Services during the Non-Circumvention Period. If this occurs within the Non-Circumvention Period, you agree to pay Company 30% of the monthly gross fees for services paid to the Expert, not including any reimbursable expenses (“ Retainer Commission ”). You will pay the Retainer Commission to Company within thirty (30) days of the end of each month in which you have procured services from the Expert for the lesser of one year or the duration of the engagement of the Expert on a retainer basis. If you procure services from Expert on a retainer basis after the end of the Non-Circumvention Period, you will not be responsible for the payment of any Retainer Commission.

(c) You understand and agree that if Company determines, in its sole discretion, that you have violated this Section, Company may, to the maximum extent permitted by law (a) close your account and revoke your authorization to use the Website and Platform Services, and/or (b) charge you for all losses and costs (including any and all time of Company’s employees) and reasonable expenses (including attorneys’ fees) related to investigating such breach and collecting such fees.

19. Publicity

By agreeing to these Terms, you agree that Company may publicly reference and publish your business name and logo in customer lists, on the Website, in investor pitches and sales presentations and in promotional material, or otherwise for marketing and promotional purposes unless otherwise agreed to by the parties.

20. Warranty Disclaimer

(a) You agree not to rely on the Platform Services or the continuation of the Website, or any information or Services provided by other users. The Platform Services are provided “as is” and on an “as available” basis. We make no express representations or warranties with regard to the Platform Services, content, user content, any work product provided to you, or any activities or items related to these Terms. To the maximum extent permitted by Law, we disclaim all express and implied conditions, representations, and warranties including, the warranties of merchantability, accuracy, fitness for a particular purpose, title, and non-infringement. Some jurisdictions may not allow for all of the foregoing limitations on warranties, so to that extent, some or all of the above limitations may not apply to you.

(b) Your access to and use of the Platform Services and any Services provided by other Users under these terms is for informational purposes only and is not intended to provide, offer, or render investment, tax, legal, or business advice. You agree that the Platform Services are only a tool you may access as part of your comprehensive compliance process, and you acknowledge that you will not rely on the Platform Services as the primary basis for your compliance decisions. Except as otherwise set forth in these terms, Company is not liable for your compliance decisions or any decisions or actions you take or authorize third parties to take on your behalf based on your access to, use of, or information obtained from the Platform Services.

(c) The content on the Platform Services is for informational purposes only and Company does not represent that the services described on the Platform Services are suitable for any specific company or available in any particular location.

21. Indemnification

(a) To the fullest extent permitted by Law, you agree to indemnify and hold harmless Company, its affiliates and subsidiaries, and their respective officers, directors, partners, employees, representatives, members, managing members and agents (each, an “ Indemnified Party ”) from and against any and all actions, causes of actions, claims, charges, demands, costs, expenses, damages, losses, or liabilities of any kind (including, attorney’s fees and related expenses) (“ Claims ”) resulting from, arising out of or in connection to: (a) your use of the Platform Services; (b) any Services provided to you; (c) your Collective Content; (d) your breach of any and all of these Terms; (e) any Services Agreement entered into by you or your agents, including any employment-related claims, such as those relating to employment termination, employment discrimination, harassment, or retaliation; and any claims for unpaid wages or other compensation, overtime pay, sick leave, holiday or vacation pay, retirement benefits, worker’s compensation benefits, unemployment benefits, or any other employee benefits; (f) failure to comply with Law by you or your agents; and (g) defamation, libel, violation of privacy rights, unfair competition, or infringement of intellectual property rights or allegations thereof to the extent caused by you or your agents.

(b) You will promptly notify Company and the Indemnified Parties of any third-party Claims, cooperate with Company and the Indemnified Parties in defending such Claims, and pay all fees, costs, and expenses associated with defending such Claims (including attorneys’ fees). The Company and the Indemnified Parties will have control of the defense or settlement, at Company’s sole option, of any third-party Claims. This indemnity is in addition to, and not in lieu of, any other indemnities set forth in a written agreement between you and Company or the other Indemnified Parties.

22. Limitation of Liability

YOU AGREE THAT UNDER NO CIRCUMSTANCES WILL COMPANY, ITS SUCCESSORS OR AFFILIATES, LICENSORS OR THIRD-PARTY VENDORS BE LIABLE TO YOU OR ANY OTHER PARTY FOR ANY SPECIAL, CONSEQUENTIAL, INCIDENTAL, INDIRECT, EXEMPLARY, OR PUNITIVE COSTS OR DAMAGES, LITIGATION COSTS OR PRODUCTION OF PROFIT ARISING FROM OR IN CONNECTION TO THE SERVICES AND/OR YOUR CONDUCT THROUGH THE PLATFORM SERVICES, OR ANY OF YOUR INTERACTIONS WITH OTHER USERS OR COMPANY PARTIES. IN NO EVENT WILL THE TOTAL AGGREGATE LIABILITY OF COMPANY, ITS SUCCESSORS OR AFFILIATES, OR ITS LICENSORS OR THIRD-PARTY VENDORS TO YOU FOR ANY CLAIMS ARISING FROM OR IN RELATION TO THE SERVICES OR ANY CONDUCT IN CONNECTION TO THE PLATFORM SERVICES OR THESE TERMS EXCEED THE LESSER OF: (A) $2,500; OR (B) ANY FEES RETAINED BY COMPANY WITH RESPECT TO SERVICES OR PROJECTS WITH WHICH YOU WERE INVOLVED DURING THE SIX-MONTH PERIOD PRECEDING THE DATE OF THE CLAIM. COMPANY WILL NOT BE LIABLE FOR ANY CLAIMS BY THIRD PARTIES BROUGHT AGAINST YOU. THESE LIMITATIONS WILL APPLY TO ANY LIABILITY, ARISING FROM ANY CAUSE OF ACTION WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THESE TERMS, WHETHER IN CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY, OR OTHERWISE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH COSTS OR DAMAGES AND EVEN IF THE LIMITED REMEDIES PROVIDED HEREIN FAIL OF THEIR ESSENTIAL PURPOSE. SOME STATES AND JURISDICTIONS DO NOT ALLOW FOR ALL OF THE FOREGOING EXCLUSIONS AND LIMITATIONS, SO TO THAT EXTENT, SOME OR ALL OF THESE LIMITATIONS AND EXCLUSIONS MAY NOT APPLY TO YOU.

23. Liability Release

To the maximum extent permitted by law, you release company, its officers, directors, employees, agents, and affiliates from any and all claims, demands, and damages of every kind and nature, known and unknown, suspected and unsuspected, disclosed and undisclosed, that are now in existence or may arise in the future and in any way relate to a dispute with one or more users, including, a dispute arising out of the use of the Platform Services, fees paid to an Expert, or compliance with these Terms. Where applicable and to the maximum extent permitted by law, you hereby waive your rights under California Civil Code §1542 (and any analogous law in any other applicable jurisdiction), which provides: “A general release does not extend to claims that the creditor or releasing party does not know or suspect to exist in his or her favor at the time of executing the release and that, if known by him or her, would have materially affected his or her settlement with the debtor or released party.”

24. Compliance With Law

You will not violate any applicable foreign, federal, state, or local laws or third-party rights on or related to the Platform Services. Without limiting the generality of the foregoing, you agree to comply with all Laws and regulations, including, import and export control laws and third parties’ intellectual property rights.

25. Governing Law

These Terms and any controversy or claims arising from them, shall be governed by the laws of the State of New York, without regard to the conflicts of law provisions.

26. Arbitration

(a) No Representative Actions. You and Company agree that any dispute arising out of or related to these Terms or the Platform Services is personal to you and Company and that any dispute will be resolved solely through individual action, and will not be brought as a class arbitration, class action, or any other type of representative proceeding.

(b) Arbitration of Disputes. Except for small claims disputes in which you or Company seeks to bring an individual action in small claims court located in the county of your address or disputes in which you or Company seeks injunctive or other equitable relief for the alleged infringement or misappropriation of intellectual property, you and Company waive your rights to a jury trial and to have any other dispute arising out of or related to these Terms or the Platform Services, (collectively, “ Disputes ”) resolved in court. Instead, for any Dispute that you have against Company, you agree to first contact Company and attempt to resolve the claim informally by sending a written notice of the claim (“ Notice ”) to Company by certified mail addressed to Enquire AI, Inc., 1233 20th Street NW, Suite 470, Washington DC 20036. The Notice must: (i) include your name, residence address, email address, and telephone number; (ii) describe the nature and basis of the Dispute; and (iii) set forth the specific relief sought. Company’s notice to you will be similar in form to that described above. If you or Company cannot reach an agreement to resolve the Dispute within thirty (30) days after such Notice is received, then either party may submit the Dispute to binding arbitration administered by JAMS or, under the limited circumstances set forth above, in court. All Disputes submitted to JAMS will be resolved through confidential, binding arbitration before one arbitrator. Arbitration proceedings will be held in New York, New York. You and Company agree that Disputes will be held in accordance with the JAMS Streamlined Arbitration Rules and Procedures (“ JAMS Rules ”). The most recent version of the JAMS Rules is available on the JAMS website and is hereby incorporated by reference. You either acknowledge and agree that you have read and understand the JAMS Rules or waive your opportunity to read the JAMS Rules and waive any claim that the JAMS Rules are unfair or should not apply for any reason.

(c) You and Company agree that these Terms affect interstate commerce and that the enforceability of this Section 26will be substantively and procedurally governed by the Federal Arbitration Act, 9 U.S.C. § 1, et seq. (the “ FAA ”), to the maximum extent permitted by Law. As limited by the FAA, these Terms, and the JAMS Rules, the arbitrator will have exclusive authority to make all procedural and substantive decisions regarding any Dispute and to grant any remedy that would otherwise be available in court, including the power to determine the question of arbitrability. The arbitrator may conduct only an individual arbitration and may not consolidate more than one individual’s claims, preside over any type of class or representative proceeding or preside over any proceeding involving more than one individual.

(d) The arbitration will allow for the discovery or exchange of non-privileged information relevant to the Dispute. The arbitrator, Company, and you will maintain the confidentiality of any arbitration proceedings, judgments, and awards, including information gathered, prepared, and presented for purposes of the arbitration or related to the Dispute(s) therein. The arbitrator will have the authority to make appropriate rulings to safeguard confidentiality unless the law provides to the contrary. The duty of confidentiality does not apply to the extent that disclosure is necessary to prepare for or conduct the arbitration hearing on the merits, in connection with a court application for a preliminary remedy or in connection with a judicial challenge to an arbitration award or its enforcement, or to the extent that disclosure is otherwise required by law or judicial decision.

(e) You and Company agree that the state or federal courts of the State of New York and the United States have exclusive jurisdiction over any appeals and the enforcement of an arbitration award.

(f) Any Dispute must be filed within one year after the relevant claim arose; otherwise, the Dispute is permanently barred, which means that you and Company will not have the right to assert the claim.

(g) You have the right to opt-out of binding arbitration within 30 days of the date you first executed these Terms by sending written notice via certified mail addressed to Enquire AI, Inc., 1233 20th Street NW, Suite 470, Washington DC 20036. In order to be effective, the opt-out notice must include your legal name and address, reference these Terms, and clearly indicate your intent to opt-out of binding arbitration. By opting out of binding arbitration, you are agreeing to resolve Disputes in accordance with Section 25.

(h) If any portion of this Section 26 is found to be unenforceable or unlawful for any reason, (i) the unenforceable or unlawful provision shall be severed from these Terms; (ii) severance of the unenforceable or unlawful provision shall have no impact whatsoever on the remainder of this Section 26 or the parties’ ability to compel arbitration of any remaining claims on an individual basis pursuant to this Section 26; and (iii) to the extent that any claims must therefore proceed on a class, collective, consolidated, or representative basis, such claims must be litigated in a civil court of competent jurisdiction and not in arbitration, and the parties agree that litigation of those claims shall be stayed pending the outcome of any individual claims in arbitration. Further, if any part of this Section 26 is found to prohibit an individual claim seeking public injunctive relief, that provision will have no effect to the extent such relief is allowed to be sought out of arbitration, and the remainder of this Section 26 will be enforceable.

27. Trademarks

Enquire and our logos, product or service names, slogans, and the look and feel of the Platform Services are trademarks of Company and may not be copied, imitated, or used, in whole or in part, without our prior written permission. All other trademarks, registered trademarks, product names, and company names or logos mentioned on or in connection with the Platform Services are the property of their respective owners. Reference to any products, services, processes, or other information by trade name, trademark, manufacturer, supplier, or otherwise does not constitute or imply endorsement, sponsorship, or recommendation by us.

28. Modifying and Terminating the Platform Services.

We reserve the right to modify the Platform Services or to suspend or terminate providing all or part of the Platform Services at any time; charge, modify, or waive any fees required to use the Platform Services; or offer opportunities to some or all end users of the Platform Services. We may provide you with notice in advance of the suspension or discontinuation of all or part of the Platform Services, such as by sending an email or providing a notice through the Platform Services. All modifications and additions to the Platform Services will be governed by the Terms or Supplemental Terms, unless otherwise expressly stated by Company in writing. You also have the right to stop using the Platform Services at any time, and you may terminate these Terms by ceasing use of the Platform Services. We are not responsible for any loss or harm related to your inability to access or use the Platform Services.

29. Export Control

You are responsible for compliance with United States export controls and for any violation of such controls, including any United States embargoes or other federal rules and regulations restricting exports. You represent, warrant and covenant that you are not: (a) located in, or a resident or a national of, any country subject to a U.S. government embargo or other restriction, or that has been designated by the U.S. government as a “terrorist supporting” country; or (b) on any of the U.S. government lists of restricted end users.

30. Miscellaneous

(a) Assignment. You may not assign these Terms, or any of your rights or obligations hereunder, without our prior written consent. We may freely assign these Terms without your consent. Any attempted assignment or transfer in violation of this section will be null and void. Subject to the foregoing restrictions, these Terms will inure to the benefit of the successors and permitted assigns of the parties.

(b) Entire Agreement. These Terms, together with any policies incorporated herein, sets forth the entire agreement and understanding between you and Company relating to the subject matter herein and cancels and supersedes any prior or contemporaneous discussions, agreements, representations, warranties, and other communications between you and us, written or oral, to the extent they relate in any way to the subject matter herein. You represent that you had ample time to review and decide whether to agree to these Terms. If an ambiguity or question of intent or interpretation of these Terms arises, no presumption or burden of proof will arise favoring or disfavoring you or Company because of our authorship of any provision of these Terms.

(c) Severability. If any provision of these Terms is held to be illegal, invalid or unenforceable in part or in whole under Law, that provision will be eliminated or limited to the minimum extent necessary to conform to Law so that the Terms will remain in effect and be enforceable.

(d) No Waiver. The failure or delay of either party to exercise or enforce in any respect any right or claim provided for herein does not constitute a waiver of any such right or claim and will not affect any further rights or affect that party’s right to later enforce or exercise it.

(e) Modifications. No modification or amendment to these Terms will be binding upon Company unless in a written instrument signed by a duly authorized representative of Company. For the purposes of this section, a written instrument will expressly exclude electronic communications, such as email and electronic notices, but will include facsimiles. This section “Modifications” does not apply to amendments to these Terms posted by Company to the Website from time to time.

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## Expert Terms — https://enquire.ai/terms-expert

Expert Terms

# Expert Terms of Service

### Terms of Service

Expert

Last Updated: [May 31, 2024]

Expert Terms of Service

These Terms of Service (these “ Terms ”) are a contract between you, in your individual capacity or the business entity you represent (“ you ” or “ Client ”), and Enquire AI Holdings, Inc. (“ Enquire Holding ”), a Delaware corporation and an affiliate of Enquire AI, Inc. a Delaware corporation (“ Enquire AI ”) (Enquire Holdings and Enquire AI shall, as context requires, be referred to as, the “ Company ”, “ we ”, or “ us ”). “ You ” and “ your ” as used herein will refer and apply to that entity and the persons that can access the Platform Services (as defined herein) on its behalf. Please read this entire agreement carefully. You must read, agree to, and accept these terms and conditions in full in order to use the website located at www.enquire.ai (or any successor sites or URLs) (the “ Website ”) and related software and services (collectively, the “ Platform Services ”). For more information about our collection of your information, please visit our privacy policy (or any successor link) (“ Privacy Policy” ) as may be in effect and modified by us from time to time.

BY CLICKING [“ I ACCEPT ”] OR BY USING THE PLATFORM SERVICES, YOU AGREE TO THESE TERMS. BY AGREEING TO THESE TERMS, EXCEPT FOR IN (A) CERTAIN TYPES OF DISPUTES DESCRIBED IN SECTION 26, (B) WHERE YOU EXERCISE YOUR RIGHT TO OPT OUT OF ARBITRATION AS DESCRIBED IN SECTION 26, OR (C) TO THE EXTENT PROHIBITED BY LAW, DISPUTES BETWEEN YOU AND COMPANY WILL BE RESOLVED SOLELY ON AN INDIVIDUAL BASIS AND NOT AS A CLASS ARBITRATION, CLASS ACTION, ANY OTHER KIND OF REPRESENTATIVE PROCEEDING, OR BY JURY TRIAL. IF YOU DO NOT AGREE TO THESE TERMS, DO NOT USE THE PLATFORM SERVICES.

Subject to the conditions set forth herein, Company may, in its sole discretion, amend these Terms by posting a revised version on the Website. The “Last Updated” date above indicates when these Terms were last changed. Company will provide reasonable advance notice of any amendment that includes a material change, by posting the updated Terms on the Website, providing notice on the Website, and/or sending you notice by email. Unless we say otherwise in Company’s notice, any changes to these Terms will take effect on the last updated date therein and your continued use of the Platform Services after we provide such notice will confirm your acceptance of the changes. If you do not agree to the amended Terms, you must immediately stop using the Platform Services. We may indicate that different or additional terms, conditions, guidelines, policies, or rules apply in relation to some of the Platform Services (“ Supplemental Terms ”). Any Supplemental Terms become part of your agreement with us if you use the applicable Platform Services, and if there is a conflict between these Terms and the Supplemental Terms, the Supplemental Terms will control for that conflict. Any changes to these Terms will not apply to any dispute between you and us arising prior to the date on which we posted the updated Terms incorporating such changes or otherwise notified you of such changes.

If you agree to these Terms on behalf of an entity, or in connection with providing or receiving services on behalf of an entity, you represent and warrant that you have the authority to bind that entity to these terms and agree that you are binding both you and that entity to these terms. In that event, “ you ” and “ your ” will refer and apply to you and that entity.

- Services Overview

Company provides a technology-enabled marketplace that economically and efficiently matches sophisticated global affairs professionals offering professional services (the “ Expert Services” ), with enterprising investors and decision-makers across a variety of sectors using the Platform Services to procure such Expert Services (“ Clients ”) for a variety of projects (“ Projects ”). Under these Terms, Company provides the Platform Services to you as a Client, which include curating Experts and Projects, and functioning as the payment processor for you and Experts. Experts and Clients must register for an account to use the Platform Services (each, a “ User ”). Unless otherwise set forth in a separate and mutually agreed writing, Company’s Services are provided to you only for your internal and non-commercial business use and not for the benefit or use of any third party.

- Eligibility

By creating an account on the Website and making use of the Platform Services, you represent and warrant that you: (a) are a legal entity or a person who is at least eighteen (18) years old (or have otherwise reached the age of majority in the jurisdiction in which you conduct business) and capable of forming legally binding contracts under Law (as defined below); (b) are authorized to represent and act on behalf of any entity for which you are employed by or solicit Projects for; (c) are not identified on the U.S. General Services Administration’s Excluded Parties List, the U.S. Department of Commerce’s Denied Persons or Entity List, the U.S. Department of the Treasury’s Specially Designated Nationals or Blocked Persons Lists, or the Department of State’s Debarred Parties List, or any similar list of individuals limited by U.S. export control laws and regulations or other economic sanctions of the US, other government or multilateral organization; (d) are not considered to be a “Politically Exposed Person”; (e) have not been convicted or charged, or pled guilty or nolo contendere to any felony or any misdemeanor involving securities fraud or investment-related business; and (f) are not and have not been the subject of any regulatory or enforcement proceeding brought by any securities, commodities or other financial regulatory authority, self-regulatory organizations or other law enforcement body, including the U.S. Securities and Exchange Commission and the Financial Industry Regulatory Authority.

- Account Registration

To sign up to use the Platform Services, you must create an account profile and provide accurate and complete information. This includes providing us with your legal or business name, address, billing/payment information, contact information, including the name and contact information of a representative authorized to act on your entity’s behalf, and any other relevant information to your use of the Platform Services as determined in Company’s sole discretion. It is your responsibility to keep your account information updated. Should your eligibility for a Company account as outlined above change during your time using the Platform Services, you must notify Company within a reasonable amount of time, but no later than thirty (30) days after such change by emailing support@enquire.ai . You shall not use the name or email of (a) another person with the intent to impersonate that person, or (b) an entity without appropriate authorization. We reserve the right, in our sole discretion, to refuse, suspend, or revoke your account via the Platform Services upon discovery that any account information provided is untrue, inaccurate, or incomplete, or such information or other conduct otherwise violates these Terms, or for any other reason or no reason in our sole discretion.

- Restrictions

(a) You agree that: (i) you will not use the Platform Services if you are not fully able and legally competent to agree to these Terms; (ii) you will only use the Platform Services in full compliance with all applicable statutes, laws, ordinances, regulations, rules, codes, orders, constitutions, treaties, common laws, judgments, decrees, other requirements or rules of law of any federal, state, local or foreign government or political subdivision or agency thereof (collectively, “ Law ” or “ Laws ”) and these Terms; and (iii) you will not use the Platform Services for fraudulent purposes or to engage in any offensive, indecent or objectionable conduct. Further, you will not, in connection with the Platform Services:

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Engage in any harassing, threatening, intimidating, predatory, or stalking conduct;

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Use or attempt to use another user’s account or information without authorization from that user and us;

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Copy, reproduce, distribute, publicly perform, or publicly display all or portions of the Platform Services, except as expressly permitted by us or our licensors in writing;

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Modify the Platform Services, remove any proprietary rights notices or markings, or otherwise make any derivative works based upon the Platform Services;

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Use the Platform Services in any manner that could interfere with, disrupt, negatively affect, or inhibit other users from fully enjoying the Platform Services or that could damage, disable, overburden, or impair the functioning of the Platform Services in any manner;

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Reverse engineer any aspect of the Platform Services or do anything that might discover or reveal source code, or bypass or circumvent measures employed to prevent or limit access to any part of the Platform Services;

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Use any data mining, robots, or similar data gathering or extraction methods designed to scrape or extract data from the Platform Services;

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Develop or use any applications or software that interact with the Platform Services without our prior written consent;

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Send, distribute, or post spam, unsolicited or bulk commercial electronic communications, chain letters, or pyramid schemes; or

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Use the Platform Services for any illegal or unauthorized purpose, or engage in, encourage, or promote any activity that violates these Terms.

(b) You further agree that you may not, and may not cause or permit others to: (a) modify, make derivative works of, disassemble, decompile, reverse engineer, reproduce, republish, download, or copy any part of the Platform Services (including data structures or similar materials); (b) access or use the Platform Services to build or support, directly or indirectly, products or services competitive to Company; or (c) license, sell, transfer, assign, distribute, outsource, permit timesharing or service bureau use of, commercially exploit, or make available the Platform Services to any third party except as permitted by these Terms.

(c) Enforcement of this Section 4 is solely at our discretion, and failure to enforce this Section 4 in some instances does not constitute a waiver of our right to enforce it in other instances.

- Marketplace Feedback

(a) You hereby acknowledge and agree that Users publish and request Company to publish on their behalf information through the Platform Services about the User, such as feedback, geographical location, or verification of identity or credentials. However, such information is based solely on unverified data that Experts or Clients voluntarily submit to Company and does not constitute and will not be construed as an introduction, endorsement, or recommendation by Company. Company provides such information solely for the convenience of Users.

(b) You acknowledge and agree that User feedback benefits the marketplace, all Users, and the efficiency of the Platform Services and you specifically request that Company post feedback about Users, including yourself, on User profiles and elsewhere on the Platform Services.

- Ownership; Limited License

The Services and Platform Services, including, the text, graphics, images, photographs, videos, illustrations, and other content contained therein, and all intellectual property rights therein and thereto and anything developed or delivered by or on behalf of Company under these Terms (including Deliverables as such term is defined in the Expert Terms of Service), are owned by Company or our licensors and are protected under both United States and foreign laws. Except as explicitly stated in these Terms, all rights in and to the Platform Services and Services, including all intellectual property rights therein and thereto, are reserved by us or our licensors. Any use of the Platform Services other than as specifically authorized herein, without our prior written permission, is strictly prohibited and will terminate the license granted herein and violate our intellectual property rights.

Our Network Pulse™ tool allows Users to submit questions, which Experts may review and respond to. Subject to these Terms, Client is hereby granted the non-exclusive, worldwide, limited right to use and access the Network Pulse™ responses (together with all intellectual property rights therein and thereto) ordered by such Client during a ninety (90) day period or as otherwise set forth in a mutual writing between Client and Company and solely for the internal business operations of Client. Client may allow its employees to access the Network Pulse™ responses for this purpose, and Client is responsible for their compliance with these Terms.

- User Content & Collective Content

(a) User Content. You represent and warrant that you have the right, power and authority or have valid licenses to all data, feedback, content, or other materials you upload, post, publish, submit, create, store, share, or display through the Platform Services (collectively, the “ User Content ”) and to grant the licenses specified below. When you post or otherwise share User Content on or through the Platform Services, you understand that your User Content and any associated information may be visible to others and is non-confidential and we have no confidentiality obligation of any kind with respect to such information. Company may collect, store, and post User Content (including Expert responses and responses to Projects) within the Platform Services on an Expert’s behalf based upon information provided by Expert or other publicly available sources, provided that if Company has informed Expert that payment will be made for such User Content before publication, Company will contact Expert and provide payment for such User Content.

(b) Collective Content Ownership & Use. As between the parties, you retain all ownership rights in any Collective Content (as defined below). You hereby grant Company a non-exclusive, worldwide, transferable, fully paid, royalty-free, perpetual, irrevocable right and license (with the right to sublicense through multiple tiers), through all media now known or hereafter created, to collect, access, use, reproduce, modify, adapt, publish, translate, distribute, publicly or otherwise perform and display, exploit, transmit, display, index, process, store, and create derivative works from the User Content, Feedback (as defined below), and any other data, content, or information you submit (“ Collective Content ”) for: (a) any purpose in connection with the Platform Services; (b) as authorized or instructed by you; (c) to respond to claims that any of your Collective Content violates the rights of third parties; and (d) as required by Law. Company shall own aggregated metrics and statistical data generated from the provision, operation or use of the Platform Services provided that Company will only disclose such data externally in an aggregated and/or anonymous form that shall not directly or indirectly identify you. To assist us in meeting business operations needs and to perform certain services and functions, we may share your Collective Content with vendors and service providers in various jurisdictions, including providers of hosting services, cloud services, and other information technology services providers, event management services, email communication software and email newsletter services, and web analytics services. Pursuant to our instructions, these parties will access, process, or temporarily store Collective Content in the course of performing their duties to us.

(c) Prohibited Collective Content. You are solely responsible for your Collective Content and you agree not to upload any Collective Content to the Website which violates any part of these Terms or Laws. You are solely responsible for your Collective Content and for ensuring it does not violate any part of these Terms or Laws. Company retains the right to investigate and take appropriate legal action against any User who fails to do so. It is the sole discretion of Company to determine what is objectionable and what constitutes a reason to restrict or terminate your use of the Platform Services. Should you have multiple end users authorized to access and receive the Platform Services through the Website, the Client will be responsible for any such end user who violates these Terms. Without limiting the foregoing, you will not upload, post, publish, submit, create, store, share, or display anything which:

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Is “protected health information” under HIPAA

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Infringes any proprietary, contractual, or privacy rights of any party;

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Is deemed to be material nonpublic information that if disclosed would violate securities laws;

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Contains software viruses, files, programs or any other computer code designed to interrupt, destroy or limit the functionality of any computer software or hardware or telecommunication equipment;

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Constitutes unsolicited or unauthorized advertising, promotional materials, commercial activities (e.g. junk mail, spam, chain letters, pyramid schemes, contests, sweepstakes or other solicitations);

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Is in violation of any Laws; or

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Is inappropriate, vulgar, harassing, obscene, pornographic, threatening or is in any other way objectionable.

(d) Feedback. You may voluntarily post, submit comments or ideas, or otherwise communicate to Company any questions, comments, suggestions, ideas, original or creative materials, or other information about Company or the Platform Services, including about how to improve the Platform Services (collectively, “ Feedback ”). By submitting any Feedback, you agree that: (a) your disclosure is voluntary, free, unsolicited, and without restriction; (b) your Feedback does not contain the confidential or proprietary information of third parties; and (c) we are free to use the Feedback without any compensation to you and to disclose the Feedback on a non-confidential basis or otherwise to anyone, including to develop, copy, publish, or improve the Feedback or Platform Services, or to improve or develop new products, services, or the Platform Services in Company’s sole discretion. Company will exclusively own all improvements to, or new, Company products, services, or Platform Services based on any Feedback. You understand that Company may treat Feedback as nonconfidential.

Enforcement of this Section 7 is solely at our discretion, and failure to enforce this Section 7 in some instances does not constitute a waiver of our right to enforce it in other instances.

- Repeat Infringer Policy; Copyright Complaints

(a) Our Policy. In accordance with the Digital Millennium Copyright Act (“ DMCA ”) and other Law, we have adopted a policy of terminating, in appropriate circumstances, the accounts of users who repeatedly infringe the intellectual property rights of others (our “ DMCA Policy ”).

(b) Reporting Claims of Copyright Infringement. If you believe that any content on the Platform Services infringe any copyright that you own or control, you may notify Company’s designated agent (your notification, a “ DMCA Notice ”) as follows:

Designated Agent: Enquire AI DMCA Agent

Address: 1233 20th Street NW, Suite 470

Washington, DC 20036

Telephone Number: (202) 495-0795

Email Address: support@enquire.ai

(c) Please see Section 512(c)(3) of the DMCA for the requirements of a proper notification. If you fail to comply with all of the requirements of Section 512(c)(3) of the DMCA, your notice may not be effective. If you knowingly materially misrepresent that any activity or material on Platform Services is infringing, you may be liable to Company for certain costs and damages.

- Third-Party Content

(a) The Platform Services rely on or interoperate with third-party products and services, including, data storage services, communications technologies, IoT platforms, third-party app stores, and internet and mobile operators (collectively, “ Third-Party Materials ”). These Third-Party Materials are beyond our control, but their operation may impact, or be impacted by, the use and reliability of the Platform Services. You acknowledge that (a) the use and availability of the Platform Services is dependent on third-party product vendors and service providers and (b) these Third-Party Materials may not operate reliably 100% of the time, which may impact the way that the Platform Services operate.

(b) We may further provide information about or links to other third-party products, services, activities, or events, or we may allow third parties, to make their content, advice, and information available on or through the Platform Services (collectively, “ Third-Party Content ”). We provide Third-Party Content as a service to those interested in such content. Your dealings or correspondence with third parties and your use of or interaction with any Third-Party Content are solely between you and the third party.

(c) We have no obligation to monitor Third-Party Materials or Third-Party Content, and we may block or disable access to any Third-Party Materials or Third-Party Content (in whole or part) through the Platform Services at any time. Your access to and use of such Third-Party Content or Third-Party Materials may be subject to additional terms, conditions, and policies applicable to such Third-Party Content (including terms of service or privacy policies of the providers of such Third-Party Materials). You are responsible for obtaining and maintaining any computer hardware, equipment, network services and connectivity, telecommunications services, and other products and services necessary to access and use the Platform Services.

(d) You further acknowledge that no relationship (such as partnership, agent, joint venturer, or employee) is created between you and Company or between any user and Company by these Terms or by your participation on the Platform Services.

IT IS YOUR RESPONSIBILITY TO EVALUATE THE ACCURACY, COMPLETENESS AND USEFULNESS OF ANY OPINION, ANSWER OR OTHER CONTENT AVAILABLE THROUGH THE WEBSITE, FROM THIRD PARTIES OR OBTAINED FROM A LINKED SITE. PLEASE SEEK THE ADVICE OF PROFESSIONALS, AS APPROPRIATE, REGARDING THE EVALUATION OF ANY SPECIFIC OPINION, ANSWER, PRODUCT, SERVICE, OR OTHER CONTENT.

- Fees and Payment

(a) Fees. Creating an account on the Website is free and there is no charge to post Projects. Instead, Company collects a percentage of all fees paid or payable for each Project completed and any Expert Services performed by Experts for Client (“ Service Fees ”) in addition to the fees due to an Expert by Client (“ Expert Fees ”, and collectively with Service Fees, the “ Fees ”). Company will function as the payment processor for Expert Fees through the Platform Services. Any additional information on Fees shall be posted on Company’s “Fees Page” (available at or any successor link) and any additional questions on Fee collection should be directed to support@enquire.ai with a copy sent to .

(b) Payments. Except as otherwise set forth in a Project agreement, all payments under these Terms are due net fifteen (15) days from the invoice date sent to you by Company and shall be in United States dollars. Any payment not received from you by the due date for Fees may accrue, at Company’s discretion, late charges at the rate of 1.5% of the outstanding balance per month, or the maximum rate permitted by law, whichever is lower, from the date such payment was due until the date paid.

(c) Taxes. You agree that you shall be responsible for all applicable sales, use, value added, or similar taxes, if any, payable with respect to the Expert Services provided under these Terms or arising out of or in connection with these Terms whether at the time of invoicing or later determined by a taxing jurisdiction, provided that Company shall be responsible for all taxes imposed on Company’s net income or gross receipts, for any personal property taxes on property it owns or leases. You agree that Company may charge you any applicable taxes at the time of your payment of fees.

(d) Disputes. Should any dispute arise between Clients and Experts over the quality or completeness of work on a Project, Company may (but is not obligated to) hold any fees paid by Client while the dispute is resolved. Company reserves sole discretion to make determinations in such disputes and has the right to cancel and not return payment while the dispute is resolved pursuant to Section 11 below.

- Disputes Between Users

Company encourages Clients and Experts to try and settle disputes amongst themselves, however, if this is not possible Company may (but is not obligated to) resolve disputes by obtaining information about the dispute between the Client and an Expert and making a resolution decision. If Company elects in its sole discretion to resolve a dispute, such resolution will be final and binding and, as further described in Section 26, Company will have no responsibility or liability for the consequences of such resolution.

- Services Agreement

(a) In the event a Client and Expert wish to enter into a services agreement for Experts’ provision of certain professional services to Client (a “ Services Agreement ”), the parties acknowledge such Services Agreement is a contractual relationship directly between the Client and Expert. Client and Expert have complete discretion both with regard to whether to enter into a Services Agreement with each other and with regard to the terms of any Services Agreement. You acknowledge, agree, and understand that Company is not a party to any Services Agreement, that the formation of a Services Agreement between Users will not, under any circumstance, create an employment or other service relationship between Company and any User or a partnership or joint venture between Company and any User.

(b) With respect to any Services Agreement, Clients and Experts may enter into any written agreements that they deem appropriate (e.g., confidentiality agreements, ownership of deliverables provided to Client by Expert, invention assignment agreements, assignment of rights, etc.) provided that any such agreements do not conflict with, narrow, or expand Company’s rights and obligations under these Terms, including Sections 17and 18below. Clients will post any additional agreements (including any applicable Services Agreement) required to be executed by an Expert for a Project as an attachment to any posted Project requests. Once Client has selected an Expert(s) for a Project, Client will send any agreements required to be executed by Experts to.

- Audit Rights

Clients will: (a) create and maintain records to document satisfaction of each party’s respective obligations under these Terms and any additional agreements between the parties (including any Services Agreement) (e.g. payment obligations and compliance with tax laws); and (b) provide copies of such records to Company upon request. Company maintains the right to audit each party’s operations and records to confirm compliance. However, this provision should not be construed as implying Company has the obligation to supervise an Expert’s or Client’s compliance with their internal policies, or monitor any services performed by Experts or delivered to Clients.

- Company Role

(a) Acknowledgement. You expressly acknowledge and agree that: (a) the Website is merely a venue where Users may act as Experts or Clients; (b) Experts and Clients are solely responsible for negotiating, agreeing to, and executing any terms or conditions of Services Agreements; (c) Company does not, in any way, supervise, direct, or control the Expert Services delivered to Clients; (d) Company will not have any liability or obligations for any acts or omissions by you or any other Users related to the Platform Services; (e) your use of the Platform Services is subject to your acceptance of these Terms (which includes ensuring compliance with these Terms); and (f) Company makes no representations as to the reliability, capability, or qualifications of any Experts or the quality, security, or legality of any Expert or Expert Services, and Company disclaims any and all liability relating thereto.

(b) You acknowledge, agree, and understand that Company does not, in any way, supervise, direct, control, or evaluate Experts or their work and is not responsible for any Project, Project terms, or work product. Company makes no representations about and does not guarantee, and you agree not to hold Company responsible for, the quality, safety, or legality of the Expert Services; the qualifications, background, or identities of Users; the ability of Experts to deliver the Expert Services; the ability of Clients to pay for the Expert Services; user content and statements or posts made by users; or the ability or willingness of a Client or Expert to actually complete a transaction.

(c) You further acknowledge, agree, and understand that you are solely responsible for assessing whether to enter into a Services Agreement with another User and for verifying any information about another User. Company does not make any representations about or guarantee the truth or accuracy of any Expert’s or Client’s listings or other user content on the Website; does not verify any feedback or information provided by Users about Experts or Clients; and while Company screens Experts using a third-party vendor, Company does not perform background checks on or guarantee the work of Experts or Clients.

(d) Independent Contractor Relationship. Client and Expert each acknowledge and agree that their relationship is that of independent contractors. The Expert shall perform services as an independent contractor and nothing in these Terms shall be deemed to create a partnership, joint venture, agency, or employer-employee relationship between Expert and Client or between Company and any Client or Expert.

- Confidentiality

(a) Confidential Information. As used in these Terms, “ Confidential Information ” means all confidential or proprietary information of a party (“ Disclosing Party ”) disclosed to the other party (“ Receiving Party ”), whether orally or in writing, that is either marked or designated as confidential or that a reasonable person would deem confidential or proprietary given the nature of the information and the circumstances under which it is disclosed. Confidential Information will include: (i) pricing and other terms and conditions reflected in Service Agreements or any other agreements between Users; (ii) User Content or work product; (iii) the Disclosing Party’s business and marketing plans, technology and technical information, product designs, or business processes; (iv) opinions expressed or work commissioned; (v) information about any actual or potential business, investment or trading decisions, or transactions of any Client; and (vi) any other nonpublic or proprietary information of Company. Confidential Information does not include any information that a Receiving Party can show: (A) is or becomes generally known to the public without breach of any obligation owed to the Disclosing Party; (B) was known to the Receiving Party prior to its disclosure by the Disclosing Party; (C) was independently developed by the Receiving Party without breach of any obligation owed to the Disclosing Party; or (D) is received from a third party without any duty or confidentiality obligation owed to the Disclosing Party.

(b) Confidentiality Obligations. To the extent Confidential Information is shared, the Receiving Party will protect the secrecy of Disclosing Party’s Confidential Information with the same degree of care as it uses to protect its own Confidential Information, but in no event with less than due care, and will: (i) not disclose or permit others to disclose Disclosing Party’s Confidential Information to anyone without first obtaining the express written consent of Disclosing Party; (ii) not use or permit the use of Disclosing Party’s Confidential Information, except as necessary for the performance of its obligations under these Terms (including, the storage or transmission of Confidential Information on or through the Website for use by an Expert); and (iii) limit access to Disclosing Party’s Confidential Information to Receiving Party’s personnel who need to know such information for the performance of the Receiving Party’s obligations under these Terms. A disclosure of information will be immune from prosecution or civil action under the Defend Trade Secrets Act, 18 U.S.C. section 1832, if it: (A) is made (1) in confidence to a Federal, State, or local government official, either directly or indirectly, or to an attorney; and (2) solely for the purpose of reporting or investigating a suspected violation of law; or (B) is made in a complaint or other document filed in a lawsuit or other proceeding, if such filing is made under seal. Additionally, Clients and Experts agree not to disclose or to attempt to use or personally benefit (financially or otherwise) from any Confidential Information that is disclosed or learned because of their activities through the Platform Services. The confidentiality obligations hereunder will remain in effect until the Confidential Information enters the public domain through no action of the Receiving Party or any other person under legal obligation not to disclose such information.

(c) Material Non-Public Information. Clients and Experts will not disclose or otherwise share with any person, including other Clients or Experts, material non-public information which, if used by such person, might constitute illegal insider trading. “ Material non-public information ” is defined as any information that there is a substantial likelihood that a reasonable investor would consider important in making his or her investment decisions, or information that is reasonably likely to have a substantial effect on the price of a company’s securities, and which has not been effectively communicated to the marketplace.

- Termination

Subject to your obligation to pay for Services which you have received, you may cancel your account on the Website at any time by providing notice via email to support@enquire.ai and sending a copy to accounting@enquire.ai . For avoidance of doubt, cancelling your account shall not relieve you of any obligations under these Terms intended to survive termination, including, Sections.

- Non-Circumvention

(a) You acknowledge and agree that a substantial portion of the compensation the Company receives for making the Website available to you is collected through the Fees described in Section 10 and that in exchange a substantial value to you is the relationships you make with other Users when you identify or are identified by another person through the Website or Platform Services (“ Company Relationship ”). The Company only receives a Service Fee when a Client and an Expert pay and receive payment through the Platform Services. Therefore, for 12 months from the later of: (a) the start of a Company Relationship; or (b) the date you complete your last project for a Client (the “ Non-Circumvention Period ”), you agree to use the Company and its Platform Services as your exclusive method to request, make, and receive all payments for work directly or indirectly with that person or arising out of your relationship with that person and not to circumvent the payment methods offered through the Platform Services unless you pay a Headhunter Fee or a Retainer Commission as set forth below.

(b) Examples of circumvention include: (i) accepting proposals from, receiving Expert Services from, or making payments to any Experts first identified through the Platform Services outside of Company; or (ii) paying or reporting on the Platform Services a payment amount lower than that actually agreed between you and an Expert through the Platform Services. You are not authorized to expand the scope of any Services or Project through the Platform Services unless Company has agreed in advance and in writing.

(c) You agree to not solicit or accept any offer which violates these Terms unless we provide express written permission, as outlined below, which we may withhold at our discretion. If you become aware of any breach or attempted breach of this Section 17, you must immediately inform Company at support@enquire.ai . Termination of your relationship with Company will not affect your obligations under this Section 17.

- Headhunter/Retainer Fees

(a) Subject to Company’s prior express written permission, you may offer employment to an Expert introduced to you through the Platform Services during the Non-Circumvention Period. If this occurs within the Non-Circumvention Period, you agree to pay Company 25% of the hired Expert’s yearly Base Salary (as defined below) (“ Headhunter Fee ”). “ Base Salary ” is defined as the annual gross income of the hired Expert and does not include any additional bonuses or compensation, such as signing or discretionary bonuses, moving expenses, allowances or any other type of compensation. You agree to pay the Headhunter Fee to Company within thirty (30) days of the hired Expert’s start date of employment. If you extend an employment offer to an Expert after the Non-Circumvention Period, you will not be responsible for the payment of a Headhunter Fee.

(b) Subject to Company’s prior express written permission, you may procure services on a retainer basis from an Expert you were introduced to through the Platform Services during the Non-Circumvention Period. If this occurs within the Non-Circumvention Period, you agree to pay Company 30% of the monthly gross fees for services paid to the Expert, not including any reimbursable expenses (“ Retainer Commission ”). You will pay the Retainer Commission to Company within thirty (30) days of the end of each month in which you have procured services from the Expert for the lesser of one year or the duration of the engagement of the Expert on a retainer basis. If you procure services from Expert on a retainer basis after the end of the Non-Circumvention Period, you will not be responsible for the payment of any Retainer Commission.

(c) You understand and agree that if Company determines, in its sole discretion, that you have violated this Section, Company may, to the maximum extent permitted by law (a) close your account and revoke your authorization to use the Website and Platform Services, and/or (b) charge you for all losses and costs (including any and all time of Company’s employees) and reasonable expenses (including attorneys’ fees) related to investigating such breach and collecting such fees.

- Publicity

By agreeing to these Terms, you agree that Company may publicly reference and publish your business name and logo in customer lists, on the Website, in investor pitches and sales presentations and in promotional material, or otherwise for marketing and promotional purposes unless otherwise agreed to by the parties.

- Warranty Disclaimer

(a) You agree not to rely on the Platform Services or the continuation of the Website, or any information or Services provided by other users. The Platform Services are provided “as is” and on an “as available” basis. We make no express representations or warranties with regard to the Platform Services, content, user content, any work product provided to you, or any activities or items related to these Terms. To the maximum extent permitted by Law, we disclaim all express and implied conditions, representations, and warranties including, the warranties of merchantability, accuracy, fitness for a particular purpose, title, and non-infringement. Some jurisdictions may not allow for all of the foregoing limitations on warranties, so to that extent, some or all of the above limitations may not apply to you.

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---

## Blog posts

### Stakeholder Maps Expire Faster Than Strategy Decks — https://enquire.ai/blog/stakeholder-maps-expire-faster-than-strategy-decks

Author: Bridget Bowen · Published: 2026-09-28 · Topic: Consulting

Formal authority is only one source of influence. On a live policy issue, urgency changes, coalitions shift, implementation creates new actors, and informal brokers can become more consequential than senior titles. Stakeholder intelligence should be maintained as a dynamic, evidence-backed network rather than a workshop artifact.

Stakeholder Maps Expire Faster Than Strategy Decks
Formal authority is only one source of influence. On a live policy issue, urgency changes, coalitions shift, implementation creates new actors, and informal brokers can become more consequential than senior titles. Stakeholder intelligence should be maintained as a dynamic, evidence-backed network rather than a workshop artifact.
A public affairs team opens a stakeholder map built six months ago. The minister remains in the high-power, high-interest quadrant. The regulator is still labeled influential but neutral. The industry association is marked supportive.
The chart is tidy. The underlying relationships have already changed.

# Influence belongs to an issue
Static maps treat power and interest as durable attributes of a person or institution. In practice, influence depends on the issue, the stage of the policy process, the jurisdiction, and the actor's relationships with others.
A regulator may control implementation but have little influence over legislative design. A technical official may become central during drafting. A local trade group may matter only when enforcement begins. An actor with no formal authority may connect two coalitions that otherwise do not communicate.
The useful question is not, Who is powerful? It is, Who can affect this specific outcome now, through what mechanism?

# Map relationships as well as actors
A list of stakeholders cannot explain how influence travels. A network can.
For each material actor, record:

- The specific role in the issue

- Current position and degree of urgency

- Formal authority

- Informal influence or access

- Key relationships and coalition ties

- Evidence, confidence, and last update

- Events that could change the assessment

Relationships deserve the same attention as the actors themselves. A previously strong alliance may weaken. A new intermediary may connect government and industry. A technical working group may become the practical center of implementation.
Network measures can help, but centrality is not the same as power. A well-connected actor may transmit information without controlling the decision. A less connected actor may hold a veto. Metrics should support a causal explanation, not replace one.

# Put time on the map
Stakeholder maps decay because policy processes change their cast.
During agenda setting, elected officials, advisers, advocacy groups, and media voices may matter most. During drafting, lawyers and technical officials gain influence. During implementation, regulators, vendors, local authorities, courts, and enforcement teams can become decisive.
Every important node and relationship should therefore include a time marker. When was the judgment last verified? What event would make it stale? Is the influence increasing, stable, or declining?
Calendar reviews are useful, but events should drive refreshes. Elections, leadership changes, draft rules, court decisions, funding announcements, public consultations, enforcement actions, and coalition statements can all reshape the network.

# Add provenance before adding detail
Informal influence is difficult to verify and easy to exaggerate. A claim may come from an official record, a public statement, a local operator, or one person's interpretation of a relationship.
Label the source and confidence of each important claim. Distinguish observed interaction from inferred influence. Seek corroboration for sensitive or decision-critical judgments. If confidence is low, the map should show the uncertainty and direct the next research question.
This discipline also limits scope. The goal is not to map everyone. It is to maintain the smallest network capable of changing the policy strategy.

# Use the map to direct action and research
A living map should guide more than meeting requests. It should help the team decide:

- Which pathway could change the policy outcome

- Where the company lacks reliable local knowledge

- Which coalition is forming or weakening

- Which assumption should be tested before advocacy resources are committed

- Which stakeholder could provide evidence rather than access alone

Map coalitions, not just positions. Two actors may both support a policy for incompatible reasons and split during implementation. Two opponents may coordinate tactically despite different objectives. Those relationships often matter more than a simple supportive or opposed label.

# Protect sensitive network intelligence
Stakeholder data can be sensitive. Relationship assessments should be collected for a legitimate business purpose, access should be limited, and unverified claims should not be presented as fact. Teams should avoid recording personal detail that is irrelevant to the policy question.
The analytical value of a map does not justify careless collection or broad distribution. Strong governance protects both the people represented and the organization using the information.

# Where Enquire fits
Official sources can establish mandates, positions, consultations, and formal authority. Informal influence and emerging change often require current operating knowledge from people close to the issue.
Enquire can help teams gather and compare those perspectives across jurisdictions, retain the provenance and confidence behind each claim, and revisit the same policy question as the network changes. The platform is most useful when it helps the team test the map rather than simply add more names.

# A useful map shows how it could be wrong
No stakeholder map will perfectly represent political influence. Relationships are partly hidden, motives change, and shocks create new actors.
A strong map makes that uncertainty visible. It identifies the mechanisms of influence, the evidence supporting them, and the events that would require the team to redraw the network. That is what turns a static chart into working policy intelligence.
Sources and further reading
1. Ronald Mitchell Bradley Agle and Donna Wood, Toward a Theory of Stakeholder Identification and Salience, Academy of Management Review; 2. Chris Ansell and Alison Gash, Collaborative Governance in Theory and Practice, Journal of Public Administration Research and Theory; 3. Florence Metz and Laurence Brandenberger, Policy Networks Across Political Systems, American Journal of Political Science; 4. OECD, Principles on Stakeholder Engagement in Regulatory Policy; 5. OECD, Regulatory Policy Outlook 2025; 6. Enquire, current research platform overview

### Expert Disagreement Is Data If You Preserve It — https://enquire.ai/blog/expert-disagreement-is-data-if-you-preserve-it

Author: Public Equities · Published: 2026-09-14 · Topic: Consulting

Conflicting expert views do not automatically mean the evidence is weak. They may reflect different facts, definitions, incentives, horizons, geographies, or causal models. Before averaging the answers or selecting the one that fits the thesis, research teams should determine why the disagreement exists and what evidence could resolve it.

Conflicting expert views do not automatically mean the evidence is weak. They may reflect different facts, definitions, incentives, horizons, geographies, or causal models. Before averaging the answers or selecting the one that fits the thesis, research teams should determine why the disagreement exists and what evidence could resolve it.

Ten expert interviews produce an awkward result. Six respondents expect industry pricing to remain resilient. Three expect material deterioration. One argues that list prices are no longer the right measure because rebates and contract terms are changing faster.
The easiest synthesis is a vote count. It is also usually the least informative.

# Consensus can hide the useful evidence
Investment research has strong incentives to compress. Portfolio managers do not want ten transcripts, and analysts need a conclusion that can enter a model. The output becomes a range, an average, or a sentence saying that most experts agree.
That compression can erase the mechanism behind the answers. Six experts may agree because they share the same outdated data. Three may disagree because they operate in a customer segment that is turning first. The outlier may be the only respondent measuring the variable that matters.

# Identify what differs
Most disagreements fall into a manageable set of categories:

- Facts: respondents have different or differently dated information

- Definitions: they use the same word for different measures

- Assumptions: they answer under different unstated conditions

- Roles and incentives: their positions shape what they see or emphasize

- Horizons: a near-term operational view conflicts with a long-term structural view

- Geography or value chain: each respondent accurately describes a different part of the market

- Causal models: they accept the same facts but disagree about what those facts will produce

The category determines the follow-up. A factual gap needs fresher evidence, a definition gap needs a clearer question, and competing causal models need a discriminating test.

# Ask what would separate the models
The best follow-up is rarely, Why do you disagree with the others? That encourages debate and can push the expert toward defending a position.
Ask instead:

- What are you observing that leads to this view

- Under what conditions would your answer change

- What would we see next if your explanation is correct

- Which customer, geography, or channel should behave differently

- What evidence would make the competing explanation more credible

These questions turn disagreement into research design. They identify observations that should look different depending on which explanation is right.

# Build a disagreement ledger
A compact ledger should sit between the raw interviews and the investment memo. For each material disagreement, record:

- The precise question

- The competing answers

- The reason each expert gives

- Relevant differences in role, geography, horizon, or information

- Evidence shared by both sides

- Evidence that would discriminate among the views

- The next research step or signpost

# Do not average incompatible answers
Aggregation can help when experts estimate the same defined quantity independently. It is much less useful when respondents are answering different questions or describing different parts of a system.
Weight answers using relevant experience, information access, calibration, and reasoning quality. But do not delete a materially different explanation solely because it receives less weight.
The right output may be conditional rather than singular. Pricing may remain resilient in enterprise accounts while deteriorating among smaller customers. Near-term demand may be strong even as the long-term market structure weakens.

# Protect independence before synthesis
Expert evidence becomes less useful when later respondents are exposed to an emerging consensus. Interview guides narrow, follow-up questions converge, and social influence reduces independent information.
Keep initial responses separate where possible. Use a consistent core question, capture the reasoning before sharing other views, and only then ask experts to react to a competing explanation. This preserves both independent judgment and the value of direct challenge.

# Where Enquire fits
Research systems often compress multiple expert perspectives into one polished answer too early. Enquire can help preserve alignment and divergence across traditional calls, asynchronous AI-led interviews, and shorter expert inputs while retaining the context behind each view.
The useful output is not an automated consensus. It is a structured record of where experts agree, why they differ, which assumptions carry the disagreement, and what the team should test next.

# A strong synthesis explains the disagreement
Expert disagreement is not always profound. It may reflect poor sampling, ambiguous questions, or stale experience. But when credible respondents differ for identifiable reasons, the disagreement is data.
The strongest synthesis does more than report the majority view. It explains which parts of the market support each answer, what evidence would distinguish them, and how the investment conclusion changes under each case.

Sources and further reading
1. M Granger Morgan, Use and Abuse of Expert Elicitation in Support of Decision Making for Public Policy, PNAS; 2. Jan Lorenz et al, How Social Influence Can Undermine the Wisdom of Crowd Effect, PNAS; 3. Abigail Colson and Roger Cooke, Expert Elicitation Using the Classical Model to Validate Experts Judgments; 4. Philip Tetlock et al, Forecasting Tournaments Tools for Increasing Transparency and Improving the Quality of Debate; 5. Charlan Nemeth, Minority Influence Theory; 6. Enquire, Capital Markets

### The Enquire Edge for VC Investors and Board Members — https://enquire.ai/blog/the-enquire-edge-for-vc-investors-and-board-members

Author: Enquire Team · Published: 2026-09-11 · Topic: Venture Capital

Company updates and board decks answer the questions management has already asked. The harder and most important questions, the ones that could affect a company's trajectory are rarely asked or investigated ahead of the meeting. Enquire exists to solve this problem.

Company updates and board decks answer the questions management has already asked. The harder and most important questions, the ones that could affect a company's trajectory are rarely asked or investigated ahead of the meeting. Enquire exists to solve this problem.

Consider a venture investor preparing for a portfolio company board meeting. The board deck arrives a day or two in advance with the obvious facts: pipeline, burn, hiring plan, a competitive slide listing familiar names, and a roadmap. The numbers are accurate. The narrative is coherent. And by the time the meeting begins, the agenda has already been set by the only party at the table with complete information about the business: management.

This is the structural condition of venture governance, and it is not a failure of character on anyone's part. Management holds the operating data, the customer conversations, and the framing. Management is also, reasonably, motivated to demonstrate control. The result is a meeting organized around answering the questions that have already been asked. The questions that matter the most and could change the company’s trajectory are rarely on the radar.

The board member’s edge and real value is independent preparation on the hardest issues ahead of the meeting.

## The asymmetry is one directional, and it compounds
An operating partner sits on a board precisely because the company believes judgment adds value beyond capital. But judgment operates on inputs, and the inputs are supplied by the party being governed.

Three categories of information behave differently at a board table. Known knowns : revenue, burn, headcount are reported reliably and consume most of the agenda. Known unknowns: a contract that may not renew, a hire that may not close are typically disclosed, but framed as managed risks with a plan attached. Unknown unknowns : the hardest and most important second & third order consequences of a shift in an adjacent market, a competitor's repositioning, a bump into a product roadmap are often absent. No one at the table has the tools, the time or the incentive to raise them, and management, operating inside the business at speed, is often the last to see them.

That third category is where boards either add value or fail to. Enquire exists to help board members do exactly this.

## Second & third order effects should be the board's territory
The most questions a well equipped board should ask is not “ how is the business performing” , but rather “ which trends already in motion or early shoots could change the company’s trajectory twelve months from now. ”

Enquire’s Research Center is structured to independently and intentionally surface trends, products, competitors, risks and their relevant second and third order consequences. None of this is set to directly contradict management's numbers. Instead, it fundamentally changes the board discussion. This is a different kind of contribution than scrutiny, and it is the kind that founders generally welcome.

## The constraint has always been the time and cost to develop / frame the most important questions
Every experienced director knows this work is worth doing. Almost none of them do it, for a straightforward reason. A serious independent read on a private company and its market has historically taken a research team days or weeks. No venture investor has a research team per portfolio company. The economics of the board seat do not support it. So directors read the deck, arrive with instinct, and rely on the quality of the conversation to surface what the preparation did not.

This is the problem Enquire's Research Center uniquely solves. It produces structured analysis on private companies and themes where no external coverage exists, drawing on more than sixty analytical modules across a dozen knowledge domains. Those modules span competitive and fundamental analysis, forecasting, and a dedicated set aimed at the uncertainties that blindside operators. Every finding carries multiple provenanced citations, which matters when a director intends to raise it in front of a founder. Where the public evidence runs out or where deeper insights or scenarios are warranted, the questions that remain are put to practitioners in the relevant sector through the Expert Network, with responses inside twenty-four hours.

## Better questions, not more information
The constraint on venture governance was never information scarcity. There is already more information about any market than a board can absorb, and more is produced every day. The constraint is that raw volume does not organize itself into the two or three questions that would change a decision.

Three things close that gap. Provenanced evidence a director can put in front of a founder without qualification. Direct access to practitioners when the public record is silent. And the discipline to convert both into questions rather than assertions. Better questions produce better intelligence. Better intelligence produces decisions a board can stand behind.

Before your next board meeting, consider a simple test. Look at the agenda and identify which items exist because management proposed them, and which exist because a director asked something management had not anticipated. The ratio is a reasonable proxy for how much the board is contributing.

### What is Intelligence Part II — https://enquire.ai/blog/what-is-intelligence-part-ii

Author: Zach Riggle · Published: 2026-09-09 · Topic: Consulting

From Information to Understanding In Part I, I argued that the central challenge facing investment professionals is no longer simply gaining access to information. It is determining what, among an expanding universe of available information, actually matters to the decision at hand.

## From Information to Understanding
In Part I, I argued that the central challenge facing investment professionals is no longer simply gaining access to information. It is determining what, among an expanding universe of available information, actually matters to the decision at hand.
That distinction leads to a second question: if information is not intelligence, what has to happen to transform it into intelligence? That is to say, what must happen before it becomes something a decision-maker can use?
One way of approaching the problem is through a hierarchy familiar to intelligence professionals:
Data → Information → Knowledge → Understanding
The terms are often used interchangeably in ordinary conversation. In intelligence work, they describe significantly different stages of development. Each step represents more than an increase in volume or sophistication. It represents a change in the relationship between what is known and the problem the decision-maker is trying to solve.
For investment research, that distinction has become increasingly important. Modern technology is exceptionally good at helping us move through the lower levels of the hierarchy. The harder question is whether it can help us move higher.

## From Data to Knowledge
Data is the raw material: observations, signals, events, numbers and other inputs that have not yet been meaningfully organized. A company reports revenue. A competitor launches a product. A regulator proposes a rule. A stock moves five percent. On their own, these are facts about the environment.
Data becomes information when it is processed into a usable form. It may be formatted, translated, plotted, organized, correlated or placed alongside other observations. A quarterly revenue figure becomes more informative when viewed against prior periods, management guidance, consensus expectations and competitor performance.
At this stage, structure has been added, but not necessarily meaning. Meaning emerges through analysis. Information becomes knowledge when it is evaluated, integrated and interpreted well enough to support a conclusion. Individual facts are placed into context. Evidence is compared. Relationships emerge. The analyst is no longer simply describing what is present; he or she can make an informed assessment about what appears to be true.
That is an important step. It is also where much conventional research effectively ends. The facts have been collected, organized and analyzed. The research has produced an answer. But a decision-maker often needs more than the answer.

## Knowledge Is Not Understanding
The distinction between knowledge and understanding is easy to blur because both imply that we know something. In intelligence work, however, the difference is consequential.
A math problem offers a useful analogy. Knowledge is arriving at the correct answer. Understanding is being able to show your work: to explain why the answer is correct and, importantly, to solve the problem again when the variables change.
Consider an investor assessing whether a company has pricing power. Data may show that the company raised prices by 8 percent while unit volumes declined by only 1 percent. Information places those figures alongside historical pricing, competitor actions, retention and industry inflation. Analysis may support the conclusion that the company does, in fact, possess pricing power. That conclusion is knowledge.
Understanding requires knowing why. Perhaps the product is mission-critical, switching costs are high, credible alternatives are limited and the product represents only a small share of the customer's total cost base. Those mechanisms explain the result.
The distinction becomes most valuable when circumstances change. A new competitor enters the market. A substitute improves. Customer economics deteriorate. An investor who understands the mechanisms supporting the original conclusion can assess whether the company's pricing power still holds. An investor who knows only the conclusion has no equivalent basis for adaptation.
A static answer can be correct when it is produced and wrong when it is used. Understanding provides the means to know when, and why, it should change.
This is why judgment matters. Intelligence professionals can collect, process and analyze information to develop knowledge. But the decision-maker combines that knowledge with an appreciation of the broader situation, then applies experience, judgment and intuition. Understanding is not simply a more detailed answer. It is the ability to see how the answer was produced and how it should change as the situation changes.

## Purpose Changes the Research Problem
The "why" matters at another level as well: not only why a conclusion is true, but why the research is being conducted in the first place.
Military doctrine offers a useful parallel in the concept of Commander's Intent. A unit ordered to seize a piece of terrain knows its task. A unit that knows it must seize that terrain in order to prevent an enemy reinforcement understands the purpose. If circumstances make the original task impossible, the second formulation provides enough context to adapt. The distinction is between knowing what to do and understanding what must ultimately be accomplished.
The same principle applies to research. Consider an analyst asked to "research the competitive landscape." Taken literally, the assignment may produce a comprehensive account of competitors, market shares, products, funding histories and recent strategic activity. The work could be excellent and still fail to address the investment question.
Now frame the assignment around its purpose:
Determine whether this company's competitive advantage is durable enough to support a five-year underwriting case.
The research problem changes immediately. The relevant question is no longer simply, "Who are the competitors?" It becomes: what do we need to know about the competitive environment to determine whether this advantage can persist, and what evidence would tell us that our current assessment is wrong?
One approach begins with a subject. The other begins with a decision. One encourages comprehensive coverage. The other demands discrimination. This is where research starts to resemble intelligence.

## Intelligence Has to Be Usable
There is one further distinction worth making. Intelligence has little value simply because it exists. Its value is realized when it can be used.
Marine Corps intelligence doctrine treats utilization as part of the intelligence process itself. An assessment is not valuable merely because it is analytically sound. It must reach the decision-maker in time, in context and in a form that can contribute to judgment.
A brilliant analysis delivered after the decision has been made has little practical value. A comprehensive assessment that a decision-maker cannot navigate has limited value. A 100-page report may contain the answer somewhere on page 74, but that does not mean it has performed the function intelligence is intended to serve.
At Enquire, we have increasingly described this requirement using three deliberately simple words:
Navigable. Consumable. Usable.
The point is not stylistic. If intelligence exists to reduce uncertainty and support judgment, its value cannot be measured by the number of pages produced, sources searched or datapoints collected. The relevant measure is whether the decision-maker can find what matters, understand its significance and apply it to the problem at hand.
That standard becomes more important as generative AI reduces the cost of producing research. We can already search more documents, summarize more sources and generate more analysis faster than at any point in the history of professional research. Those capabilities are valuable. But producing more information does not automatically move us higher up the information hierarchy. Sometimes it simply produces more information.
The more interesting opportunity is to help investors move from information toward understanding. That distinction increasingly shapes how we think about research at Enquire. The ambition is not merely to accelerate the production of research, but to support the harder work that gives research purpose, context and utility.
A fuller treatment of what that means for the research environment itself will emerge later in this series. For now, it is enough to sharpen the working definition introduced in Part I:
Intelligence is decision-relevant knowledge, deliberately developed and placed into context in support of a specific objective, then made navigable, consumable and usable so that a decision-maker can develop understanding, reduce uncertainty and exercise better judgment.
This definition is deliberately demanding. It makes intelligence more than collected information, more than analysis and more than a finished report. It requires purpose at the beginning and use at the end. It also subtly explains the bridge between intelligence and understanding. (Don’t worry if you missed it; countless research/intelligence practitioners, professionals, and providers have been missing it for decades, if not longer.)
The bridge between intelligence and understanding is the active participation of the end-user . This is why, in the nearly five years of accumulated time I spent in combat theaters as an intelligence officer, I was rarely beyond the reach of the unit commander.
I had data. He had context. I had information. He had judgement. I had knowledge. He had experience and intuition.
I had intelligence.
He had understanding .

Next: The Intelligence Cycle and the Case for Dynamic Research

### Why Enquire Is the Ideal Research & Diligence Partner Across the Private Equity Lifecycle — https://enquire.ai/blog/why-enquire-is-the-ideal-research-diligence-partner-across-the-private-equity-lifecycle

Author: Enquire Team · Published: 2026-09-04 · Topic: Private Equity

Built around the way experienced private equity investors and operators work, Enquire delivers deeply researched, fully provenanced insights, frameworks, and outputs that are ready to be put to work.

Built around the way experienced private equity investors and operators work, Enquire delivers deeply researched, fully provenanced insights, frameworks, and outputs that are ready to be put to work.
Private equity research often breaks at the handoffs: The market thesis lives in one presentation, target-screening analysis sits in a spreadsheet,. Expert call notes are scattered across inboxes, diligence findings appear in a separate investment memo, Post acquisition priorities are rebuilt over and over again for operating partners and management teams.
At every transition, context is lost. Assumptions become detached from their supporting evidence. Teams repeat work that has already been completed. By the time an asset enters the portfolio, the operating team may be reconstructing the reasoning that led to the investment in the first place.
Experienced, successful private equity investors work differently. They develop hypotheses, pressure-test them, identify the debates that matter, and progressively refine their conviction. They connect market structure to company performance, company performance to value creation, and value creation to the specific decisions management and the board must make. Most importantly, they carry those insights forward rather than starting over at each stage.
That is how Enquire is built. Enquire provides a consistent research and decision support partner across the investment lifecycle: from identifying attractive markets and companies to evaluating targets, conducting deep diligence, and supporting portfolio ownership. Its frameworks, expert insights, analyses, and deliverables can be combined with a firm’s proprietary research, notes, and investment processes, creating a shared and continuously developing body of knowledge.
The result is not more research. It is research organized around better investment decisions.

## Stage A: Framing the Markets, Themes, and Companies Worth Pursuing
Experienced investors know that sourcing begins long before a target enters a process.
It begins with a view of where to play: which markets are structurally attractive, which themes are likely to persist, which business models are best positioned, and which companies offer the most compelling combination of quality, strategic relevance, and addressability. This requires more than a broad market overview. It requires a disciplined way to move from a macro theme to an actionable target list.
Enquire offers more than 20 frameworks and research solutions that can be used to identify and frame attractive opportunities. These can support work such as:

- Defining markets and subsegments

- Identifying structural growth drivers and constraints

- Assessing market maturity and competitive intensity

- Sizing markets and individual opportunity pools

- Mapping industry value chains and profit pools

- Comparing competitors, products, and business models

- Ranking companies against investment specific criteria

- Identifying the most attractive targets within a market or theme

- Evaluating market entry paths, consolidation opportunities, and strategic adjacencies

The important distinction is that these frameworks are not applied mechanically. They are organized around the questions an experienced investor or operator would ask.
A large market, for example, is not necessarily an attractive market. The more relevant questions are whether growth is durable, where value accrues, how competition is evolving, what creates defensibility, and whether an investor can identify a credible path to differentiated returns.
Similarly, a target screen should not be a static list of companies. It should explain why particular companies rank highly, which criteria drive that ranking, where information remains uncertain, and which targets warrant the next increment of time and attention.
Enquire turns a broad sector interest into a decision architecture: a clearly framed market, a set of relevant themes, a ranked universe of companies, and an explicit rationale for where the team should focus.
The output is ready to inform a sourcing strategy, sector thesis, investment committee discussion, or target development program.

## Stage B: Developing an Informed Early Read on a Target
Once a target emerges, the nature of the decision changes. At this point, the team does not yet need every possible answer. It needs to determine whether the opportunity deserves scarce diligence resources, senior attention, and further engagement. An effective early read therefore has to balance speed with substance.
With Enquire, an initial investment memo can be developed in under two hours, giving the deal team a structured view of the company, market, potential investment thesis, key risks, acquisition considerations, and preliminary value creation opportunities. This is not intended to replace diligence. It is designed to make diligence more intelligent.
A strong early-stage assessment should clarify:
Why the company may be attractive. What is distinctive about its market position, business model, product, customer base, or competitive advantage?
What may underwrite future value. Which market developments, operating improvements, strategic initiatives, or inorganic opportunities could drive returns?
What could undermine the thesis. Where are the major commercial, competitive, operational, technological, or regulatory risks?
What the team should investigate next. Which unanswered questions are most likely to change the investment decision?
How the opportunity might be pursued. What acquisition tactics, engagement angles, or strategic narratives may be relevant?
This reflects the way experienced investors approach an unfamiliar company. They separate what is known from what is inferred. They identify the assumptions on which the emerging thesis depends. They focus the next phase of work on the questions with the highest decision value.
Speed matters, but only when it accelerates understanding. Enquire allows the team to move quickly without reducing the work to a shallow company summary. The result is an early read that can be circulated internally, debated by senior investors, used to guide initial outreach, and converted directly into a focused diligence agenda.

## Stage C: Conducting Diligence Around the Debates That Matter
As a deal progresses, the research challenge becomes deeper and more specific.
The objective is no longer simply to understand the market or company. It is to develop conviction on the issues that determine whether the investment thesis is sound.
Too much diligence is organized around collecting information rather than resolving debates. Teams conduct broad calls, commission extensive reports, and accumulate hundreds of pages of material without clearly connecting the evidence to the decision.
Experienced investors work in the opposite direction. They begin with the investment case and ask: What has to be true? Where are we least certain? Which assumptions create the greatest risk? What evidence would increase or reduce our conviction?
Enquire supports this debate led approach with access to deep industry expertise through several formats.
One to one expert calls can be used when the team needs detailed discussion, follow up questions, or nuanced interpretation. AI interviews can broaden the range of perspectives collected and make it possible to explore a topic with greater flexibility. Short form questions can quickly test highly specific assumptions without requiring a full interview process.
These formats can also be combined. A team might begin with short-form questions to identify areas of disagreement, use AI interviews to broaden coverage, and then conduct focused one-to-one calls on the issues requiring the deepest judgment.
The research can address questions such as:

- How durable is customer demand?

- What truly drives purchasing decisions?

- How high are switching costs in practice?

- How does the company compare with its strongest competitors?

- Is pricing power sustainable?

- How are products perceived by customers, partners, and former employees?

- Which market developments are most likely to alter growth?

- Where could the management plan prove too optimistic?

- Which value-creation initiatives are operationally realistic?

- How are competitors likely to respond?

Enquire can provide the underlying expert output, synthesized analysis, or both.
That flexibility is important. In some situations, an investor may want direct access to the raw evidence and the precise language used by an expert. In others, the team may need a concise synthesis of what the evidence means for a particular investment debate. Often, the most useful deliverable combines the two: decision ready conclusions with clear provenance back to the supporting inputs.
Conflicting perspectives are not flattened into artificial consensus. They can be surfaced, contextualized, and assessed against the specific assumptions in the investment case.
This creates a stronger diligence record and a more productive investment committee discussion. Instead of presenting a volume of disconnected research, the deal team can show how each critical question was examined, what the evidence indicates, where uncertainty remains, and how that uncertainty affects the recommendation.

## Stage D: Carrying the Investment Thesis Into Ownership and the Boardroom
The acquisition is not the end of the investment process. It is the point at which the underwriting becomes an operating agenda.
Yet this is another stage at which valuable context is often lost. The deal team has developed a detailed understanding of the market, competitors, customers, risks, and potential value creation levers. But those insights may not reach the operating partners, management team, or board in a usable form.
Enquire helps preserve the continuity between diligence and ownership.
The same research base that supported the investment decision can be developed into board preparation materials, value creation strategies, competitor assessments, market updates, and analyses of important strategic decisions.
Board preparation, for example, should do more than summarize performance. It should connect current results to the assumptions made during underwriting. It should identify where the market or competitive environment has changed. It should clarify which decisions require board attention and what evidence should inform them.
Enquire can support the development of board materials that combine company information with external market intelligence, helping directors and management teams engage with the issues that matter most.
The same principle applies to value creation.
Generic operating playbooks may identify familiar levers: pricing, salesforce effectiveness, product development, cost reduction, acquisitions but they do not explain which levers are most relevant for a particular company or how they interact.
A deeply researched value creation strategy considers the company’s actual market position, customer behavior, competitive dynamics, operating capabilities, and organizational constraints. It connects strategic ambition to the conditions required for execution.
Enquire can also help teams examine the second- and third-order effects of company and competitor actions.
Consider a competitor introducing a lower-priced product. The immediate question may concern pricing pressure. But the next-order questions may be more consequential: How will channel partners respond? Will the move alter customer expectations? Could it change the service burden or product mix? Will it affect working capital, retention, or the company’s premium positioning? How might other competitors react?
Experienced operators look beyond the visible first order impact. Enquire helps bring that same discipline to portfolio-company decision-making.

## One Evidence Base, Adapted Across the Firm
The value of a research partner compounds when the work from one stage remains accessible and useful in the next.
Enquire enables firms to upload and combine their proprietary materials including internal research, investment notes, historical analyses, expert-call records, templates, and established processes with new external research and expert insight.
This allows each firm to build on the way it already invests rather than forcing its investment process into a generic structure.
A sector team can incorporate its existing market taxonomy. A deal team can use the firm’s preferred investment-memo format. Operating partners can connect diligence findings to established value-creation frameworks. Portfolio teams can preserve important market and competitive insights between board cycles.
Different teams can access the same underlying knowledge in the form most relevant to their roles.
The deal team may need a detailed debate map and investment thesis. The investment committee may need a concise recommendation supported by clearly attributed evidence. Operating partners may need a prioritized set of value-creation initiatives and execution dependencies. Management and the board may need a focused view of market changes, competitive moves, and strategic decisions.
The presentation changes, but the underlying evidence and chain of reasoning remain consistent.
That consistency creates a shared institutional memory. It reduces duplication, limits information loss, and makes it easier for teams to understand not only what was decided, but why.

## What “Investment-Committee-Ready” Really Means
An investment committee ready output is not simply a well formatted presentation.
It makes the logic of the decision visible.
It explains why the market is attractive, why the company is positioned to win, why the opportunity exists now, and what must be true for the investment to succeed. It distinguishes evidence from assumption. It identifies the strongest counterarguments. It shows where expert perspectives agree and disagree. It connects key risks to potential mitigants and value-creation opportunities.
Most importantly, it enables the committee to spend its time on judgment rather than reconstruction.
The same standard applies after the investment. A board-ready output should not simply contain more information. It should clarify what has changed, why it matters, what choices are available, and which actions should follow.
Enquire is designed to produce work that is ready to enter these decision environments. Its research is not an end product sitting outside the investment process. It becomes an input to the memo, the committee discussion, the value-creation plan, the board deck, and the next strategic decision.

## A Research Partner That Compounds Across the PE Lifecycle
The strongest private equity firms do not treat sourcing, diligence, and ownership as separate analytical exercises.
They develop knowledge over time. They revisit earlier assumptions. They carry market insight into company evaluation, company insight into value creation, and value-creation insight into board-level decisions. Each stage sharpens the next.
Enquire provides the consistent, deeply provenanced research foundation needed to support that model.
From framing attractive markets and ranking targets, to producing rapid early-stage investment memos, conducting expert-led diligence, developing value-creation strategies, and preparing for board discussions, Enquire works across the full investment lifecycle.
By combining external expertise with a firm’s proprietary content, notes, frameworks, and processes, it also makes the resulting insight accessible across the entire private equity value chain.
The firm does not have to rebuild its understanding at every stage. Its knowledge becomes reusable. Its decisions become easier to trace. Its teams work from a shared body of evidence. And its research becomes more valuable with every investment, expert interaction, and portfolio-company decision.
That is what makes Enquire more than a point solution or research provider.
It is a partner built around how experienced private equity investors and operators actually work.

### What is Intelligence? Part I — https://enquire.ai/blog/what-is-intelligence-part-i

Author: Zach Riggle · Published: 2026-08-31 · Topic: Consulting

When More Information Isn’t the Answer Intelligence has become one of the more elastic words in modern business. There is market intelligence, business intelligence, competitive intelligence and consumer intelligence. Retailers use increasingly sophisticated systems to predict what customers will buy. Streaming services anticipate what viewers might want to watch. Financial platforms collect,…

## When More Information Isn’t the Answer
Intelligence has become one of the more elastic words in modern business.
There is market intelligence, business intelligence, competitive intelligence and consumer intelligence. Retailers use increasingly sophisticated systems to predict what customers will buy. Streaming services anticipate what viewers might want to watch. Financial platforms collect, organize and analyze enormous volumes of market data. And artificial intelligence has introduced an expanding category of products promising some form of intelligence on demand.
Yet much of what is described as intelligence is, more accurately, information. And information, however plentiful, sophisticated or rapidly produced, is not necessarily intelligence.
The distinction is not semantic. It matters because investment professionals now have access to more data, research, transcripts, expert commentary, news, alternative data and machine-generated analysis than at any previous point in the industry’s history. The longstanding problem of gaining access to information is gradually being replaced by a different one: determining what, among everything available, actually matters.
For investors, the problem is increasingly not scarcity but abundance.
The question, then, is not simply how to obtain more information. It is how to turn information into something useful for judgment and decision-making.

## Intelligence begins with a decision
I have spent much of my career working with intelligence in very different settings. I began as an intelligence officer and special operations intelligence officer in the Marine Corps and U.S. Marine Corps Forces Special Operations Command, and later worked in the private sector as a managing director at Veracity Worldwide, a global corporate intelligence firm. Today, my work at Enquire brings me into another information-intensive environment: investment research.
The contexts are plainly different. A military commander, a corporate executive and an investor face different questions, sources and consequences. But they share an important constraint. Each must make consequential decisions with incomplete information and under conditions of uncertainty.
That is the problem intelligence is intended to address.
There are formal doctrinal definitions of intelligence, some of which I will return to later in this series. A useful working definition is simpler:
Intelligence is decision-relevant knowledge, deliberately developed and placed into context in support of a specific objective, with the purpose of reducing uncertainty and enabling better judgment.
The important feature of that definition is purpose.
A fact can be accurate without being useful. A report can be meticulously researched without answering the question that matters. A database can contain every relevant datapoint and still leave its user uncertain about what to do.
Marine Corps intelligence doctrine draws much the same distinction. Data and information do not become intelligence simply because they have been collected. They must be focused, interpreted and placed in context for a particular decision.
Good intelligence work therefore begins before the search for information. It begins by defining the problem: the decision to be made, the questions that decision depends upon, the uncertainties that matter most and the evidence that might alter the current assessment.
Without that discipline, research can easily become an exercise in accumulation.

## The danger of knowing more
One of the more counterintuitive lessons of intelligence work is that more information does not necessarily make a decision-maker better informed.
Sometimes it does the opposite.
When little intelligence is available, uncertainty is difficult to ignore. A capable commander knows that important things remain unknown and plans accordingly. Contingencies are developed, assumptions are identified and risk is treated explicitly.
A headquarters saturated with reporting can create a different problem. Imagery, databases, assessments and operational updates produce the impression that the answer must exist somewhere, if only enough material is reviewed.
The natural response is to keep looking.
More reporting is requested. More sources are consulted. Competing explanations multiply. The amount of information increases while the decision itself may become no easier to make.
This is not an argument for less information. It is an argument for discrimination. Information has value to the extent that it contributes to the decision.
In operational settings, that distinction was often stark. A single piece of decision-relevant intelligence could outweigh pages of supporting reporting. Sometimes the critical question was extraordinarily narrow: Was a target present? Had a particular condition been met? Had a key indicator occurred?
A single answer might determine whether an operation proceeded.
The surrounding information still had value. It provided context, supported analysis and could matter later. But at the point of decision, one piece of intelligence mattered disproportionately because it was directly connected to action.
That is one of the central functions of intelligence: not merely to tell a decision-maker what is known, but to distinguish what matters from what merely exists.
Military intelligence doctrine explicitly warns that presenting a commander with all available data can magnify uncertainty rather than reduce it. Irrelevant, incomplete and contradictory information can overwhelm the very person the intelligence function is meant to support.
The observation feels increasingly relevant outside the military.

## From information scarcity to information abundance
Investment research has historically devoted considerable resources to solving problems of access.
Where can I find the data? Who has researched this market? How can I locate the relevant filing? Is there an expert who understands the industry? Can I search across transcripts, company documents and third-party research efficiently?
Technology has made many of these tasks dramatically easier. Generative AI can search large collections of documents, extract datapoints, summarize lengthy materials, compare companies, surface themes and assemble research products at remarkable speed.
These are meaningful advances. But faster access to information does not, by itself, solve the intelligence problem.
It may even make the distinction between information and intelligence more important.
As the cost of finding and producing information falls, the scarce resource shifts. The challenge becomes deciding what deserves attention, which questions are most consequential, where important gaps remain, how conflicting evidence should be interpreted and what uncertainty still cannot be resolved.
Those are not primarily retrieval problems.
They are intelligence problems.
The implication for investment research is significant. The most useful research technology will not simply make it possible to search more sources or generate longer reports in less time. Its value will increasingly depend on whether it helps investors structure the problem itself: to identify the questions that matter, separate signal from noise, recognize gaps in the evidence, test competing explanations and understand what would have to be true for an investment thesis to hold.
In other words, the objective is not to make the pile bigger, faster.
It is to transform what is available into something a decision-maker can use.
That raises a deeper question. What actually occurs during that transformation? How does raw data become information, information become knowledge, and knowledge become the kind of understanding that supports sound judgment?
That is where Part II begins.
Next: What Is Intelligence? Part II: From Information to Understanding

### Scenarios Fail When No One Tracks the Signposts — https://enquire.ai/blog/scenarios-fail-when-no-one-tracks-the-signposts

Author: Enquire Team · Published: 2026-08-20 · Topic: Consulting

A vivid set of alternative futures can improve a strategy workshop and still have no effect on the strategy. The operating asset is the system underneath the stories: assumptions, observable signposts, owners, decision thresholds, and…

A vivid set of alternative futures can improve a strategy workshop and still have no effect on the strategy. The operating asset is the system underneath the stories: assumptions, observable signposts, owners, decision thresholds, and options the organization has agreed to preserve.

Three months after a scenario-planning workshop, the strategy team reconvenes to review a major investment.

The workshop had gone well. Executives had debated four plausible futures, challenged the company's assumptions, and discussed what each world would mean for customers, competitors, regulation, technology, and capital allocation. The scenarios had memorable names. The final deck was excellent.

Now the investment committee needs a decision.

Someone asks which scenario appears to be emerging.

No one is quite sure.

The external environment has moved, but the team never agreed which developments would count as evidence for one scenario rather than another. No one owns the assumptions. There is no threshold for reopening the investment. The scenario deck lives in the strategy folder while the capital decision proceeds using an updated version of the base case.

The company did scenario planning.

It did not build a scenario capability.

That distinction matters because the strategic value of scenarios does not reside primarily in the stories themselves. Stories make uncertainty discussable. They expose assumptions and force leaders to consider different causal structures.

But organizations allocate capital, change supply chains, enter markets, hire capabilities, delay investments, and exercise options through decisions, not stories.

For scenarios to affect those decisions after the workshop, each important uncertainty needs to be translated into an operating system:

assumption → signpost → evidence owner → trigger threshold → decision → low-regret action → option to preserve.

The scenario narrative helps create that system.

It should not be mistaken for the system itself.

## The workshop is not the capability

Scenario planning is often strongest at the moment of creation.

Executives are temporarily pulled out of the annual-planning process. Assumptions that normally sit invisibly inside budgets become debatable. People from different functions compare views of the external environment. The organization permits itself to consider futures that do not fit the official forecast.

Then normal management resumes.

This is where scenario planning often loses contact with resource allocation.

McKinsey described a version of the problem in its work on strategy under uncertainty: companies may construct several scenarios, discuss their relative plausibility, select one as the base case, and then effectively stop dealing with uncertainty. The authors argue instead for explicit trigger points that cause decisions to be reconsidered as new information changes the odds.

The gap is still visible in 2026. In McKinsey's recent work on geopolitical foresight, 53% of respondents said scenario planning, real-time intelligence modeling, and related foresight approaches would significantly strengthen their organizations' geopolitical resilience, yet fewer than 30% reported using such tools to guide decisions. The same research emphasizes trigger points, dashboards, monitoring processes, and governance as necessary to keep scenarios operational as conditions change.

The implication is more demanding than “refresh the scenarios regularly.”

A scenario program should leave behind something that remains useful even if nobody rereads the stories.

It should tell the organization:

What assumptions are we currently making?

Which observable developments would make those assumptions less credible?

Who is responsible for detecting them?

At what point does the new information deserve a decision?

What can we do now that makes several futures easier to handle?

And which strategic option will disappear if we wait too long?

That is what turns episodic foresight into ongoing management.

## Separate scenarios from predictions

The first requirement is to stop asking scenarios to forecast which future will occur.

Shell, which has used scenario thinking for decades, is unusually explicit about this distinction. Its current description says scenarios explore different possible futures under different assumptions; they are not predictions, expectations, a business plan, or an expression of company strategy. Their purpose is to stretch thinking and improve decisions.

That sounds like a methodological caveat. It has an important operating consequence.

If scenarios are treated as competing forecasts, the organization's post-workshop task becomes:

Which scenario is winning?

That can create endless arguments about probabilities.

If scenarios are instead treated as structured hypotheses about what could make the strategy succeed or fail, the question becomes:

Which assumptions are changing, and does that alter what we should do?

The second question is more actionable.

Research on Shell's use of scenario planning in Russia demonstrates why prediction is the wrong performance standard. Riccardo Vecchiato's longitudinal case study examined Shell's Russian ventures from 1994 to 2016. The scenarios did not foresee several major events in the Russian energy market. But they did direct management attention toward possibilities including a greater role for gas, the strategic importance of transportation infrastructure, and increasing government intervention. The study argues that this helped managers adjust their beliefs and respond to developments that had not themselves been predicted by the scenarios.

It is one company case and should not be generalized into proof that scenario planning produces superior investment outcomes.

Its more useful lesson is narrower:

> A scenario can be wrong about events and still improve decisions if it changes what managers notice, investigate, and preserve the option to do.

That suggests a better quality test.

Do not ask, “Did our scenario come true?”

Ask:

Did it identify an uncertainty early enough that we knew what to watch and retained a useful choice when the evidence changed?

## Convert every important scenario into signposts

The transition from narrative to operating system begins by decomposing the scenario.

Suppose a company is planning around a future in which customers adopt a new industrial technology rapidly.

That future may depend on several assumptions:

Technology performance improves enough.

Economics cross customers' required payback threshold.

Regulation becomes supportive.

Critical infrastructure becomes available.

Competitors enter and normalize adoption.

Customers develop enough internal capability to implement the technology.

Those assumptions are much more useful operationally than the scenario label.

Now ask what could be observed before the final outcome becomes obvious.

For technology performance, perhaps it is the first commercially validated installation above a specified scale.

For customer economics, it could be verified operating cost per unit.

For policy, it may be publication of a final rule rather than political rhetoric.

For adoption, it might be signed customer commitments rather than stated interest.

For supply, perhaps a key component's lead time falls below a defined threshold.

Those are signposts.

The concept has been developed particularly rigorously in research on adaptive policymaking under deep uncertainty. Raso, Kwakkel, Timmermans, and Panthou describe adaptive plans as requiring monitoring systems composed of signposts and triggers. They propose evaluating signposts according to four characteristics: relevance, observability, completeness, and parsimony.

Those four tests translate well into corporate strategy.

Relevance: Does movement in the signpost actually tell us something about an assumption material to the strategy?

Observability: Can we measure or assess it reliably enough to distinguish change from noise?

Completeness: Taken together, do our signposts cover the critical ways the strategy could become inappropriate?

Parsimony: Are there few enough that somebody will actually monitor them?

That last criterion deserves more weight than it normally receives.

A scenario workshop can easily produce 60 things to watch.

An operating system with 60 supposedly critical indicators has probably identified none.

McKinsey makes a similar point in its work on strategy in energy and materials: a few high-quality signposts are generally more useful than a large collection of poorly defined ones, because excessive monitoring creates its own fog.

A signpost deserves a place in the register only if the strategy team can complete this sentence:

If this indicator moves materially, we may need to reconsider ___ because ___.

Otherwise it belongs in general market intelligence, not the scenario operating system.

## A signpost is not a trigger

This distinction is where scenario monitoring becomes decision management.

A signpost tells you what to watch.

A trigger tells you when the evidence has changed enough to require something from the organization.

The difference sounds small. Operationally, it is enormous.

Imagine a capital-intensive business considering a new production technology.

A scenario exercise establishes that investment becomes attractive if four conditions evolve favorably: the technology proves scalable, government policy supports the product, customers demonstrate sufficient demand, and low-cost feedstock becomes available.

Monitoring those variables is useful.

But the investment still has no decision rule.

McKinsey offers a concrete version of this problem using investment in gasification for biofuels. Potential signposts include technological development, government mandates, customer demand, and access to cellulosic feedstock. Potential trigger points become much more specific: a full-scale plant comes online; government requires a defined share of advanced biofuel; customers make explicit fleet commitments; or competitors begin acquiring feedstock aggregators.

The distinction is:

Signpost: technology is improving.

Trigger: a full-scale commercial plant has demonstrated the process.

Or:

Signpost: policy is becoming more supportive.

Trigger: a mandate with defined commercial consequences has been enacted.

That matters more in capital-intensive strategy because action has lead times.

If the company waits until the future is obvious, the relevant site may be unavailable. Permitting may take three years. Equipment suppliers may be committed to competitors. Feedstock may already have been contracted.

Scenario planning and real-options logic fit naturally together for this reason. Research on Shell's scenario practice has argued that scenario analysis can help identify future options, determine when exercising those options becomes appropriate, and assess irreversible investments under uncertainty.

The strategic choice is therefore rarely just act versus wait.

It can be:

invest fully;

run a pilot;

secure land;

begin permitting;

sign a contingent supply agreement;

take a minority position;

retain technical capability;

delay irreversible capex;

or abandon the option.

A good scenario system connects the evidence to that ladder of commitments.

## Pre-commit the next question before you pre-commit the action

The obvious danger is turning this into strategy by spreadsheet.

Not every uncertainty supports an elegant numerical threshold.

A geopolitical scenario might depend on the willingness of a government to impose restrictions. A customer-behavior scenario may hinge on changing attitudes that become visible through several imperfect signals. A technology scenario may involve expert disagreement about whether a technical breakthrough is real.

In those situations, “trigger = 47.5%” creates false precision.

The alternative is not vagueness.

It is to pre-commit the next question.

Suppose a company considering expansion in a policy-sensitive market has a scenario in which local regulation becomes materially more restrictive.

Its register might contain:

Assumption: Foreign operators will continue to have access to the current licensing regime.

Signposts: Changes in government rhetoric; draft legislation; staffing of the relevant regulator; enforcement against adjacent sectors; demands placed on local competitors.

Trigger for review: Two independent institutional developments indicate that the licensing framework, not merely political language, is moving toward restriction.

Next question: Does the emerging regime materially change our ability to operate the intended business model?

Low-regret action: Map contractual and operational exposures now.

Option to preserve: Maintain an alternative local-partner structure that could be activated if restrictions tighten.

The trigger does not automatically order market exit.

It orders serious reassessment.

This is consistent with the adaptive-policy literature. Raso and colleagues define a trigger as the signpost value at which adaptation becomes necessary, while also emphasizing the challenge of detecting real change in noisy and ambiguous observations.

In corporate settings, the adaptation may still require judgment.

The key is that the organization has agreed in advance that particular evidence cannot simply be absorbed into the old plan without review.

That protects strategy from a familiar failure mode: every new development is individually explainable, so no individual development is ever allowed to change the decision.

## The monitoring system should preserve surprise

A scenario operating system needs another capability that a simple dashboard lacks.

It must monitor what the scenario team expected and remain open to things it did not expect.

Schoemaker, Day, and Snyder's work on strategic radars offers a useful real-world example. They describe a system developed at the U.S. Defense Logistics Agency after a scenario exercise around global logistics and defense operations.

The DLA monitored predefined forces such as energy availability, defense spending, outsourcing, supply-chain technology, geopolitical risk, and policy changes, while also searching its network for unexpected weak signals. In one case, evidence of slowing commercial deployment of RFID and growing alternative wireless-sensor technologies caused the agency to reassess scenarios and priorities around a technology already deeply embedded in its logistics operations. Rising energy prices and possible constraints on energy use also became relevant to its operating model.

The process was systematic. Roughly 100 external signals were processed in each quarterly cycle and narrowed by experts to 20–30 considered most relevant. Updated evidence fed into the scenario weights and dashboards used to identify strategic or tactical decisions that might require revision.

But the case contains a valuable warning too.

The authors report that some signals were missed, including the rise of smartphones while BlackBerry still appeared dominant. They also stress that weak signals require interpretation rather than automatic processing.

That is exactly the boundary a corporate scenario system needs.

If the register tracks only variables explicitly imagined during the original workshop, the scenarios can become a new form of tunnel vision.

The operating model should therefore have two inputs:

Directed monitoring: What evidence is arriving against the assumptions we already identified?

Open scanning: What is happening that our scenario architecture did not anticipate at all?

The second input may require changing the scenarios themselves.

The future has no obligation to select one of the options from the workshop deck.

## Every scenario should produce low-regret actions and options

Tracking signposts becomes strategically relevant only when the organization can respond.

For each material uncertainty, scenario teams should distinguish two kinds of action.

A low-regret action makes sense across most plausible futures.

Examples can include improving data visibility, eliminating a single-source dependency, developing a scarce capability, clarifying decision rights, or obtaining a permit that is inexpensive relative to the flexibility it creates.

An option-preserving action makes a future move possible without committing fully today.

That may mean reserving land rather than building the factory.

Conducting engineering work rather than approving the full project.

Qualifying a second supplier without immediately shifting volume.

Running a market pilot rather than launching nationally.

Negotiating an extension clause rather than exercising it.

The distinction changes the economics of scenario planning.

A scenario exercise no longer needs to determine which world management should “bet on.”

It can instead ask:

Which actions are attractive across worlds, and which relatively inexpensive commitments prevent us from losing choices while we learn?

This is particularly important for capital-intensive strategies. Long lead times mean waiting for certainty can itself be a decision.

McKinsey's energy-and-materials work makes this explicit: a blanket wait-and-see strategy can destroy value if competitors secure infrastructure, feedstock, customers, or other control points while the company waits for the uncertain environment to resolve.

The scenario register should therefore record not just what management plans to do if a future arrives.

It should record what needs to be done before the future arrives so that management still has a choice.

## Run a quarterly trigger review, not a quarterly scenario presentation

For most organizations, the scenario stories themselves do not need rewriting every quarter.

The assumptions do.

A practical quarterly review can be run from a compact signpost register.

For each material uncertainty, record:

- Scenario assumption: What proposition about the external environment makes this scenario materially different?

- Signpost: What observable evidence would provide information about that assumption?

- Current evidence: What has changed since the last review, including evidence against the emerging view?

- Evidence owner: Who is accountable for maintaining the external evidence, not for defending the original scenario?

- Update cadence: How frequently can the signpost move meaningfully?

- Decision threshold: What condition requires the relevant decision to be reopened?

- Low-regret action: What should be done regardless of which scenario ultimately dominates?

- Option to preserve: Which future choice becomes costly or impossible if the organization does nothing now?

- Decision owner: Who has authority to act once the threshold is reached?

- Next review: When will the assumption be revisited if no trigger fires?

The meeting itself should begin with changes.

### Which signpost moved most?

### Which critical assumption has accumulated evidence but not changed our view?

### Has any decision threshold been crossed?

### Did we discover a signal that does not fit the existing scenarios?

Which option is becoming more expensive to preserve?

Which signpost has proved too noisy or too late to be useful?

This last question is important.

Monitoring systems need to be redesigned as evidence accumulates.

The adaptive-policy literature's emphasis on relevance, observability, completeness, and parsimony is useful precisely because a plausible indicator is not necessarily an informative one.

A quarterly review that repeatedly concludes “nothing changed” can still be valuable.

The test is whether management can explain why the observed developments remained below the threshold for action.

## Some uncertainties should not have triggers

The strongest boundary condition is that certain strategic uncertainties do not offer meaningful advance warning.

A leadership decision by a government can happen abruptly.

A competitor can announce an acquisition.

A scientific breakthrough can alter an industry's economics before the company's chosen indicator moves.

A court can issue a consequential judgment on a timetable known in advance but with an outcome that is difficult to forecast.

A signpost can also become visible too late to preserve the option that matters.

This is not merely a conceptual problem. Monitoring research under deep uncertainty distinguishes between indicators that provide earlier warning with greater ambiguity and indicators that give stronger confirmation but may arrive later. A monitoring system has to balance those properties rather than assume that every critical change will generate an ideal leading indicator.

For those uncertainties, scenario planning should produce preparedness, not a fake trigger.

The organization can clarify decision rights.

Construct contingencies.

Pre-negotiate contractual flexibility.

Maintain liquidity.

Run tabletop exercises.

Define what information would be needed immediately after the event.

McKinsey's 2026 geopolitical framework makes this distinction explicitly: horizon scanning, scenario planning, contingency planning, simulations, and tabletop exercises serve different purposes and should be used together rather than expecting one method to eliminate uncertainty.

The scenario-to-trigger system therefore needs an honest category:

## No reliable leading signpost.

That is useful information.

It tells management that resilience and option preservation matter more than prediction.

## Judge the scenario program by the decisions it changes

Organizations commonly evaluate scenario work using the wrong indicators.

Were senior executives engaged?

Were the scenarios sufficiently differentiated?

Was the workshop provocative?

Did participants find it useful?

Those questions matter to process quality.

They say little about whether the capability survives.

A more demanding review would ask:

What percentage of material scenario assumptions have a named evidence owner?

Which strategic decisions have explicit reconsideration thresholds?

How often has a trigger caused a real decision to be reopened?

Which options did the company preserve because of scenario analysis?

Which low-regret investments were made across multiple scenarios?

Which indicators were retired because they proved noisy or irrelevant?

How long does it take between material external evidence arriving and the responsible decision maker seeing its implication?

When a scenario became less plausible, did capital actually move?

These measures are not proof that the scenario program created financial value. Counterfactuals in corporate strategy are too difficult for that.

They do establish whether scenarios have entered the management system rather than remaining an intellectual exercise.

The Shell Russia research offers a useful standard here. Its central finding was not that Shell correctly forecast every consequential event; the study explicitly says it did not. The claimed contribution was that scenario work altered managers' beliefs and attention in ways that affected later strategic responses.

That is a more credible ambition for most companies.

The scenario does not need to tell management what will happen.

It needs to make management better prepared to notice when an important assumption is becoming wrong.

## Where Enquire fits: preserve the scenario after the workshop

This operating model creates a research-continuity problem.

The original scenario may be built from months of work: external data, customer research, regulatory analysis, expert conversations, technology assumptions, economic models, and executive judgments.

Then the workshop ends.

Six months later, somebody researching one signpost may have only the final slide deck and little understanding of why the assumption mattered or which evidence originally supported it.

Enquire's current Research Center is designed around preserving that kind of evolving context. Its product materials describe an Evolving Research Context in which information persists across successive inquiries, alongside structured exploration and synthesis, organization-level Research Centers, and expert input.

Its current positioning also describes the Research Center as a workspace for building, testing, and refining understanding as conditions change, combining structured AI research with expert perspective and institutional memory.

That can support a scenario-to-trigger process if the unit being preserved is not merely the scenario document.

The useful persistent object is the chain:

## scenario → assumption → evidence → signpost → threshold → question → decision.

Structured research can refresh public evidence around a signpost. Expert perspectives can help interpret ambiguous operating or regional signals. The Research Center can preserve why the indicator was selected, what the original evidence showed, and how interpretations change over successive reviews.

But a workspace cannot supply the missing operating discipline.

Management still has to choose the few signposts worth tracking.

Someone still needs to own each one.

Leadership must still agree what evidence will reopen a decision.

And the organization must still preserve an option before it disappears.

The technology can keep the scenario alive.

It cannot decide to act on it.

## The scenario is finished when the monitoring starts

The most visible output of scenario planning is usually produced at exactly the wrong moment.

The scenarios themselves are completed before the organization has learned which future is unfolding.

That means the workshop should be understood as the beginning of the operating process, not its conclusion.

Its real deliverables are less impressive than the scenario stories:

- A small number of assumptions.

- Observable signposts.

- Evidence owners.

- Trigger thresholds.

- Pre-agreed questions.

- Low-regret moves.

- Options worth preserving.

- A review cadence.

Those elements determine whether a future change eventually appears inside a management decision or merely inside the next strategy presentation.

The discipline also changes what a good scenario looks like.

The best scenario is not necessarily the most vivid.

It is the one that makes management capable of saying:

> If the world begins moving in this direction, here is what we expect to see first. Here is who will notice. Here is when we will reopen the decision. And here is the option we refuse to lose while we wait.

Without that, scenario planning may broaden thinking for an afternoon.

With it, uncertainty can become part of how the organization allocates resources every quarter.

## Sources and further reading

- Paul J. H. Schoemaker, “Scenario Planning: A Tool for Strategic Thinking,” Sloan Management Review researchgate.net

- Paul Schoemaker, George Day, and Scott Snyder, “Integrating Organizational Networks, Weak Signals, Strategic Radars and Scenario Planning,” Technological Forecasting & Social Change researchgate.net

- Luciano Raso et al., “How to Evaluate a Monitoring System for Adaptive Policies,” Climatic Change link.springer.com

- Riccardo Vecchiato, “Scenario Planning, Cognition, and Strategic Investment Decisions in a Turbulent Environment,” Long Range Planning sciencedirect.com

- Peter Cornelius, Alexander Van de Putte, and Mattia Romani, “Three Decades of Scenario Planning in Shell,” California Management Review cmr.berkeley.edu

- McKinsey, “How to Confront Uncertainty in Your Strategy” mckinsey.com

- McKinsey, “The Art, Science, and Technology of Geopolitical Scenario Planning” mckinsey.com

- McKinsey, “Strategies Can Create Value in Volatile Times” mckinsey.com

- Shell, “What Are Shell Scenarios?” shell.com

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### The AI Trade Is Becoming an Electricity Thesis — https://enquire.ai/blog/the-ai-trade-is-becoming-an-electricity-thesis

Author: Enquire Team · Published: 2026-07-30 · Topic: Public Equities

AI demand does not become revenue merely because customers want more compute. Increasingly, analysts need to understand whether power, grid access, permitting, flexibility, and geography allow that demand to become energized capacity,…

AI demand does not become revenue merely because customers want more compute. Increasingly, analysts need to understand whether power, grid access, permitting, flexibility, and geography allow that demand to become energized capacity, and at what cost.

For several years, the analytical boundary around the AI trade was relatively intuitive.

Start with model capability and application demand. Translate that into accelerator requirements. Follow foundry capacity, advanced packaging, high-bandwidth memory, networking, servers, and cloud capex. Estimate how quickly new compute can be installed and how efficiently it will be used.

That boundary is becoming a risk in its own right.

An AI accelerator does not generate inference revenue because it exists. A cloud provider does not monetize a data-center shell because construction is complete. A leased megawatt is not necessarily an energized megawatt. And a signed utility request is not the same as a credible energization date.

Electricity is moving from an operating input in AI models toward a causal variable in the investment thesis.

The International Energy Agency now expects global data-center electricity consumption to roughly double from about 485 TWh in 2025 to 950 TWh in 2030, with consumption at AI-focused facilities growing considerably faster than the overall data-center total. At the same time, its 2026 update says infrastructure and supply-chain bottlenecks are reducing the probability of the most aggressive near-term demand scenarios.

The United States makes the interaction especially visible. The IEA forecasts U.S. electricity consumption rising by close to 2% annually through 2030, more than twice the pace of the previous decade, with data centers accounting for about half of the increase. FERC, meanwhile, has ordered all six regional grid operators under its jurisdiction to justify or reform the rules governing how data centers and other large loads connect to the transmission system, including rules for flexible loads, co-location, and nearby generation.

That does not establish a deterministic “power shortage” thesis.

It establishes something more useful for investors:

## The conversion of AI demand into revenue increasingly runs through the electricity system.

The analytical task is to map that transmission company by company.

## The coverage boundary is now the analytical risk

Traditional technology coverage is optimized for technology supply chains.

Power markets are optimized around a different set of variables: generation adequacy, transmission topology, interconnection studies, local congestion, peak demand, reliability standards, rate structures, permitting, and regulatory cost allocation.

For an AI analyst, many of those variables can look several steps removed from earnings.

The mistake is assuming that distance means immateriality.

Consider a simplified chain:

AI application demand → compute demand → accelerator order → data- center installation → energization → utilization → billable compute.

A semiconductor analyst may concentrate on the first three links.

An electricity constraint acts farther down the chain. But depending on contract terms and where the bottleneck sits, it can feed back into equipment acceptance, deployment schedules, customer capex, cloud availability, depreciation, power costs, and future ordering.

The effect is rarely uniform.

A grid delay may postpone revenue for a data-center developer while leaving a chip supplier's near- term shipment unchanged.

It may cause a hyperscaler to redirect equipment to another geography.

It may increase demand for more efficient accelerators because compute per megawatt becomes more valuable.

It may support gas generation, batteries, transformers, or utility investment without reducing aggregate AI infrastructure spending.

Or it may do very little because the site already has firm power.

That is why “AI electricity demand is rising” is not yet an investment conclusion.

The relevant question is:

## Where, in this company's economic chain, does electricity become binding?

The IMF's 2026 scenario work makes essentially this macroeconomic distinction. It identifies energy and grid capacity alongside data-center infrastructure as physical constraints that can slow the diffusion of AI even if frontier model capability continues advancing rapidly. The IMF does not claim electricity will always be the binding constraint; its point is that technological capability and economic deployment are separate variables.

Equity research increasingly needs the same separation.

## Translate megawatts into company assumptions

The first discipline is to stop treating megawatts as an endpoint.

A headline that a company has secured or announced several gigawatts of capacity sounds substantial. But “gigawatt” can describe very different stages of economic reality.

Is the power operational?

Contracted but awaiting transmission upgrades?

Supported by a utility study but not yet a final service agreement?

Dependent on generation that has not been built?

Behind the meter?

Interruptible?

Available continuously or only under defined operating conditions?

Expected in 2027 or 2031?

The distinction already matters in official load forecasting.

PJM's 2026 long-term forecast tightened how it treats large-load requests: near-term projects require firmer commitments, while less-certain projects farther out are discounted. The resulting forecast was lower than the previous year's in the near term in part because of changes to how large-load adjustments were assessed.

For an analyst, this suggests a basic rule:

## Discount power pipelines the way you discount revenue pipelines.

Do not assign the same probability to a proposed 500 MW campus, a signed interconnection arrangement, infrastructure already under construction, and an energized facility.

Then connect the power status to the relevant financial variable.

For a cloud or hyperscale operator, the chain may run:

energization timing → available compute capacity → ability to serve backlog → revenue timing → utilization → depreciation and energy expense → cloud margin.

Alphabet's second-quarter 2026 results illustrate why this translation increasingly belongs in technology models. The company said Q2 capex reached $44.9 billion, raised its full-year 2026 capex guidance to $195–205 billion primarily because it was accelerating capacity delivery, and said growing technical infrastructure would create P&L pressure through depreciation and data-center operating costs including energy.

For an accelerator or equipment supplier, the path can be different:

energized customer capacity → deployment schedule → acceptance or installation cadence → mix of products favored by constrained power → future order timing.

For a data-center developer or operator:

connection probability → development timetable → leased versus energized capacity → occupancy and utilization → return on invested capital.

For a utility:

credible large-load additions → generation and network investment → regulatory treatment → customer contribution and cost allocation → rate-base growth and earnings.

For an equipment supplier to the power system:

new generation and transmission requirements → project sanction → transformer, switchgear, turbine, cable, storage, or power-electronics demand → backlog conversion.

The point is not to force every AI company into an electricity model.

It is to identify the specific financial assumption through which the electricity system can enter the thesis.

If that connection cannot be articulated, a megawatt statistic is probably context rather than a model input.

## Five signposts matter outside the tech stack

A useful electricity map for AI-exposed companies can be built around five variables.

## 1. Connection quality, not connection headlines

The first signpost is the maturity of the grid connection.

FERC's June 2026 intervention is important because large-load integration has become sufficiently consequential that the Commission is asking regional grid operators to revisit transmission-service applications, cost allocation, co-location, flexibility, and processes for nearby generation.

The analyst should therefore track more than “power secured.”

Ask:

What stage of the utility or grid process has been completed?

What upgrades are necessary?

Who bears their cost?

What is the firm energization date?

What contractual commitment supports the forecast?

Can the load be curtailed?

Does the site's economics depend on a regulatory reform that has not yet taken effect?

This is especially important because announced load can greatly exceed realized load.

Generation queues provide a useful cautionary analogue. Lawrence Berkeley National Laboratory's 2026 queue study found more than 2,060 GW of generation and storage capacity seeking U.S. grid connection at the end of 2025, yet only 13% of capacity entering queues from 2000 through 2020 had reached commercial operation by the end of 2025. Projects that did reach operation in 2025 had spent more than five years between their initial interconnection request and commercial operation in regions where the relevant data were available. The dataset covers generation rather than data-center load, so the percentages should not be transferred directly to AI projects. The lesson is narrower: queue volume and executable capacity are different quantities.

A model should reflect that difference.

## 2. Local adequacy and electricity economics

A terawatt-hour is a global statistic. AI infrastructure is built locally.

Power availability in northern Virginia, Texas, Ireland, France, or the Nordics is not interchangeable simply because all are connected to sophisticated electricity systems.

The IEA explicitly warns against assuming that large new loads have one universal price effect. In systems with tight supply-demand balances, additional load can trigger investment and increase costs; in systems with excess capacity, it can improve utilization of existing generation and networks and potentially lower average costs.

For investors, this means power prices need to be understood locationally and causally.

Is the relevant price an energy-market price, a regulated tariff, or a long-term power-purchase agreement?

Does the customer pay for network upgrades?

Is the business exposed to congestion?

Is firm, around-the-clock capacity the scarce product rather than annual energy?

Do higher local costs flow through to cloud customers, compress provider margins, or change where workloads are placed?

The answer can create a competitive advantage for one geography and a delay for another.

Ireland provides a useful European boundary case. Its utility regulator said data centers accounted for 22% of national electricity demand in 2024 and introduced a new connection policy that explicitly considers grid constraints, security of supply, and generation adequacy. New data-center connections must support their requested demand with generation and/or storage under the policy framework.

That does not imply Europe generally has an “Ireland problem.”

It demonstrates why an AI-capacity model that treats Europe as one electricity market can miss material local differences.

## 3. The build rate of the power system

The next signpost sits on the supply side.

If load is growing faster than existing infrastructure can accommodate, investors need to know what is being built to close the gap, and whether it is likely to arrive on time.

The U.S. Department of Energy's draft 2026 National Transmission Needs Study identifies data centers and other new large loads as contributors to a pressing need for additional transmission capacity.

The IEA similarly points to the tension between fast-moving data-center development and slower electricity infrastructure, and notes that rising AI rack density is testing supply chains for power electronics and transformers as well as generation and grid capacity.

This creates second-order investment questions.

A semiconductor model may need a transformer lead-time assumption without the semiconductor company ever buying a transformer.

A data-center model may depend on transmission construction controlled by a utility.

A utility thesis may depend on whether regulators permit investment costs to enter rate base.

A generation-equipment supplier can benefit from the same constraint that slows a hyperscaler's campus.

The electricity thesis is therefore not simply a bearish overlay on technology.

A bottleneck destroys value for some parts of the chain and transfers it toward others.

## 4. Flexibility and substitution

The most important counterweight to a deterministic power-shortage thesis is adaptation.

Data centers do not have to consume electricity in exactly the same way forever.

Some workloads may become more geographically mobile.

Training may tolerate more temporal flexibility than low-latency inference.

Batteries can alter peak demand.

On-site or nearby generation can reduce dependence on traditional grid timelines.

Companies can choose different markets.

Chips and models can become more energy efficient.

Pricing can encourage customers to shift workloads.

FERC's current large-load proceeding explicitly includes flexible transmission services, co-located load, behind-the-meter generation, and generation serving electrically proximate large customers. ENTSO-E's 2026 work likewise treats European data centers as potentially flexible grid participants rather than purely inflexible demand.

The supply response is already visible in corporate strategy.

Google has described a data-center energy portfolio spanning geothermal, advanced nuclear, and gas generation with carbon capture as it works to secure future power. OpenAI and SB Energy announced an integrated infrastructure partnership around a 1.2 GW Texas data-center project that includes plans for new generation to support the site's power needs.

These examples do not prove that every constraint can be bypassed economically or quickly.

They show why the investment question cannot be reduced to “Is grid power scarce?”

It must include:

What substitutions are technically available, how much do they cost, and how quickly can the company deploy them?

## 5. Geography and permitting

The final signpost is location itself.

AI workloads vary in their dependence on latency, data sovereignty, customers, connectivity, labor, water, and regulation. Those requirements determine how substitutable one prospective data-center market is for another.

A training cluster that can move across several states is exposed differently from an inference facility that must sit near a particular metropolitan market.

An enterprise workload subject to data-residency requirements has fewer options than a globally portable batch workload.

A proposed campus whose power requires a new transmission project is different from a site beside surplus generation.

The United States and Europe illustrate different architectures rather than a simple ranking.

U.S. federal regulators are actively redesigning rules around the interconnection of very large loads. Europe operates across national systems in which data-center connection policies and constraints can vary materially, even while ENTSO-E coordinates at the continental transmission level.

An AI analyst therefore needs a geographic layer in the capacity model:

Where is the planned compute, and what has to happen locally before it can turn on?

## Build scenarios around substitution and delay

The electricity thesis becomes most useful when treated as a set of scenarios rather than a single bottleneck forecast.

Consider three.

## Scenario 1: Power constrains timing more than demand

AI demand remains robust, but grid interconnection and power-system construction prevent some facilities from energizing on schedule.

The likely consequences differ by company.

Cloud backlog stays elevated.

Data-center activation shifts right.

Some accelerator deployment is delayed or redirected.

Cloud providers carry more infrastructure spending before full utilization.

Grid equipment and generation investment accelerate.

Utilities face rising load forecasts but also regulatory questions about who pays for system expansion.

This is the scenario in which the difference between ordered compute and productive compute matters most.

The IMF's 2026 work is broadly consistent with this type of pathway: infrastructure bottlenecks slow diffusion without requiring a collapse in frontier AI capability.

## Scenario 2: Capital routes around the grid

Demand remains strong and developers overcome constraints through new geography, dedicated generation, storage, flexible connection agreements, or infrastructure partnerships.

Aggregate AI capex remains high.

But the winners can change.

Regions with credible power paths gain share.

Developers capable of integrating energy and data-center construction become more valuable.

Gas, nuclear, renewables, storage, and grid equipment can all benefit depending on local conditions.

Traditional utility interconnection becomes one route among several rather than the sole route.

Shell's 2026 Energy Security Scenarios should not be treated as forecasts, but their range makes a useful analytical point: AI-intensive futures can be supported by materially different energy mixes depending on technology, geopolitics, security priorities, and policy.

The electricity thesis survives in this scenario, but principally as a capital-allocation and geography thesis, not a scarcity thesis.

## Scenario 3: Efficiency outruns the bottleneck

The third scenario is the one an electricity-heavy AI thesis must take most seriously.

Compute becomes substantially more efficient.

Models require less computation for equivalent capability.

Purpose-built accelerators improve performance per watt.

Inference economics improve.

Workloads are scheduled more intelligently.

Underutilized infrastructure is used more effectively.

Application demand may still grow, but electricity consumption rises more slowly than today's extrapolations imply.

The IEA's scenario work has long treated efficiency as a major uncertainty. Its 2025 Energy and AI analysis found that a high-efficiency case produced data-center electricity demand in 2035 roughly 20% below its base case. Its 2026 update continues to emphasize uncertainty from efficiency, AI economics, supply constraints, and the speed with which bottlenecks are removed.

This possibility is not peripheral.

If compute per watt improves faster than economically useful AI demand expands, some of today's proposed power infrastructure could turn out to be excessive.

That would reverse parts of the causal map.

## A causal indicator map for the investment model

The practical discipline is to connect every energy indicator to a modeled investment assumption.

For each AI-exposed company, identify five things.

Power assumption: What amount of energized capacity does the current model implicitly require?

Do not begin with industry TWh. Begin with the company's own deployment path.

Transmission mechanism: How does a change in that capacity affect the company's economics?

For example:

energization delayed six months → fewer available accelerators → slower cloud revenue recognition.

Or:

local power price rises → higher data-center opex → lower incremental cloud gross margin unless passed through.

Or: grid constraints accelerate utility capital programs → higher equipment orders subject to regulatory approval and manufacturing lead times.

Observable indicator: Which external evidence sits closest to the causal mechanism?

Possibilities include executed utility agreements, firm versus speculative load forecasts, transmission construction milestones, generation procurement, grid-equipment backlogs, permitting decisions, local capacity prices, or new connection tariffs.

Evidence threshold: How much does that indicator need to move before the financial model changes?

“Grid constraints are worsening” is not sufficient.

A defensible threshold might look like:

Two major planned campuses representing more than 15% of modeled 2028 compute capacity move energization beyond the revenue forecast period.

Or:

Contracted electricity cost for new capacity rises enough to reduce modeled incremental gross margin by 150 basis points if unpassed through.

Or:

A new flexible-connection structure allows 1 GW of previously delayed capacity to energize a year earlier, changing the deployment assumption.

Review date: When will the evidence next become decision-relevant?

Power-system information moves on different clocks from quarterly semiconductor results. Utility planning, regulatory decisions, interconnection agreements, and transmission construction need their own monitoring cadence.

That is the map.

It prevents the analyst from turning every electricity headline into an AI estimate change while making it harder to ignore the power developments that genuinely matter.

## Watch the denominator: AI is not the only load on the grid

Another reason to avoid simplistic causal claims is that AI does not operate in an empty electricity system.

Electric vehicles, heat pumps, industrial reshoring, semiconductor manufacturing, cooling, electrification, and ordinary economic activity are also changing demand.

The IEA expects global electricity consumption to grow by an average 3.6% annually between 2026 and 2030, with data centers only one contributor among several. Even in the United States, where data centers are expected to account for about half of incremental demand, other buildings, industry, and transport still matter.

For investors, attribution matters.

A utility's load growth is not automatically an AI exposure.

A transformer backlog is not necessarily caused primarily by data centers.

An increase in power prices cannot be attributed to AI without considering weather, fuel prices, generation retirements, transmission constraints, industrial load, and the broader market design.

The causal map should therefore contain a denominator:

### How much of the relevant power-system change is actually associated with the AI capacity in the company model?

Otherwise the electricity thesis becomes a thematic story that explains everything after the fact.

### The trade is becoming cross-sector before it becomes purely electrical

Power is also not the only external constraint.

The AI buildout still depends on semiconductors, memory, networking, cooling, construction labor, financing, data availability, trade policy, application demand, and the willingness of customers to pay for AI services.

WTO data underline the scale of the technology investment cycle: its current trade outlook says unexpectedly strong 2025 merchandise trade was supported by surging trade in AI-enabling products, while warning that 2026 trade growth remains exposed to geopolitical and energy-price risks.

That is an important boundary for the thesis.

Electricity should not replace semiconductor analysis.

It should extend it.

The analytical advantage comes from knowing where the company sits across multiple interacting bottlenecks.

A GPU supplier may remain constrained by advanced packaging even in a region with abundant electricity.

A fully energized data center may remain underutilized if AI demand disappoints.

A hyperscaler may have abundant accelerators and demand but struggle to energize the building.

A utility may prepare for extraordinary load growth only to discover that developers submitted overlapping projects in several jurisdictions and ultimately build only one.

The investor's job is not to nominate the single “real” bottleneck.

It is to identify which constraint is most likely to bind for this company, in this period, under this demand scenario.

## What would falsify the electricity thesis?

A useful thesis needs evidence that would weaken it.

At least five developments should cause analysts to reduce the weight placed on power constraints.

## First, AI efficiency consistently outruns workload growth.

If equivalent AI output requires dramatically less energy, and demand does not expand enough to consume those efficiency gains, electricity becomes less important at the margin.

## Second, announced power projects repeatedly exceed realized AI demand.

The industry may be double- or triple-counting prospective data centers across competing locations. PJM's decision to distinguish firmer from less-firm large-load requests is one institutional response to precisely this forecasting problem.

## Third, flexible load becomes commercially routine.

If training and other workloads can shift geographically or temporally without significant performance or economic penalties, existing grid capacity becomes more usable and connection constraints become less binary. Both FERC and ENTSO-E are actively examining this possibility.

## Fourth, behind-the-meter and dedicated power scale faster than grid delays.

If integrated energy-and-data-center development reliably produces capacity faster and economically, the grid may stop being the principal gating mechanism in the most important markets.

## Fifth, AI application economics weaken.

This is the largest falsifier because electricity demand is downstream of economic demand for compute. The IEA itself notes that future data-center electricity consumption is sensitive to market expectations about returns on AI and data-center investment as well as macroeconomic and financing conditions.

Those conditions are why the right statement is not:

AI is power-constrained.

It is:

> The probability distribution for AI deployment increasingly depends on power-system variables that many technology models still treat as exogenous.

That is a much more durable investment proposition.

## Where Enquire fits: research across the coverage boundary

The practical challenge is organizational.

Technology analysts understand accelerators, cloud architecture, and model economics. Utility analysts understand generation, grids, tariffs, and regulatory mechanisms. Infrastructure specialists understand construction and financing. Policy analysts understand permitting and regional implementation.

The AI thesis now crosses all four.

Enquire's current capital-markets offering is explicitly designed for questions that extend beyond an analyst's formal coverage, including adjacent value chains, emerging themes, and second- and third- order risks. It combines public information, structured AI research, and expert perspective while preserving context as an investment debate evolves.

For this use case, the value is not producing another forecast of global data-center electricity consumption.

It is connecting the forecast to operating evidence.

A semiconductor analyst can maintain the core technology thesis while adding structured research on the utility systems serving major deployment regions. A utility or data-center operator can test whether the assumed energization schedule is realistic. Power-market specialists can explain which

congestion, flexibility, or cost-allocation issues are actually material in a particular jurisdiction. Semiconductor-supply-chain experts can test whether higher efficiency meaningfully changes the demand for power rather than simply enabling more compute.

Enquire's current product materials describe structured AI research that identifies gaps, direct input from vetted specialists, and an evolving research context that preserves prior inquiries as new evidence arrives.

That structure is useful precisely because this is a cross-coverage problem.

Expert opinion should not substitute for system-operator data, company filings, or regulatory records. But it can help determine which causal assumptions deserve further investigation, and where a neat top-down model is colliding with operating reality.

## The relevant unit is not electricity demand. It is deployable compute.

The AI power debate can easily become a competition in enormous numbers.

Gigawatts announced.

Terawatt-hours forecast.

Billions of dollars of grid investment.

Nuclear reactors proposed.

Gas plants ordered.

Those numbers establish scale. They do not tell an investor what to change in a model.

The more useful analytical unit is deployable compute.

How much compute can actually be installed, energized, cooled, connected, and economically utilized in the geography and period assumed?

What limits that quantity?

What does the constraint cost?

Who pays?

How quickly can it be substituted?

And which financial line changes when the answer moves?

For the technology analyst, that may mean adding interconnection milestones beside accelerator supply.

For the cloud analyst, it may mean separating server capex from energization and utilization.

For the utility analyst, it means distinguishing serious large-load commitments from speculative requests.

For the infrastructure analyst, it means understanding which shortages produce pricing power and which simply delay projects.

The AI trade is not becoming an electricity trade because chips and software have stopped mattering.

It is becoming an electricity thesis because the economic value of those chips increasingly depends on whether the physical system around them can turn demand for compute into operating compute.

The hardest question for an AI-exposed model is therefore moving one step outside the traditional stack:

What power-system assumption is embedded in our earnings forecast, and what evidence would make us change it?

## Sources and further reading

- International Energy Agency, Electricity 2026 iea.org

- International Energy Agency, Key Questions on Energy and AI iea.org

- International Monetary Fund, Global Economic and Financial Implications of Artificial Intelligence elibrary.imf.org

- Federal Energy Regulatory Commission, large-load interconnection actions ferc.gov

- Lawrence Berkeley National Laboratory, Queued Up: 2026 Edition emp.lbl.gov

- ENTSO-E, “Data centres and the power system” entsoe.eu

- Commission for Regulation of Utilities, Ireland data-centre connection policy cru.ie

- Alphabet, Q2 2026 earnings call abc.xyz

- Shell, 2026 Energy Security Scenarios shell.com

- Enquire, Capital Markets enquire.ai

### Policy Signal or Political Noise? — https://enquire.ai/blog/policy-signal-or-political-noise

Author: Enquire Team · Published: 2026-07-09 · Topic: Consulting

The value of an early policy signal is not that it arrives before everyone else. It is that the signal can be connected to a plausible mechanism, independently corroborated, and translated into a decision threshold. Without those tests,…

The value of an early policy signal is not that it arrives before everyone else. It is that the signal can be connected to a plausible mechanism, independently corroborated, and translated into a decision threshold. Without those tests, monitoring systems can reward novelty and anxiety rather than foresight.

A minister makes an unexpected comment at a conference. A regulator recruits several specialists in a previously quiet area. A parliamentary committee asks unusually pointed questions. An industry association changes its language. A draft circulating among stakeholders appears to move policy in a new direction.

By the end of the day, a policy-intelligence team has added another item to the watchlist.

The difficult question comes next:

## So what?

Calling something a “weak signal” can make it sound important before anyone has established why it should matter.

Some early observations do precede major policy changes. Others are speeches that go nowhere, consultation options that are discarded, bureaucratic experiments that remain marginal, trial balloons designed principally to test political reaction, or rumors repeatedly reported by different outlets that ultimately trace back to one source.

A good early-warning system cannot eliminate that ambiguity.

It can impose a discipline on what happens next.

The central mistake is treating earliness as evidence quality. A weak signal is useful only when analysts can connect an observation to a credible mechanism of policy change, corroborate that mechanism through genuinely independent evidence, specify which existing assumption it challenges, and define how large a change in belief would justify action.

The practical output should be a signal-to-decision ladder:

observation → mechanism → independent corroboration → affected assumption → probability update → threshold → action → review date.

The ladder deliberately makes escalation harder. That is the point. Policy-intelligence teams do not create value by seeing the largest number of unusual things first. They create value by identifying the relatively small number of unusual things that should cause the organization to think or act differently.

## Early is not the same as informative

Strategic-foresight research takes weak signals seriously precisely because consequential change can begin in ambiguous, fragmented observations.

But serious foresight practice also recognizes how much filtering is required.

A 2026 Sustainability Science study led by Jason Jabbour used a global Delphi exercise involving 790 respondents across 132 countries to generate 1,200 horizon-scanning observations. Researchers reduced those to 280 candidate weak signals and ultimately to 20 priorities using additional coding, scenario stress-testing, judgments about likelihood and impact, expert review, and analysis of relationships among signals. The authors acknowledge that expert elicitation remains subjective and that their resulting influence map is a static representation of systems that change over time.

The useful lesson for a corporate policy team is not the study's particular list of signals.

It is the compression ratio.

A disciplined foresight process did not treat 1,200 interesting observations as 1,200 matters requiring senior attention. It introduced multiple stages between something new has happened and this deserves priority. The study also used separate analyst teams, diverse participants, anonymized early inputs, and structured challenge to reduce anchoring and conformity risks.

Government foresight practice makes a similar distinction. The UK's updated Futures Toolkit defines horizon scanning as systematic collection of emerging trends and weak signals, but then treats scanning as an input into further driver analysis, scenario work, and policy testing. It explicitly warns that narrow expert pools can produce groupthink and that scanning too narrowly can cause important changes to be missed.

This is the first design principle:

## Scanning should maximize discovery. Escalation should not.

If the same threshold governs both activities, one of two failures follows.

Set the bar high and analysts screen out precisely the strange, immature evidence that foresight is supposed to detect.

Set it low and leadership receives a constant stream of supposedly consequential developments whose main qualification is that they are unusual.

The system needs a wide funnel and a narrow escalation gate.

## A signal needs a mechanism

The first question after observing something unusual should not be, “How important is this?”

It should be:

Through what mechanism could this observation actually change policy?

Consider several hypothetical observations:

A minister says an industry has become “a priority.”

A regulatory agency creates a specialist enforcement unit.

A governing party inserts a proposal into an election platform.

A ministry requests data from companies.

A legislature allocates budget to an implementing agency.

A regulator begins consulting on a specific compliance mechanism required by legislation.

All can plausibly be called signals.

They are not equally informative.

The difference is not rhetorical intensity. It is the number and strength of the causal steps between the observation and the business outcome.

A useful mechanism map asks:

Who must act next?

Do they possess legal authority?

Is another institution's agreement necessary?

Does funding exist?

Is there an administrative process through which the proposal would normally travel?

Who benefits from moving it forward, and who can stop it?

What implementing capability would have to be built?

How would a business eventually experience the change, as a legal obligation, enforcement practice, procurement requirement, licensing decision, trade restriction, tax change, or counterparty demand?

This prevents a common monitoring error: treating political prominence as policy probability.

An issue can dominate public discussion while facing severe institutional constraints.

A comparatively obscure technical development can be much more consequential because the implementation pathway is already intact.

The European Union's recent implementation of AI transparency rules provides a useful illustration.

When the European Commission opened a September 2025 consultation on guidelines and a code of practice for Article 50 of the AI Act, that was not merely another discussion about AI regulation. The statutory obligation already existed, the Commission identified the concrete implementation instruments it intended to develop, named the affected stakeholder groups, and linked the process to transparency obligations scheduled to apply from August 2, 2026.

By July 2026, the Commission had adopted detailed guidelines for those Article 50 obligations, clarifying their scope shortly before application began.

The September consultation did not tell a company exactly what the final guidance would say.

But it was decision-relevant early information because it sat on a visible causal chain:

law → named implementation process → participating institutions → defined output → known compliance date.

Contrast that with a political statement for which none of those links can yet be identified.

Both deserve monitoring.

Only one may deserve immediate escalation.

## Corroborate the mechanism, not the headline

The next rung is independent corroboration.

This is harder than it sounds because modern information systems manufacture apparent confirmation extremely quickly.

Five news stories may be one source.

Three industry experts may have learned the issue from the same trade-association briefing.

Several political contacts may all be interpreting one speech.

Social-media repetition can make an interpretation appear widely established while adding no new evidence.

The practical question is therefore not:

How many sources are saying this?

It is:

How many independent information-generating processes support the mechanism?

Suppose a policy team believes a government is preparing to tighten oversight of a sector.

Useful corroboration could come from different classes of evidence:

A minister changes rhetoric.

The responsible agency changes staffing.

A budget allocates new resources.

A consultation asks detailed questions consistent with the proposed direction.

A regulator requests previously uncollected data.

Multiple affected industries independently report similar supervisory questions.

A legislative committee begins developing the necessary statutory authority.

Those observations do not merely repeat one another. They illuminate different parts of the policy mechanism.

Independence matters because convergence itself can be misleading.

In an experiment involving 144 participants performing estimation tasks, Lorenz and colleagues found that even relatively mild exposure to others' estimates reduced diversity without a corresponding improvement in collective accuracy; participants could also become more confident as judgments converged. The experiment was not about government affairs, so it should not be treated as direct evidence about policy intelligence. Its narrower lesson is relevant: <mark>agreement produced after information has circulated is not equivalent to independent confirmation.</mark>

For policy teams, source maps should therefore track provenance.

An analyst should be able to distinguish:

Four independent sources point toward the same mechanism.

from:

Four sources repeat the same originating claim.

Those sentences carry radically different evidentiary weight.

## Make the signal attack an existing assumption

Even a well-corroborated development does not automatically deserve escalation.

It must matter to something the organization currently believes.

Imagine a multinational maintains the assumption:

Material restrictions on technology X in Country A are unlikely before 2028.

An analyst notices new parliamentary rhetoric.

The rhetoric alone may not change the assumption.

Then the relevant ministry creates a technical working group.

Probability moves slightly.

The annual budget adds resources to the supervising agency.

Probability moves again.

A consultation asks how firms could implement a particular restriction.

Now the original assumption may deserve formal reconsideration.

This sounds obvious, but many monitoring systems store events rather than beliefs.

They contain thousands of developments and no explicit record of which corporate assumption each one is capable of changing.

The result is alert accumulation.

Analysts become extremely informed about what happened while management still lacks a clear answer to what it should believe differently.

A signal-to-decision system reverses the relationship.

Begin with the assumption.

Examples might include:

A proposed obligation will not become effective within the planning horizon.

A national regulator is unlikely to enforce a provision aggressively during its first year.

A market will remain accessible under existing trade rules.

A planned investment is unlikely to require additional licensing.

A public-procurement framework will remain open to a particular technology.

The relevant weak signal is then something capable of altering one of those beliefs.

This also makes prioritization easier. An unusual development that does not touch a consequential assumption can remain in the research layer without demanding executive attention.

## Novelty is not impact.

## Update probabilities before updating posture

Once a signal has survived the first four rungs, the team should say explicitly how much it changes the view.

Not “risk is increasing.”

Not “we are watching closely.”

Not “this feels significant.”

A direction and magnitude are more useful.

If the organization previously assessed a policy outcome as roughly a 20% possibility during the planning period, does the new evidence move that assessment to 25%, 40%, or 70%?

The number will not be objectively correct. That is not the standard.

Its value comes from forcing analysts to distinguish information from reaction.

Research on geopolitical forecasting suggests that disciplined updating need not mean dramatic updating. Atanasov and colleagues analyzed more than 400,000 probability forecasts covering almost 500 geopolitical questions over four years. More accurate forecasters tended to update more frequently in relatively small increments, while weaker forecasters were more likely either to reaffirm previous beliefs or make occasional large revisions. The results are associations from a forecasting tournament, not a rule that policy teams should change every estimate by a fixed amount. But they show that good updating can occupy the space between stubbornness and alarmism.

Tetlock and colleagues' forecasting-tournament work provides another reason for making beliefs explicit: probabilistic predictions create records that can later be scored against outcomes, reducing the rhetorical freedom to claim that a vague warning was “basically right.” Their geopolitical

tournaments evaluated forecasts on defined questions and rewarded movement toward the outcomes that ultimately occurred without forgiving extreme false positives merely because a risk had once seemed imaginable.

A policy-intelligence function need not become a prediction tournament.

But it should preserve the same accountability principle:

What did we believe before the signal? What do we believe after it?

If the answer is unchanged, there may be no reason to escalate.

## Build the signal-to-decision ladder

A practical monitoring record can be compact.

For each potentially consequential signal, maintain eight fields.

## 1. Observation

What actually happened?

Strip interpretation out of this field.

Not “the government is becoming hostile to foreign platforms.”

Instead:

The ministry's consultation asks whether foreign platforms should be required to maintain a locally accountable legal representative.

The distinction matters because the mechanism should not be smuggled into the observation.

## 2. Mechanism

What causal pathway could connect this development to a business outcome?

Name the institutions, authorities, dependencies, and intermediate steps.

If the mechanism cannot yet be articulated, the correct status may simply be watch.

## 3. Independent corroboration

What evidence generated independently of the original observation supports or contradicts the mechanism?

Record provenance, not just source count.

A budget line and an agency hire provide different information from two articles describing the same rumor.

## 4. Affected assumption

Which existing company belief does the signal challenge?

No material assumption, no automatic escalation.

This field converts monitoring from an external-news process into a decision-support process.

## 5. Probability update

What did the team believe before, and what does it believe now?

Ranges are fine when precision is artificial.

“From 20–30% to 40–50%” is considerably more informative than “risk elevated.”

## 6. Threshold

At what belief level, or combination of signals, does a particular decision need reconsideration?

McKinsey's current work on geopolitical scenario planning similarly recommends defining trigger points, dashboards, and monitoring processes that determine when scenarios should be refreshed as conditions change.

## 7. Action

Escalation is only one possible response.

Others include commissioning targeted research, seeking legal analysis, adjusting a scenario, beginning contingency preparation, changing advocacy priorities, briefing management, or deliberately doing nothing yet.

The action should be proportional to the threshold crossed.

## 8. Review date

Signals decay.

Political statements are superseded. Bills stall. Personnel change. Consultations close. New evidence arrives.

A signal that has not advanced should eventually be downgraded rather than remaining permanently “amber.”

## Institutions often tell you more than rhetoric

The ladder becomes particularly useful when policy rhetoric is abundant. The United States' 2025 federal deregulatory push offers a second, structurally different example from the EU process. A general political commitment to deregulation would tell an affected company something about direction but relatively little about exactly how agency behavior would change. The information content increased substantially when the administration issued Executive Order 14192 in January 2025. The order instructed agencies, subject to legal limits, to identify at least ten existing regulations for elimination for each new regulation and created a regulatory-cost budgeting mechanism overseen by the Office of Management and Budget. It increased again when OMB issued implementation guidance in March explaining how agencies should apply those requirements and connecting subsequent planning to the Unified Agenda process. The point here is not whether deregulation was desirable, how any particular rule should have been treated, or whether every downstream change was predictable. It is the hierarchy of signals.

## Rhetoric supplied direction.

## The executive order supplied authority and instructions.

## OMB guidance supplied operating mechanics.

For a regulated company trying to anticipate agency behavior before every specific rulemaking became visible, those developments deserved different weights. That principle travels across policy systems. The analyst should ask not simply whether senior politicians are talking about an issue, but whether the institutional machinery required to produce the relevant outcome is beginning to move.

## Protect the system from novelty and echo effects

Once teams are rewarded for early warning, incentives can become dangerous.

The analyst who finds an exotic emerging risk looks prescient.

The analyst who says, “This is interesting but probably does not change anything,” looks less valuable.

Over time, the system can become biased toward escalation.

The metric quietly changes from decision usefulness to novelty discovered.

Foresight practice itself recognizes the danger. The Jabbour study used broad geographic participation, independent coding, structured expert debate, and explicit bias-mitigation measures rather than assuming that an unusual observation was informative merely because it was unconventional. The UK's government futures guidance likewise combines horizon scanning with subsequent sense-making and strategy-testing instead of treating raw weak signals as decisions.

Corporate teams can introduce several safeguards without building a bureaucracy.

First, separate scanner from escalator where resources permit. The person rewarded for finding novelty should not be the only person judging its decision significance.

Second, preserve the original source before distributing interpretations. Analysts should be able to see whether multiple internal alerts are actually based on one external event.

Third, record disconfirming evidence beside corroboration. A signal assessment containing only evidence for escalation is an advocacy document.

Fourth, audit false alarms. If every issue that once appeared “high risk” silently disappears from the dashboard, the team cannot learn whether its thresholds are sensible.

Fifth, distinguish interesting from actionable as explicit statuses.

There is no embarrassment in labeling a development interesting.

The embarrassment should come from pretending interest and evidence are the same thing.

## Know when not to escalate

The most valuable output from a monitoring system is sometimes:

## No posture change.

That can be a substantive conclusion.

A senior politician's statement may be striking but inconsistent with the governing coalition's agreement.

A consultation may list an aggressive option that the relevant authority lacks power to implement.

A staffing change may look important but involve a unit with no jurisdiction over the company's activity.

Three articles may appear to confirm a rumor but all trace to one anonymous official.

A proposal may become more probable while remaining far below the threshold at which the company should incur preparation costs.

A good policy-intelligence team should be able to explain those conclusions with the same confidence it uses to recommend escalation.

This is consistent with the broader logic of risk-based regulation itself. The OECD's 2025 Regulatory Policy Outlook emphasizes using evidence and risk analysis to prioritize scarce attention rather than treating every potential compliance problem alike; it also stresses continuous monitoring as risks and real-world conditions evolve.

Corporate policy functions face a parallel allocation problem.

Their scarce resource is not regulatory authority.

It is organizational attention.

Every escalation competes with another issue for executive time, research capacity, legal resources, scenario preparation, lobbying effort, and management concern.

False positives therefore have a cost.

So do false negatives.

The right threshold depends on that asymmetry.

For an easily reversible decision, waiting for stronger evidence may be rational.

For an exposure where mitigation takes eighteen months and the downside is severe, even a modest probability increase may justify low-cost preparation.

The signal cannot determine that by itself.

Only the signal plus the decision context can.

## Some shocks will still arrive without useful warning

No signal framework should promise that major political developments will always advertise themselves.

They will not.

Some events are deliberately concealed. Some depend on individual decisions made rapidly. Elections, wars, court judgments, leadership changes, market crises, and political scandals can alter policy trajectories faster than a corporate monitoring system can construct a robust signal chain.

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Even foresight research that advocates weak-signal monitoring acknowledges limitations in subjective judgment and in using static models to understand changing systems.

That boundary matters because otherwise structured monitoring creates its own illusion:

If we build a sufficiently sophisticated system, surprise becomes a process failure.

It does not.

The purpose of the ladder is more modest.

For developments that do emit observable precursors, it improves the discipline with which they are interpreted.

For developments that may occur abruptly, the appropriate tool is often scenario and contingency planning rather than a forced search for predictive signals. McKinsey's 2026 geopolitical-foresight framework explicitly treats horizon scanning, scenario planning, contingency planning, simulations, and tabletop exercises as different tools for different questions rather than as substitutes for one another.

A mature policy-intelligence function knows when it is monitoring an emerging pathway and when it is preparing for irreducible uncertainty.

## Where Enquire fits: accumulate signals without collapsing them prematurely

A signal-to-decision system creates a research-management problem.

Weak observations accumulate over time and across jurisdictions. The mechanism that looks implausible in January may become credible after a staffing decision in March, a budget allocation in May, and reports from regulated operators in June.

If each inquiry is treated independently, that evolution is difficult to see.

Enquire's current positioning is relevant to that continuity problem. Its Research Center is described as a workspace for building and refining understanding as conditions evolve, with structured AI research intended to synthesize signals and identify gaps, multiple expert perspectives that can expose agreement and disagreement, and a “Living Research Context” that carries prior inquiry forward rather than resetting it.

For a policy-intelligence team, the useful application would be to preserve the ladder around a live assumption.

Official sources can establish the observable policy facts. Structured research can connect those facts across time. Region- or sector-specific experts can help test the proposed mechanism: whether an agency has the capacity implied by a new mandate, whether stakeholders interpret a consultation in the same way, or whether apparent change in one jurisdiction is actually typical administrative behavior there.

Those expert views should remain evidence about operating context, not substitutes for official text or authoritative legal interpretation.

And disagreement should be retained.

If three informed regional perspectives contradict one another, the correct output may be higher uncertainty, not an averaged consensus.

The value of a persistent research context is therefore not that it predicts what government will do.

It is that a team can see the evidence accumulate against an assumption and decide more deliberately when the weight has become sufficient to act.

## Foresight should make escalation rarer and better

An effective policy-intelligence operation will still collect more signals than it escalates.

That is a sign of quality, not failure.

The scanning layer should remain curious about strange developments, minority perspectives, fringe proposals, institutional experiments, and changes that do not yet fit the established model.

But curiosity is only the entrance criterion.

Before the observation reaches senior leadership, it should face harder questions:

What exactly did we observe?

What mechanism could turn it into policy?

What independent evidence supports that mechanism?

Which assumption does it change?

By how much should our probability move?

What threshold does that cross?

What action follows?

When do we reassess?

That sequence changes the culture of early warning.

Analysts no longer compete simply to be first.

They compete to demonstrate why early information deserves to influence a decision.

And executives receive fewer warnings whose principal message is that the world is uncertain.

They receive something much more useful:

an explicit account of what changed, why it might matter, how much the organization's view should move, and what, if anything, it should do now.

## Sources and further reading

- Jabbour et al., “Navigating the winds of change: strategic foresight and the power of weak signals,” Sustainability Science link.springer.com

- Philip Tetlock et al., “Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate” faculty.wharton.upenn.edu

- Pavel Atanasov et al., “Small steps to accuracy: Incremental belief updaters are better forecasters” sciencedirect.com

- Jan Lorenz et al., “How Social Influence Can Undermine the Wisdom of Crowd Effect,” PNAS pnas.org

- UK Government Office for Science, The Futures Toolkit gov.uk

- OECD, Regulatory Policy Outlook 2025: Regulating for effectiveness oecd.org

- McKinsey, “The art, science, and technology of geopolitical scenario planning” mckinsey.com

- European Commission, consultation on AI Act transparency guidelines and code of practice digital-strategy.ec.europa.eu

- U.S. Executive Order 14192, “Unleashing Prosperity Through Deregulation” whitehouse.gov

- Enquire, current research-platform overview enquire.ai

### How Many Expert Interviews Are Enough? — https://enquire.ai/blog/how-many-expert-interviews-are-enough

Author: Enquire Team · Published: 2026-06-18 · Topic: Consulting

There is no defensible universal interview target. The right sample depends on what the team needs to learn, how heterogeneous the market is, how much depth the decision requires, and whether the research has seriously searched for…

There is no defensible universal interview target. The right sample depends on what the team needs to learn, how heterogeneous the market is, how much depth the decision requires, and whether the research has seriously searched for evidence that could overturn the emerging view.

A commercial-diligence team is three days from investment committee.

It has completed eight expert interviews. Seven respondents broadly support the market-growth thesis. The eighth is an outlier. The engagement manager wants four more calls. The partner asks the obvious question:

“Why four?”

Answers to that question are often less rigorous than the research itself.

Twelve is standard. Ten feels thin. Twenty should be enough. We usually do five per geography. The themes are starting to repeat.

Each answer offers the comfort of a number without establishing what the number is supposed to accomplish.

That is a problem because expert interviews can play very different roles in an engagement. A team may be trying to identify the major forces shaping a market, understand why customers behave as they do, test a specific causal explanation, discover exceptions to an apparent rule, estimate an uncertain quantity, or determine whether a finding travels across customer segments and geographies.

Those objectives do not require the same evidence.

Qualitative-methods research makes this point more sharply than consulting practice often does. In a study of 25 in-depth interviews, Hennink, Kaiser, and Marconi distinguished between code saturation, having identified the range of relevant issues, and meaning saturation, having developed a richer understanding of what those issues mean and how they operate. In their dataset, code saturation occurred after nine interviews; meaning saturation required 16 to 24.

That is not a prescription to conduct 24 calls.

It is a warning that “we are hearing the same themes” and “we understand the mechanism well enough to advise the client” are different claims.

For consulting teams working under severe time constraints, the right question is therefore not how many interviews constitute rigor.

It is:

What does this decision require the interviews to establish, and what observable condition would tell us that another interview is unlikely to change the answer?

## The false precision of an interview target

The idea that there is a generally correct number of qualitative interviews persists partly because methodological studies have produced memorable numbers.

Guest, Bunce, and Johnson analyzed 60 interviews with female sex workers in Ghana and Nigeria and found that, in their particular study, most thematic saturation occurred within the first 12 interviews, with many high-level themes visible earlier. After 12 interviews, 92% of the codes ultimately identified in the 30 Ghana interviews had emerged.

The headline number has traveled much further than the qualification.

The authors explicitly cautioned against assuming that six to 12 interviews would always suffice. Their population was relatively homogeneous and their research objectives narrow. They noted that heterogeneous populations, diffuse questions, lower-quality data, and objectives involving variation between groups require more sampling.

A later systematic review by Hennink and Kaiser similarly found relatively modest interview counts could reach saturation in empirical studies, particularly when populations were homogeneous and study objectives narrowly defined. The authors' conclusion was not that researchers had discovered a universal number, but that sample-size guidance has to be interpreted alongside the characteristics of the study.

This matters enormously for consulting.

Twelve procurement managers buying the same product in one country may produce substantial repetition.

Twelve respondents split among four countries, three customer segments, two channel models, and regulators may represent almost no replication within any relevant subgroup.

The denominator is therefore not simply “interviews.”

It is interviews within the parts of the problem where the recommendation depends on variation.

A global study with 30 calls can be empirically thinner than a tightly framed study with eight.

## First decide what the interviews are allowed to prove

Before determining the sample, the team should define the evidentiary job of the interviews.

This is where many diligence programs become confused.

Suppose eight of ten distributors say a competitor's product is gaining traction.

That observation can support a legitimate statement:

Most of the distributors we interviewed described increasing competitive traction.

It does not automatically support:

80% of distributors in the market believe the competitor is gaining share.

The interviews were not a probability sample. The respondents may have been selected because they were accessible, knowledgeable, recently active, known to the expert network, or especially relevant to the hypothesis. The sample may overrepresent large firms, former executives, urban customers, English speakers, successful operators, or people motivated to take expert calls.

Purposeful sampling is designed precisely to find information-rich cases, not to reproduce the statistical characteristics of a population. Palinkas and colleagues describe purposeful sampling as selecting cases because of their relevance to the phenomenon under study, with different strategies appropriate to different research objectives.

That makes expert interviews extremely useful for questions such as:

Why is this happening?

What mechanism might explain the data?

Which variables are insiders watching?

Where does the conventional explanation fail?

What differences exist across customer types?

Which hypothesis should we investigate next?

It makes them much less suited, by themselves, to estimating population prevalence.

This distinction should appear in the engagement team's evidence language.

Instead of saying “70% of experts believe X,” a qualitative study may be better summarized as: X was a recurring view across these respondent strata; Y was the principal alternative explanation; and Z remains unresolved.

The objective is not weaker evidence.

It is a more accurate description of what the evidence can support.

## What kind of saturation does the decision require?

Once the evidentiary purpose is clear, saturation becomes more useful.

Consider a consulting team investigating why adoption of an industrial technology remains slow.

After seven interviews, it has heard the same five barriers repeatedly: capital cost, integration difficulty, shortage of technical staff, uncertain ROI, and procurement conservatism.

If the engagement question is simply “What are the main barriers?”, the team may be approaching code saturation.

But suppose the client needs to decide which barrier to attack with a new go-to-market model.

Now the research must establish considerably more.

Does “uncertain ROI” mean customers lack evidence of savings, cannot obtain budget approval, distrust vendors' calculations, or have payback requirements the product cannot satisfy?

Is integration difficulty a genuine technical limitation or a purchasing objection used to conceal another concern?

Does the answer change between new facilities and retrofit environments?

Who inside the customer organization can override procurement?

Under what circumstances have buyers adopted despite these barriers?

Those questions seek meaning, causal structure, and boundary conditions rather than another mention of the same theme.

Hennink and colleagues' distinction is useful because it prevents a common diligence failure: stopping when the labels are repeating even though the implications for the decision are not understood. Their empirical results were specific to one study, but the conceptual distinction is broadly applicable.

Malterud, Siersma, and Guassora offer another useful way to frame the problem. Their concept of information power proposes that the necessary sample becomes smaller when participants contain more information relevant to the study. They identify five factors affecting that information power: the specificity of the study aim, specificity of the sample, use of established theory, quality of interview dialogue, and analysis strategy.

Translated into consulting practice, five interviews with precisely selected respondents discussing a tightly specified question in depth may provide more decision-relevant information than 20 loosely matched calls built around a generic discussion guide.

The target should therefore move with the question.

Narrow question + homogeneous respondent universe + high- quality interviews = potentially smaller sample.

Broad question + heterogeneous market + multiple mechanisms + high consequence of missing an exception = larger and more deliberately structured sample.

The second case does not necessarily need dozens more respondents. It needs a better sampling design.

## Design the sample before counting responses

The fastest route to superficial rigor is to pick a target, say 20 interviews, and fill it with whoever is easiest to recruit.

The more defensible approach is to construct the sample architecture first.

Suppose a consultancy is testing the attractiveness of a B2B software market across France, Germany, and the United Kingdom.

The thesis depends on three propositions:

- customers face the same underlying pain point across markets;

- buying authority is moving toward a central functional owner;

- willingness to pay is high enough to support the proposed model.

A generic target of 15 interviews says little. The sampling question is which perspectives could cause the team to reach materially different conclusions. The team might distinguish:

- existing customers versus prospects who declined to buy;

- enterprise versus mid-market buyers;

- technical users versus economic buyers;

- mature adopters versus recent adopters;

- countries with meaningfully different buying or regulatory contexts;

- companies that implemented successfully versus those where adoption stalled.

Maximum-variation sampling formalizes part of this intuition: select cases across dimensions on which meaningful differences are expected so the study can identify both variation and patterns that persist despite it. Methodological work on purposeful sampling contrasts this with homogeneous sampling, which intentionally narrows variation when the research question permits it.

The key is not to maximize diversity indiscriminately.

Every stratum consumes interviews. Split the sample into too many cells and the team ends with one respondent representing each supposed market segment: a taxonomy rather than evidence.

A useful rule is to create a respondent stratum only when the team can finish the sentence:

If this group sees the issue differently, our recommendation might change because…

If no meaningful decision consequence follows, the distinction probably does not deserve its own quota.

## Independence matters before consensus

Sampling design also has a temporal dimension.

Teams often adapt interview guides as research progresses. That is sensible. Early interviews expose better questions.

But adaptation introduces another danger: later respondents can be recruited and questioned inside a frame created by the early respondents.

By interview eight, the team “knows” the market has three principal problems. Interviews nine through 15 then explore those problems in depth. Unsurprisingly, the synthesis becomes increasingly coherent.

The apparent saturation may partly reflect the research process narrowing itself.

Experimental work on collective judgment illustrates why independence is valuable. Lorenz and colleagues had 144 participants make quantitative estimates and found that exposure to others' estimates reduced diversity without reliably improving collective accuracy; it could also increase participants' confidence as their answers converged. The experiment involved simple estimation tasks rather than expert interviews, so it should not be treated as direct evidence about commercial diligence. Its narrower implication is useful: convergence created after exposure is not equivalent to independent convergence.

For important questions, consulting teams should therefore consider independent rounds.

Run an initial set of interviews against the same broad question before exposing respondents, or recruiters, to an emerging consensus where practical.

Then synthesize.

Use the next round deliberately to test gaps, alternative explanations, and respondent groups missing from the first.

RAND's review of expert-judgment elicitation makes a related recommendation in a very different technical context: use multiple experts where possible and include independent experts alongside people closely associated with the project being assessed. It also recommends explicitly pressing experts to consider reasons their uncertainty range may be wider than first stated.

The broader principle is valuable for diligence:

Do not let the first plausible explanation determine the entire remaining sample.

Search for negative cases before declaring saturation

A second danger arises when teams measure saturation by repetition alone.

Imagine 14 experts agree that adoption of a medical technology depends primarily on physician preference.

Interview 15 is a procurement executive from a large hospital system who explains that centralized contracting has removed most physicians' practical choice.

Has the 15th interview added only one minority view?

Or has it revealed that the original conclusion fails in the exact customer segment responsible for most of the client's expected growth?

Frequency alone cannot answer that question.

A negative case is valuable not because dissent is automatically correct but because it tests the boundaries of the emerging explanation.

The research team should ask:

What respondent would we most expect to disagree with our current synthesis?

Which customer tried the product and rejected it?

Which operator failed while peers succeeded?

Which country does not fit the apparent regional pattern?

Which former executive competed unsuccessfully under the strategy we are recommending?

Which member of the value chain loses if the apparent trend continues?

Purposeful-sampling methodologies explicitly include approaches built around extreme, deviant, and maximum-variation cases because unusual cases can reveal mechanisms or conditions hidden by typical ones.

This is particularly important in consulting because many recommendations are not statements about average experience.

They are conditional claims.

A market-entry recommendation may depend on a regulatory exception.

A commercial-diligence case may depend on the defensibility of one customer segment.

A pricing strategy may fail if an important buyer archetype has a fundamentally different procurement process.

The higher the cost of missing such an exception, the less comfortable the team should be with simple thematic repetition.

## “Expert” is not a quality control

The composition problem does not disappear when every respondent has impressive credentials.

Expertise is multidimensional.

Someone may have tremendous industry seniority but limited visibility into current purchasing behavior. A former CEO may understand market structure but not today's technical workflow. A current operator may have excellent local knowledge but unusual incentives. An adviser may have broad exposure while receiving much of their information from the same industry narrative as every other adviser.

Structured-expert-judgment research developed in risk analysis takes this problem seriously enough to evaluate not merely whether people are labeled experts but, where possible, how well their probabilistic judgments are calibrated. Cooke's Classical Model, for example, explicitly distinguishes statistical accuracy from informativeness rather than assuming credentials guarantee forecasting quality.

Most consulting interviews cannot calibrate experts formally in this manner.

But teams can borrow the underlying skepticism.

Record why each respondent is in a position to know the answer.

Separate firsthand experience from secondhand opinion.

Ask what period their knowledge covers.

Look for commercial or professional incentives that could systematically affect the perspective.

Do not turn five people repeating the same industry talking point into five independent observations.

Interview quality is partly a sampling problem and partly a provenance problem.

## A defensible stop rule for fast research

Formal saturation is difficult to establish inside a five-day diligence sprint.

The response should not be to abandon stopping rules. It should be to use one appropriate to the decision.

Francis and colleagues proposed one operational method for theory-based qualitative research: establish an initial analysis sample in advance, then specify how many further interviews must occur without new ideas before stopping. In their studies they used an initial set of ten and a stopping criterion of three subsequent interviews. Those specific numbers came from their research design, not a general law. The useful innovation was making the rule prospective and observable.

Consulting teams can adopt the principle without pretending to conduct an academic qualitative study.

Before interviews begin, define seven things.

## 1. Decision purpose

What decision is the interviews supposed to improve?

“Understand the market” is too vague. “Determine whether low adoption reflects implementation friction or weak customer economics” can guide a sample.

## 2. Respondent strata

Which groups have structurally different information relevant to the decision?

Do not add strata solely for cosmetic coverage.

## 3. Minimum diversity

Which perspectives must be represented before the evidence can be considered adequate?

This is a floor, not the final sample size.

## 4. Independent first round

Gather enough responses before narrowing the inquiry around the emerging consensus.

The objective is to preserve some independent discovery.

## 5. Saturation test

After each round, distinguish three questions:

- Are genuinely new themes still appearing?

- Are interviews still changing the team's understanding of the mechanisms behind important themes?

- Are material differences between strata still unexplained?

The first tests code saturation. The latter two move closer to meaning saturation.

## 6. Negative-case search

Before stopping, conduct a deliberate search for respondents and evidence that should be least compatible with the emerging conclusion. A research program that has only sampled likely confirmers has not earned saturation merely because they agree.

## 7. Decision stop rule

Stop when the required respondent strata have been covered; another independent round is producing little new decision-relevant information; the mechanisms material to the recommendation are understood to the required depth; credible negative cases have been actively sought; and remaining disagreements or sampling limitations are explicit enough for the decision maker to judge.

That last condition is important.

“Enough” does not mean uncertainty has disappeared.

It means the expected decision value of another interview has become lower than the cost, in time, money, or delayed action, of obtaining it.

For a reversible market-sizing discussion, that point may arrive quickly.

For a large acquisition whose thesis depends on subtle changes in customer behavior across countries, it should arrive later.

The downside of being wrong belongs in the sample-size decision even if no qualitative-methods paper can supply a formula for it.

## More interviews can make the research worse

There is a final reason not to maximize sample size.

Every interview adds analysis burden.

A team conducting 40 calls in a week may have less time to interrogate contradictions, trace claims, compare respondent types, or change the next interview guide than a team conducting 15 carefully sequenced conversations.

Scale can produce false confidence if the synthesis process collapses nuanced evidence into theme counts.

This becomes particularly relevant as AI lowers the coordination and synthesis costs of interviewing.

Being able to collect 50 responses does not establish that 50 is methodologically superior to 15. It may allow the team to cover more strata, run an independent second round, or investigate minority cases that previously would have been cut for time. Those are genuine improvements.

Simply adding more similar respondents after the central themes are already understood is not.

The scarce resource shifts from access to interviews toward sampling judgment.

## Where Enquire fits: use scale to improve sample design, not to substitute for it

Enquire's current product offering includes an AI-led interview engine designed to conduct expert interviews asynchronously, as well as text-based input from multiple vetted experts. Its broader research environment is designed to synthesize signals, identify gaps, and make differences across perspectives visible.

For consulting teams, the most defensible use of that additional capacity is not “we can interview more people.”

It is we can design a better evidence sequence within the same deadline.

A first round can cover independently selected respondent strata. Structured synthesis can show which themes repeat, which perspectives differ, and where important gaps remain. A second round can then target missing geographies, contradictory cases, or mechanisms that have not reached sufficient depth.

Asynchronous interviewing can also make it economically easier to include perspectives that a traditional schedule might exclude simply because arranging another call is inconvenient.

But the technology does not resolve the methodological questions.

A larger biased sample remains biased.

Repeated expert opinion does not become market prevalence.

AI synthesis cannot infer a missing respondent category that the research design never considered with complete reliability.

And a polished summary can obscure weak sampling as easily as a slide deck can.

Enquire's role is therefore most useful when additional interview capacity expands variation, independence, and negative-case search, while structured synthesis helps the team decide what it still does not know. Its current positioning explicitly emphasizes combining expert perspectives with structured research and surfacing where views align or diverge.

The methodological standard must still come from the engagement team.

## Defend the sample with logic, not arithmetic

When the partner asks why the team needs four more calls, there should be a better answer than “because 12 is the target.”

Perhaps the first eight interviews have identified the major themes, but only existing customers have been sampled. Four former prospects are needed to test whether the team's explanation of lost sales survives outside the customer base.

Perhaps the research has reached theme saturation in Germany, but the recommendation assumes that the purchasing mechanism transfers to France and no French economic buyer has yet been interviewed.

Perhaps no more calls are needed. The remaining uncertainty concerns market prevalence, which should be answered with transaction data or a survey rather than another qualitative interview.

Or perhaps one interview could be more valuable than ten: the team has found a credible negative case capable of disproving the causal assumption that drives the recommendation.

Those are defensible sampling decisions.

A universal count is not.

The best expert-interview program is therefore not the one with the largest N or the neatest claim of saturation. It is the one that can show a clear chain from decision → research objective → respondent design → evidence gained → disconfirmation attempted → stopping rule.

Then “How many interviews are enough?” becomes answerable.

Enough to know what the major explanations are. Enough to understand the ones that matter. Enough variation to detect where they fail. And no more than the decision requires.

## Sources and further reading

- Monique Hennink, Bonnie Kaiser, and Vincent Marconi, “Code Saturation Versus Meaning Saturation,” Qualitative Health Research journals.sagepub.com

- Greg Guest, Arwen Bunce, and Laura Johnson, “How Many Interviews Are Enough?,” Field Methods sfu.ca

- Monique Hennink and Bonnie Kaiser, “Sample Sizes for Saturation in Qualitative Research,” Social Science & Medicine sciencedirect.com

- Kirsti Malterud, Volkert Siersma, and Ann Dorrit Guassora, “Sample Size in Qualitative Interview Studies: Guided by Information Power” pubmed.ncbi.nlm.nih.gov

- Jill Francis et al., “What Is an Adequate Sample Size? Operationalising Data Saturation for Theory-Based Interview Studies” tandfonline.com

- Lawrence Palinkas et al., “Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research” pubmed.ncbi.nlm.nih.gov

- Jan Lorenz et al., “How Social Influence Can Undermine the Wisdom of Crowd Effect,” PNAS pnas.org

- RAND, Subjective Probability Distribution Elicitation in Cost Risk Analysis: A Review rand.org

- Roger M. Cooke, “Supplementary Information for Structured Expert Judgment” rogermcooke.net

- Enquire, current product overview enquire.ai

### Longer Holds Need a Different Information System — https://enquire.ai/blog/longer-holds-need-a-different-information-system

Author: Enquire Team · Published: 2026-05-28 · Topic: Private Equity

Extended ownership is not only an IRR problem. As a private-equity hold stretches, the external assumptions behind the investment and eventual exit can decay even while management executes the operating plan. Sponsors need a research…

Extended ownership is not only an IRR problem. As a private-equity hold stretches, the external assumptions behind the investment and eventual exit can decay even while management executes the operating plan. Sponsors need a research cadence designed to recalibrate the thesis, not another layer of portfolio reporting.

A private-equity board can know more about a portfolio company than it has ever known and still be working from an aging investment thesis.

The monthly pack tracks revenue, margins, cash conversion, pipeline, churn, headcount, pricing, and the value-creation plan. The sponsor sees which initiatives are on schedule. Management explains deviations. Operating partners intervene where execution falls behind.

The information system is doing exactly what it was built to do: tell the owners whether the company is executing the plan.

But suppose the hold that was expected to last five years reaches year seven.

The competitor set has changed. Artificial intelligence has altered the economics of one product line. The CEO who presented at investment committee is gone. Procurement practices among the company's largest customers have shifted. A regulation that appeared remote at entry now affects buyers. Strategic acquirers value different capabilities than they did when the deal was underwritten.

Those developments may appear somewhere in the board conversation. They rarely receive the same systematic treatment as EBITDA.

That creates a particular risk for longer-duration ownership: the company is monitored continuously while the investment thesis is refreshed episodically.

Private equity's current holding-period problem makes that asymmetry more important. Bain estimates that average buyout holding periods at exit are now around seven years, up from five to six years between 2010 and 2021. Almost 40% of companies are being held for more than five years, compared with 29% in 2019, and the industry is sitting on roughly 32,000 unrealized companies worth $3.8 trillion.

Longer holds clearly affect returns and liquidity. Bain's historical analysis finds buyout IRRs beginning to stagnate around year seven and declining thereafter, although that pattern should not be interpreted as a mechanical rule for an individual asset.

But duration creates another problem that does not fit neatly into the return model.

> The longer an asset is owned, the more opportunities there are for the facts that justified buying it, and the facts that will determine who eventually buys it, to become different facts.

The answer is not more reporting.

It is a second information system.

## A longer hold changes more than IRR

Most sponsor oversight is built around a sensible hierarchy.

Diligence establishes the thesis. The first 100 days translate it into a value-creation plan.

Management and the board then track execution against that plan. As exit approaches, the sponsor begins preparing the equity story and sale process.

That sequence works best when the assumptions at the beginning remain reasonably stable.

Extended ownership weakens that assumption.

McKinsey's 2026 private-markets report argues that the traditional five-year holding period may now be behind the industry. Its analysis also suggests that many sponsors need to treat value creation as a full-life-cycle exercise rather than concentrating effort near exit. Among successfully exited deals since 2019, McKinsey found value creation was disproportionately back-loaded, with more EBITDA-margin improvement occurring in the last two years than in individual earlier years.

That supports sustained operational attention. It does not solve the external-information problem.

A value-creation plan is fundamentally an execution instrument. It asks whether management is producing a desired outcome: increasing sales productivity, consolidating procurement, expanding internationally, raising prices, completing add-ons, or improving working capital.

An investment thesis asks a different class of questions:

Is the market still structurally as attractive as we believed?

Is the competitive advantage still generated by the mechanism we underwrote?

Has technology changed the industry's profit pool?

Do customers still value what we are building?

Is regulatory exposure developing as expected?

Would today's likely exit buyers pay for the same characteristics we are investing to create?

Those questions can deteriorate while every operational KPI remains green.

That distinction becomes particularly important because private equity increasingly depends on operating performance rather than forgiving capital markets. McKinsey argues that leverage and multiple expansion are carrying less of the return burden than they once did, increasing the importance of revenue and margin improvement. BCG likewise recommends making value creation explicitly measurable and auditable rather than relying on a generalized operating narrative.

The less the sponsor can rely on market lift, the more costly it becomes to execute the wrong operating plan extremely well.

## Thesis knowledge decays in two different ways

Calling this “information decay” can be misleading unless the mechanism is clear.

Some information literally becomes stale. A market study conducted in 2021 may no longer describe the market in 2026.

But another form of decay occurs inside the organization: the facts may still be available while the reasoning around them disappears.

Organizational-learning research has documented that knowledge does not necessarily persist merely because an organization once acquired it. In one study covering 2,732 quality-improvement initiatives across 295 suppliers, Agrawal and Muthulingam found measurable depreciation in organizational knowledge, with the persistence depending partly on whether knowledge was embedded in technology, routines, or people. That industrial setting is very different from private equity, but the relevant principle is narrower: learned knowledge has a maintenance problem.

A long-held investment is exposed to both types of decay.

The original commercial-diligence team disperses. The principal who knew why one customer interview mattered more than another becomes a partner or leaves. Management changes. The portfolio team remembers the conclusion, “pricing power is strong,” more readily than the conditions under which that conclusion was reached.

Leadership turnover makes that issue concrete. AlixPartners' 2026 survey of 174 private-equity leaders and 253 portfolio-company executives found that 65% of PE-firm respondents reported CEO turnover during the holding period; its broader findings also describe significant investor-management differences over priorities and execution. The survey is perceptual rather than transaction-level performance research, but it illustrates how often the people interpreting an investment can change before exit.

Meanwhile, ownership itself creates a behavioral problem.

Barry Staw's classic experiment on escalation of commitment found that participants in a simulated business-investment decision committed more resources to a poorly performing prior choice when they had been personally responsible for that choice. It was a laboratory study of 240 business students, not private-equity partners, so it cannot establish how frequently the effect occurs in professional investing. It does establish a mechanism worth designing against: responsibility for an earlier decision can change how later negative evidence is treated.

Over seven years, therefore, a sponsor can end up with an odd combination:

more operational data, but less distance from the original premise.

The diligence deck survives as ceremonial history. The assumptions inside it stop behaving like hypotheses.

Use three research horizons

A longer hold needs a cadence that keeps the external thesis alive without converting the board into a permanent diligence committee.

The useful design has three horizons.

Monthly: monitor assumption signposts

The monthly layer should be deliberately light.

Start with the handful of external assumptions whose failure would materially alter the investment case. Each should have one or two observable signposts that can be updated without commissioning a new research project.

A software investment might monitor whether a new technology is changing customers' willingness to pay, not merely the company's own renewal rate.

An industrial asset might track competitors' capacity additions and end-customer capital budgets alongside internal order intake.

A healthcare investment might track reimbursement or regulatory developments that could alter the economics of the value-creation plan.

The purpose is not to create another dashboard containing 40 external indicators.

It is to prevent a critical assumption from going unexamined for two years simply because management's KPI set does not measure it.

Each signpost should have an owner and an explicit question attached:

## What would this need to do before we reopen the assumption?

A monthly observation should rarely force an investment decision. It should tell the sponsor when a question has become important enough to investigate.

## Quarterly: subject one or two assumptions to external challenge

The quarterly process should go beyond monitoring but remain separate from general board reporting.

Choose the one or two external assumptions with the highest combination of uncertainty and consequence.

Then investigate them using evidence management cannot generate simply by running the company: customers, former customers, suppliers, competitors, former operators, regulatory specialists, industry data, adjacent-market developments, or other independent evidence.

The framing matters.

Do not ask, “Is our thesis still right?”

Ask questions that allow it to be wrong.

If the investment depends on pricing power, investigate why customers are accepting price increases and whether alternatives have changed.

If it depends on consolidation, investigate whether new entrants or financing conditions have changed acquisition economics.

If it depends on international expansion, investigate the buyer behavior, regulation, channel structure, or competitive response in the next market before management's own expansion results become the principal source of information.

This is external challenge, not another performance review.

That distinction solves much of the counterargument that an additional research cadence will burden management. A properly designed quarterly challenge should require little new reporting from the portfolio company. It exists precisely to examine questions the company's operating systems cannot answer.

The sponsor should return with findings, not send management another template.

## Annually: re-underwrite the investment rather than update the memo

Once a year, the sponsor should do something more difficult: treat continued ownership as a fresh capital-allocation decision.

Not literally from zero. The board now possesses years of company-specific knowledge that should inform the analysis.

But the annual review should not begin with the latest version of the original thesis and ask what needs editing.

It should begin with the current facts.

What business do we own now?

Where does its economic advantage actually come from?

Which value-creation levers still have attractive returns on time and capital?

What has become harder than expected?

Which original assumption is no longer relevant?

What risk would a new investor identify that the existing board has normalized?

What alternative use of capital competes with the next dollar invested here?

Most importantly, the review should distinguish decision quality from outcome.

Baron and Hershey demonstrated outcome bias across five experiments: participants rated decisions more favorably when they knew the eventual outcome had been favorable, even when the information available to the original decision maker was held constant. Again, laboratory judgments are not investment committees. But they establish why historical success should not be allowed to validate the reasoning automatically.

A value-creation initiative can succeed for reasons unrelated to the original thesis.

A failed initiative can have been a reasonable decision given the information available.

An extended hold makes those distinctions especially important because management, sponsor, and board have accumulated years of shared decisions that are easy to reinterpret after the fact.

The annual exercise should leave behind a revised thesis with explicit unresolved questions, not simply an affirmation that “the thesis remains intact.”

## Re-underwrite the exit buyer, not only the company

The most neglected part of a long-hold research system may be the exit.

At entry, sponsors routinely form a view of future strategic buyers, sponsor appetite, public-market comparables, likely valuation logic, and characteristics that will command a premium.

Several years later, the company may have executed the original value-creation plan exactly as intended while the buyer universe has changed its definition of quality.

That is not hypothetical in the current market. EY's 2026 exit-readiness study, based on separate cohorts of 100 PE executives and 100 executives from recently exited portfolio companies, reports that buyers have become more selective and that 35% of the global portfolio is now held for more than six years. EY also finds that buyers are increasingly examining issues such as AI adoption and AI-related disruption as part of the equity story, while detailed data supporting value-creation claims remains a major diligence requirement.

That evidence comes from a commercial survey and should not be read as establishing a universal recipe for higher exit multiples. Its more useful implication is that buyer diligence criteria evolve.

The annual re-underwrite should therefore contain an explicit exit-market section:

Who are the plausible buyers today, not at entry?

Why would each one own this asset?

Which capabilities have become strategically more valuable?

Which parts of the original equity story have become commonplace?

What evidence would a buyer demand that the company cannot currently produce?

Which value-creation initiatives are valuable to the current owner but unlikely to receive credit from the next one?

What will the next owner believe it can still do?

That final question is particularly important.

A sponsor can optimize a company so thoroughly for its own value-creation plan that it leaves the next buyer with little credible upside. Conversely, an extended hold may uncover an entirely new buyer rationale that did not exist at entry.

Exit readiness should therefore not begin only when the banker is hired.

EY's survey finds an association between earlier preparation and respondents' reported exit outcomes, with firms beginning preparation 12–24 months before sale reporting stronger perceived effects than those starting later. McKinsey likewise argues for formally recalibrating assets midcycle and making exit readiness part of full-life-cycle value creation.

For an asset whose timing is uncertain, however, even 12–24 months can be the wrong mental model.

The sponsor should continuously maintain a view of what the eventual buyer will need to believe, then intensify formal preparation when an exit window becomes plausible.

## Keep the board's external challenge narrow and consequential

There is an obvious failure mode in this proposal.

Private-equity portfolio companies already generate substantial reporting. Management teams can spend too much time responding to sponsors, boards, lenders, auditors, consultants, and transformation offices. Adding monthly research signposts, quarterly challenges, and an annual re-underwriting exercise could easily become governance inflation.

That would defeat the purpose.

The new cadence should follow one rule:

## Do not use external research to monitor anything the operating dashboard already answers adequately.

Revenue against plan belongs in ordinary management reporting.

Whether the market's basis of competition has changed does not.

Employee turnover belongs in the management system.

Whether a different leadership profile is now required for the next stage of the thesis may not.

Pipeline conversion belongs in commercial reporting.

Whether customers increasingly regard the company's product as substitutable requires a different evidence set.

The additional research burden should therefore be concentrated on assumptions that are simultaneously:

- material to the investment thesis;

- externally determined or difficult for management to observe objectively;

- uncertain enough that new evidence could change the sponsor's decision.

If none meets that test, do not commission new research that quarter.

Longer ownership does not justify permanent diligence.

It just makes unexamined assumptions more expensive.

## The research system should preserve what changed

There is one more design requirement: the three horizons need memory.

A sponsor that runs excellent quarterly research but treats each exercise as a standalone project will eventually encounter the same problem as the original diligence. The organization will retain a pile of useful outputs while losing the evolution of the thesis.

BCG's 2026 work on M&A argues for systematically comparing transaction outcomes with initial assumptions so that information from one stage feeds later decisions rather than dissipating when the team moves on. The article concerns M&A organizations more broadly, not specifically PE portfolio governance, but the feedback-loop principle transfers directly.

For a portfolio company, each material assumption should therefore retain a history:

- What did we believe at entry?

- What evidence supported that belief?

- What changed?

- What did we investigate?

- What did external sources disagree about?

- How did our confidence change?

- Did we change the operating plan?

- What remains unresolved?

- Who owns the next update?

That historical record matters more as the team around the asset changes. It also makes the annual re-underwrite less vulnerable to retrospective storytelling.

The board can see not just what it believes now, but how and why its view migrated during ownership.

## Where Enquire fits: carry diligence context through the entire hold

This is a natural use case for research infrastructure that persists beyond an individual diligence exercise.

Enquire's current capital-markets materials explicitly describe its private-equity workflow as preserving institutional understanding from pre-deal through ownership and exit and allowing teams to revisit prior work with its context intact as new information arrives. The platform combines structured research with expert perspectives and is designed to maintain a living understanding as conditions change.

Its product materials similarly describe an “Evolving Research Context,” with context preserved across successive inquiries rather than restarting research for each question.

For a longer-held PE asset, the useful workflow is not simply storing the original commercial-diligence report.

The pre-deal assumptions become the starting research context. Monthly signposts show which assumptions are moving. Quarterly external research adds current market or expert evidence around the few questions that matter. The annual re-underwrite can compare current conclusions with the original thesis and with earlier external challenges. Exit research can then inherit the same evidence trail rather than rediscovering why the sponsor built the company the way it did.

Outside perspective is particularly useful where the board's own information is structurally limited: competitor behavior, customer alternatives, regulation, technology shifts, supplier dynamics, and evolving buyer priorities.

None of that replaces management information or sponsor judgment.

It provides a second information stream for questions that internal reporting cannot resolve.

## A longer hold should produce a better thesis, not merely an older one

Longer holding periods are usually discussed as a financial consequence.

They delay distributions. They alter IRR. They increase exposure to macroeconomic conditions and complicate fundraising. Those effects are real.

But duration also changes the epistemic burden of ownership.

A five-year investment that becomes an eight-year investment has not simply added three years to the same deal.

The management team may be different.

The competitive advantage may now rest on a different capability.

The regulatory environment may have moved.

Technological change may have altered what customers value.

The next buyer may be underwriting a different source of growth.

And the sponsor itself has accumulated enough ownership of the asset to make independent reassessment harder.

The appropriate response is not another portfolio dashboard.

It is a disciplined external-research cadence:

Monthly: Which assumption signposts moved?

Quarterly: Which material external assumption deserves independent challenge?

Annually: Would we underwrite this business, value-creation plan, and exit rationale the same way today?

Over a long hold, the portfolio company should become better understood, not merely better reported.

That leaves a harder question for the next board meeting than whether the value-creation plan remains on track:

Which belief from the original deal thesis are we still acting on because the evidence remains strong, and which one survives mainly because nobody has researched it again?

## Sources and further reading

- Bain & Company, Private Equity Outlook 2026: Gaining Traction bain.com

- McKinsey & Company, Global Private Markets Report 2026 mckinsey.com

- BCG, “Private Equity's Advantage Is Shifting, Not Shrinking” bcg.com

- EY, Global Private Equity Exit Readiness Study 2026 ey.com

- AlixPartners, 11th Annual Private Equity Leadership Survey alixpartners.com

- Barry M. Staw, “Knee-Deep in the Big Muddy: A Study of Escalating Commitment to a Chosen Course of Action” web.mit.edu

- Jonathan Baron and John C. Hershey, “Outcome Bias in Decision Evaluation” bear.warrington.ufl.edu

- Agrawal and Muthulingam, “Does Organizational Forgetting Affect Vendor Quality Performance?” pubsonline.informs.org

- BCG, “AI Is Turning M&A into a High-Impact Learning Machine” bcg.com

- Enquire, Capital Markets enquire.ai

### The Cost of Starting Every Research Question From Zero — https://enquire.ai/blog/the-cost-of-starting-every-research-question-from-zero

Author: Enquire Team · Published: 2026-05-07 · Topic: Consulting

Most firms retain research outputs but lose the reasoning that made them useful. The reusable asset is not the old report; it is the chain connecting the question, assumptions, evidence, disagreement, decision, and what happened next.

Most firms retain research outputs but lose the reasoning that made them useful. The reusable asset is not the old report; it is the chain connecting the question, assumptions, evidence, disagreement, decision, and what happened next.

A strategy team receives a question it has seen before.

Perhaps the company is reconsidering a market it assessed two years ago. A consulting team is entering an industry another office recently studied. An investment group is revisiting a theme that generated several prior diligence exercises. A policy team needs to understand a regulation its colleagues analyzed when the proposal first appeared.

The organization is not starting from zero. Somewhere there are presentations, transcripts, market studies, spreadsheets, emails, expert notes, and perhaps a final recommendation.

Yet the new team often behaves as though it is.

It searches for the latest market data. It reconstructs the competitive landscape. It commissions another round of interviews. It rediscovers disagreements that prior researchers already encountered. Only late in the process does someone remember a colleague who “did something on this last year.”

The obvious diagnosis is poor retrieval.

Often, it is not.

The deeper problem is that organizations preserve documents more reliably than they preserve research memory.

A final report tells a future team what the previous team concluded. It rarely preserves enough of the structure behind that conclusion: the precise decision being considered, assumptions that were uncertain, sources that carried disproportionate weight, disagreements that were never resolved, questions deliberately left open, confidence attached to major claims, and subsequent evidence that strengthened or weakened the original view.

Strip away that structure and a report becomes difficult to reuse safely. The next researcher must either trust the old conclusion without reconstructing its basis or reconstruct the entire inquiry before deciding what remains valid.

That accumulated reconstruction cost can be thought of as research debt.

Like technical debt, it does not necessarily appear as an explicit line item. It appears as duplicated research, repeated expert calls, lost provenance, slow onboarding, contradictory internal conclusions, and teams spending expensive time establishing what the organization should already know.

AI makes this problem more consequential, not less. Generative systems are making it dramatically easier to produce and search large volumes of analytical material. The scarce organizational capability increasingly becomes the ability to determine which prior reasoning is reusable, under what conditions, and what now needs to be refreshed.

The practical unit of institutional research memory should therefore not be the document.

It should be a reusable research object.

## Repositories do not automatically create memory

Organizations have been building knowledge repositories for decades. The problem of reuse has survived them.

That is partly because knowledge retention is only one component of organizational learning. Linda Argote, Sunkee Lee, and Jisoo Park divide the process into four distinct activities: search, knowledge creation, knowledge retention, and knowledge transfer. An organization can therefore become quite good at storing what it has produced without becoming equally good at getting prior knowledge into a new decision.

The distinction becomes clearer in research on transactive memory systems.

Transactive memory is often summarized as knowing “who knows what.” It treats collective memory not simply as a common warehouse of facts but as a system in which knowledge is distributed and people understand where expertise resides. Ren and Argote's review of 76 studies found a substantial literature connecting such systems to learning and group performance, while also noting important questions about extending team-level transactive memory to whole organizations.

Empirical studies help explain why that distinction matters.

Lewis, Lange, and Gillis found that groups with an established transactive memory system and experience across multiple related tasks were better able to develop an abstract understanding that transferred to new tasks. But the effect was conditional: the authors did not find equally strong support after experience with only one prior task, and stability in who held expertise mattered.

In a different setting, Reagans, Argote, and Brooks studied joint-replacement teams in a teaching hospital. They found distinct performance contributions from individual experience, broader organizational experience, and experience working together, the last of which improves the team's ability to coordinate.

These settings are not research departments, and their findings should not be imported mechanically. But they point to an important organizational-memory principle:

It is not enough for knowledge to exist somewhere. Future users need a workable route back to the knowledge, the people, and the context that gave it meaning.

That is precisely where document repositories often fail. A presentation called Market_Entry_Final_v7.pptx may preserve the answer. It does not necessarily preserve why one source was believed over another, which assumption the team considered fragile, which former executive disputed the apparent consensus, or which question remained unanswered when the steering committee made its decision.

Search can find the file.

It cannot recover reasoning that was never stored.

## The final output is the wrong unit of reuse

The traditional knowledge-management instinct is to save deliverables.

That makes sense operationally. Deliverables are visible, polished, and comparatively easy to classify. They are also often the point at which the richest parts of the inquiry have already been compressed away.

Research is a transformation process.

A team begins with a decision or uncertainty. It frames questions. It forms assumptions. It searches. It decides which sources are credible. It encounters contradiction. It makes judgment calls about what evidence means. It develops confidence in some propositions and leaves others unresolved. Eventually, all of that becomes a recommendation, memo, presentation, model, or briefing.

The final artifact is useful precisely because it compresses that process.

Compression, however, destroys information needed for later reuse.

M. Lynne Markus's influential work on knowledge reuse identified this problem from another direction. Different kinds of knowledge reusers have different needs, and repositories built around what the original producer naturally records often fail to meet those needs. Reuse can consequently require substantial rework, while the original knowledge producer has limited incentive to repurpose material for unknown future users.

A future analyst rarely wants merely to know, “What did the previous team conclude?”

They need to know:

What were they trying to decide?

Which facts were observations, and which were interpretations?

What assumptions had to hold?

Which sources were primary?

Where did informed people disagree?

What remained unresolved?

How certain was the team?

What action followed?

What happened afterward?

Without those connections, old research has ambiguous status. It is simultaneously too valuable to ignore and too opaque to trust.

The reusable unit should therefore be a minimum research object containing at least:

- Question and decision context: What uncertainty was being investigated, and what decision would the research inform?

- Source provenance: What evidence supported the work, when was it collected, and where did it originate?

- Live assumptions: Which propositions were being treated as true but remained uncertain?

- Unresolved questions and disagreement: What was not settled, and where did credible sources diverge?

- Confidence: How strongly did the researchers believe the central conclusions, and why?

- Owner: Who understands the inquiry well enough to interpret or update it?

- Refresh trigger: What change in time, market conditions, regulation, evidence, or decision context should cause the work to be revisited?

The deliverable can sit on top of this object.

It should not substitute for it.

## Research debt compounds invisibly

Research debt starts small.

A team fails to link an expert transcript to the claim it informed. The consequence is negligible.

Six months later, another team encounters the same claim but cannot determine whether it came from one operator, five interviews, a public report, or a colleague's intuition. It searches again.

Then a third team finds both sets of research. Their conclusions differ, but there is no clear record of whether conditions changed or the teams simply interpreted the evidence differently.

Now someone must reconcile them.

The organization has paid for the same question several times: first to investigate it, then to rediscover it, then to understand why its own answers conflict.

BCG describes a version of this problem in M&A. Its 2026 research argues that insights from one deal often fail to carry into the next because data are fragmented and knowledge dissipates as teams move on. BCG highlights the opportunity to capture past interactions, decisions, rationales, assumptions, and subsequent outcomes in reusable form so that future transactions can learn from them.

The same mechanism appears across research-intensive organizations.

A management-consulting practice repeatedly investigates the economics of the same sector because knowledge is organized by engagement rather than question.

A public-markets team has years of company and industry research but cannot reconstruct how its original expectations compared with subsequent evidence.

A government-affairs organization produces excellent country updates while losing the assumptions behind earlier regulatory scenarios.

A corporate strategy group retains board presentations but not the rejected alternatives or evidence that caused the recommendation to change.

The cost is not just repeated desk research.

Research debt removes the organization's ability to distinguish new learning from rediscovery.

If a team reaches the same conclusion six months later, did evidence strengthen the thesis? Or did a new group simply reconstruct the same analysis from scratch?

If the conclusion changed, did the world change? Did new evidence arrive? Did different experts get consulted? Or did a different analyst frame the question differently?

Those are valuable organizational-learning questions.

A folder of finished reports often cannot answer them.

## AI makes context quality more important, not less

One might expect AI to eliminate research debt by making everything searchable.

It will help considerably with retrieval.

It may also make the underlying problem worse.

The volume of generated research is increasing, while the marginal cost of creating another summary, market map, competitor profile, scenario, or interview synthesis is falling. That means an organization can accumulate analytical artifacts faster than humans could ever maintain a traditional knowledge library.

Finding text becomes easier.

Knowing what deserves to be reused becomes harder.

Recent organizational-learning research is beginning to examine this problem at the system level. Figge, Anderson, and Lewis model organizations in which AI learning interacts with individual and collective learning, explicitly incorporating both learning and forgetting. Their work is computational rather than field evidence about corporate research functions, but its important conceptual move is to reject the idea that AI productivity can be understood solely at the individual- task level. AI becomes part of a broader learning system in which different stocks of organizational knowledge can reinforce, or undermine, one another over time.

This suggests a useful test for AI research infrastructure.

Do not ask only:

Can it generate a good answer?

Ask:

Does completing this inquiry leave the organization better prepared for the next related question?

An AI system that produces 100 excellent reports but leaves no durable structure connecting questions, assumptions, evidence, and later outcomes may increase individual productivity while contributing little to institutional learning.

In fact, it can accelerate research debt by creating more outputs that future teams must evaluate.

The important AI capability is therefore not merely generation or semantic retrieval. It is maintaining relationships across inquiries.

This question was asked because of that decision.

This claim rested on these sources.

This conclusion depended on these assumptions.

This experts disagreed for these reasons.

This uncertainty remained open.

This decision followed.

This later evidence changed the view.

That structure turns an archive into something closer to a learning system.

## Reuse without context creates a different failure: stale certainty

There is an obvious danger in this argument.

Starting from prior research is not always better than starting over.

Markets change. Technologies mature. regulations evolve. Competitive structures shift. Management teams change. Expert knowledge ages. A framework that worked in one client, country, or business model can fail badly in another.

Institutional memory can become institutional inertia.

Lee and Van den Steen formalized part of this tradeoff in their research on knowledge-management systems. Their model finds that formal knowledge systems become particularly valuable when organizations repeatedly encounter similar issues, but also that recording and disseminating existing practices can under some conditions discourage experimentation. They describe a potential “competency trap” in which accessible prior know-how pushes people toward established approaches rather than continued exploration.

That is the strongest case against indiscriminate reuse.

And it changes what good research memory should look like.

A poor system says:

We researched this in 2024. Here is the answer.

A good system says:

We researched this in 2024 for this decision, under these conditions. These were the assumptions.

This evidence was current as of this date. These questions remained unresolved. These developments would invalidate or require refreshing the work.

The goal is not to preserve answers indefinitely.

It is to preserve enough context to know what can safely be inherited and what must be questioned again.

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This is one reason explicit hypotheses matter. MIT CISR's research on organizational learning argues that teams learn more effectively when they unpack initiatives into underlying assumptions that can be tested separately. An explicit hypothesis makes it easier to determine what evidence has actually changed rather than simply labeling the eventual project a success or failure.

Research memory should do the same thing.

It should preserve not merely what the organization knew, but the conditions under which it believed it knew it.

## Design for updating, not copying

A reusable research object therefore needs a lifecycle.

Imagine a new team encounters prior work on the market it is investigating.

The wrong workflow has two extremes.

At one extreme, the team ignores the old work and begins again.

At the other, it imports the old conclusions directly because “we already have research on this.”

A better workflow begins with triage.

## What remains structurally reusable?

Definitions, historical analysis, primary-source collections, interview guides, causal frameworks, and descriptions of institutional structure may remain useful for years.

## What has a shorter half-life?

Market size, pricing, competitor behavior, regulation, executive opinions, operating metrics, and expert observations may require frequent updating.

## What was always uncertain?

The old research should expose its unresolved questions and low-confidence assumptions immediately instead of making the new team infer them from polished prose.

## What has happened since?

New evidence should attach to the old question, allowing researchers to see whether the prior analysis has been confirmed, weakened, superseded, or rendered irrelevant.

This changes the mindset from reuse the answer to resume the inquiry.

That is a much higher standard for knowledge management.

It is also a more defensible one.

## Diagnose research debt before buying another search tool

Heads of research and knowledge management can test how serious the problem is without building an elaborate measurement system.

Take a sample of recurring questions from the last year and ask the next researcher to reconstruct the organization's prior state of knowledge.

Measure five things.

## 1. Prior-work discovery rate

Can researchers reliably identify the relevant previous inquiries, or do they depend on someone remembering that the work exists?

## 2. Provenance recovery

Once an important prior conclusion is found, how easily can the researcher trace it back to the evidence that supported it?

A repository can score well on discovery and poorly on provenance.

## 3. Reasoning recovery

Can the researcher tell which assumptions drove the conclusion, which alternatives were rejected, and what uncertainties remained?

This is the difference between finding an output and recovering an inquiry.

## 4. Repeated-work rate

How much new research recreates evidence already collected inside the organization?

Not all repetition is waste. Sometimes deliberate replication is exactly what confidence requires. The diagnostic should distinguish purposeful retesting from accidental rediscovery.

## 5. Refreshability

Can the team identify which part of the prior work needs updating without redoing everything?

If every new question requires reopening the whole analysis, the organization has stored an artifact but not created a reusable research asset.

These measures should be interpreted together.

A team with a high reuse rate but no refresh discipline may simply be circulating stale analysis.

A team with low reuse may either have poor research memory or operate in an environment where every problem is genuinely novel.

Research debt is therefore contextual. The best candidates for structured memory are domains where questions recur while answers evolve.

Capital markets, policy monitoring, recurring diligence, sector research, competitive strategy, and professional-services delivery all fit that pattern unusually well.

## Measure whether knowledge travels

The ultimate test is not how many documents are stored or how often an internal search tool is used. It is whether experience changes later work.

Organizational-learning research provides a useful conceptual standard. Reagans, Argote, and Brooks found that organizational experience and experience working together contributed separately to performance; Lewis, Lange, and Gillis found that knowledge structures could support transfer to related tasks under particular conditions.

For research organizations, the analogous question is:

### Does inquiry N make inquiry N+1 materially better?

That could mean the second team reaches the frontier of existing knowledge faster.

It could mean it knows which question not to ask again.

It could discover a contradiction between new and old evidence sooner.

It could improve an interview guide because earlier respondents exposed a weak assumption.

It could identify a researcher or expert who already understands the issue.

Or it could recognize that the old conclusion no longer applies, and explain exactly why.

BCG's current M&A work describes closing this feedback loop explicitly: compare outcomes with earlier assumptions, identify which diligence findings proved predictive, understand where models diverged from reality, and feed those lessons into subsequent decisions.

That is a useful definition of institutional learning far beyond M&A.

The organization should not merely accumulate research.

Its next question should inherit the learning from the last one.

## Where Enquire fits: make the inquiry itself persistent

This is also where Enquire's current product direction connects naturally to the problem.

Enquire describes its Research Center as a workspace for building and refining understanding as conditions change, and its current product materials explicitly describe an Evolving Research Context in which context is preserved across inquiries. The company also says organization-level Research Centers support collaborative synthesis and that insights can accumulate rather than requiring research to restart with each question.

Its current positioning combines that persistence with structured AI research and expert perspective, allowing prior questions, emerging disagreements, and new evidence to remain connected as research develops.

The useful application is not simply a better archive of Enquire reports.

It is to make the research context itself persistent.

A team investigating a sector can begin with what previous researchers established, see where expert perspectives diverged, identify assumptions still requiring scrutiny, add new external evidence, and update the inquiry rather than opening a clean page.

That design does not solve the stale-knowledge problem automatically. Dates, provenance, confidence, unresolved questions, ownership, and refresh conditions still have to be visible.

Nor should an old expert perspective become authoritative simply because it is preserved.

The advantage is narrower: maintaining enough continuity that new research can interrogate the old work instead of first reconstructing it.

## Stop asking how much knowledge you store

The paradox of institutional knowledge is that many organizations possess more of it than they can effectively use.

Their servers contain the research.

Their people remember pieces of it.

Their search systems can increasingly locate it.

Yet the next project still begins with reconstruction because the relationships that make the knowledge reusable were discarded when the last project ended.

AI will make storing another answer nearly free.

That makes the distinction more important.

The organizations that develop durable research memory will not necessarily be those with the largest repositories, longest transcripts, or most capable enterprise search.

They will be the ones that preserve the smallest useful structure around each important inquiry:

- What were we trying to decide?

- What did we believe?

- What evidence supported it?

- Where did informed people disagree?

- How confident were we?

- What remained unresolved?

- What did we do?

- What evidence would make this research stale?

Preserve that chain, and old research becomes something a future team can challenge, update, and extend.

Lose it, and the organization acquires another document while quietly taking on more research debt.

The question for a research leader is therefore not, “Can our people find what we already produced?”

It is more demanding:

When this question returns in a year, what will the next team inherit besides our final answer?

## Sources and further reading

- Yuqing Ren and Linda Argote, “Transactive Memory Systems 1985–2010,” Academy of Management Annals experts.umn.edu

- Kyle Lewis, Donald Lange, and Lynette Gillis, “Transactive Memory Systems, Learning, and Learning Transfer,” Organization Science pubsonline.informs.org

- Ray Reagans, Linda Argote, and Daria Brooks, “Individual Experience and Experience Working Together,” Management Science pubsonline.informs.org

- Linda Argote, Sunkee Lee, and Jisoo Park, “Organizational Learning Processes and Outcomes,” Management Science pubsonline.informs.org

- M. Lynne Markus, “Toward a Theory of Knowledge Reuse,” Journal of Management Information Systems jmis-web.org

- Deishin Lee and Eric Van den Steen, “Managing Know-How,” Management Science pubsonline.informs.org

- Jeanne W. Ross and Nils O. Fonstad, “Learn from Hypotheses, Not Failures,” MIT CISR cisr.mit.edu

- Jens Kengelbach et al., “AI Is Turning M&A into a High-Impact Learning Machine,” BCG bcg.com

- Patrick Figge, Edward Anderson, and Kyle Lewis, “AI-human learning systems,” Strategic Organization journals.sagepub.com

- Enquire, current product overview enquire.ai

### A Regulatory Calendar Is Not a Strategy — https://enquire.ai/blog/a-regulatory-calendar-is-not-a-strategy

Author: Enquire Team · Published: 2026-04-16 · Topic: Consulting

The date a rule applies tells a company when its legal obligations begin. It does not tell executives how those obligations will reach the business. Regulatory strategy requires a second map: authority, capacity, guidance, complaints,…

The date a rule applies tells a company when its legal obligations begin. It does not tell executives how those obligations will reach the business. Regulatory strategy requires a second map: authority, capacity, guidance, complaints, local variation, and the counterparties that can turn a rule into operating pressure.

On August 2, 2026, a regulatory calendar for the EU AI Act acquired an important new entry.

The European Commission’s AI Office and national authorities began enforcing several parts of the Act. Transparency requirements for certain AI systems started applying. Providers of general-purpose AI models became subject to enforceable obligations. Prohibited-practice rules became enforceable as well.

But “August 2” does not describe one uniform operational event.

Enforcement responsibility is divided among the AI Office, national competent authorities, and the European Data Protection Supervisor depending on the system and provider. The Commission itself says effective enforcement depends on Member States properly designating and adequately resourcing their authorities. Meanwhile, amendments adopted in 2026 postponed important high-risk-system requirements: rules for Annex III systems now apply from December 2, 2027, while those for high-risk AI embedded in regulated products apply from August 2, 2028.

For a company trying to decide what to do next, this is the real problem with a regulatory calendar.

The calendar tells you when the law says something happens.

It does not necessarily tell you who will act first, what they will look for, which interpretation will become operational, how quickly a supervisory body can build technical capability, whether a complaint will trigger scrutiny, or when a customer, distributor, auditor, employee, or procurement function will begin imposing a stricter practical standard than the regulator has yet demonstrated.

Those questions do not alter the legal obligation. A company should not use uncertainty about enforcement to rationalize non-compliance with a rule that clearly applies.

But they matter enormously to regulatory strategy.

The most useful way to think about a new regime is therefore through three clocks:

- the compliance clock, when an obligation legally applies;

- the enforcement clock, when institutions can detect, interpret, investigate, and act on non-compliance;

- the market clock, when customers, counterparties, employees, intermediaries, and other actors begin changing behavior because of the rule.

A regulatory calendar usually tracks the first. Government-affairs and business leaders need to understand all three.

The date is the easiest fact

Effective dates are attractive because they appear precise.

They can be entered into a spreadsheet. Owners can be assigned. Implementation programs can count backward from them. Executives can ask whether the company is “ready for August” or “ready for 2027.”

Institutional reality is harder to compress.

The AI Act illustrates why. The Commission’s current enforcement architecture assigns general-purpose AI and some other systems to the AI Office, most other AI systems to national competent authorities, and systems used by EU institutions to the European Data Protection Supervisor. Within Member States, there may be multiple market-surveillance authorities, with a single point of contact intended to coordinate the national structure. Authorities can have substantial investigative powers, including access to documentation, datasets, and in some circumstances source code.

Yet a power written into a statute is not the same thing as an operational enforcement program.

Regulators must decide what to monitor, develop procedures, recruit or train people, acquire technical expertise, interpret rules, coordinate with other institutions, process complaints, and decide which potential infringements justify scarce attention.

That is not peculiar to AI. The OECD Regulatory Policy Outlook 2025 emphasizes that regulators cannot continuously inspect every product and business. Effective enforcement therefore requires allocating finite resources, often using risk to determine where intervention is most valuable. The OECD also stresses that implementation depends on institutional skills and resources, not simply well-designed legal text.

For the regulated company, the implication is not “the regulator is resource-constrained, so wait.”

It is that implementation planning and regulatory strategy answer different questions.

Compliance asks: What must we do?

Strategy asks: How is this regime likely to become operational around us, and where would additional preparation, engagement, intelligence, or executive attention have the greatest value?

## Five layers sit between a rule and its business effect

The answer requires looking beneath the legislative timetable.

## 1. The obligation

Start with the law itself.

What obligation applies? To which legal entity, product, use case, or role in the value chain? When does it apply? What transitional provisions matter? Which requirements are already operative, and which have been postponed or amended?

This layer belongs principally to legal analysis. Official text, authoritative guidance, and qualified counsel should establish the company’s compliance position.

The discipline is important because uncertainty elsewhere in the system can create a subtle temptation to blur legal and strategic questions. A regulator’s apparent lack of capacity does not make an applicable duty optional.

The enforcement-readiness exercise begins after that boundary is clear.

## 2. The authority

The next question is not simply “Who is the regulator?”

It is: Who can do what?

Regulatory regimes frequently divide responsibility by sector, product, geography, legal issue, or stage of the value chain. The AI Act is a current example. Its EU-level architecture combines the AI Office with national market-surveillance authorities, notifying authorities, fundamental-rights bodies, and established sector regulators.

National implementation can then produce different operating structures inside the common EU framework.

Germany’s AI implementation law, which entered into force on July 29, 2026, made the Bundesnetzagentur a market-surveillance authority, single point of contact, and central complaints point while retaining established sector regulators in areas such as financial services and media. The Bundesnetzagentur also has a central coordination role.

Spain illustrates a different kind of implementation maturity. Its government introduced an organic-law proposal on AI governance in May 2026, with a model involving the Spanish AI supervisory agency and existing sectoral authorities; the Congress listing available this summer still showed the bill in committee at the amendments stage.

This does not mean one country will enforce “harder” than another. The available institutional facts do not justify that conclusion.

It means that a multinational cannot infer the practical route of supervision from the EU-level regulation alone.

## 3. The capacity

Once the authority is identified, ask what it can actually execute.

How many relevant people does it have? Which technical disciplines are represented? Has it established an AI unit, testing capability, inspection process, reporting channel, sandbox, or case-management infrastructure? What work is centralized, and what depends on other agencies?

Capacity should not be reduced to headcount. A small specialist unit with strong information rights and existing sector knowledge may be more operationally consequential than a larger organization building capability from scratch.

The OECD identifies skills and resources as core requirements for effective regulation, noting the increasingly technical and analytical competencies regulators need to gather and interpret evidence. The Commission made the same point unusually explicitly when AI Act enforcement began: effective enforcement would depend in part on national competent authorities being adequately resourced.

Capacity is also dynamic. The Commission’s current governance materials say additional EU AI-model evaluation capacity is expected to become operational in 2027.

A static regulatory memo will miss this kind of institutional build-out.

## 4. The interpretation

Legal text rarely eliminates every implementation question.

Standards, implementing acts, codes of practice, FAQs, supervisory statements, case decisions, technical guidance, and informal regulator-industry interactions can progressively reduce ambiguity.

But these sources do not all have the same legal status.

That distinction is crucial. Regulatory-strategy teams should track not simply whether “guidance exists,” but its maturity and authority: Is it final or draft? Binding or non-binding? EU-wide or national? Has it been adopted into supervisory practice? Are multiple authorities interpreting the same provision consistently?

The AI Act is again instructive because its implementation environment already contains formal legislation alongside Commission guidelines, codes of practice, technical materials, and national governance decisions. The Commission’s own enforcement framework cautions that its overview is informational and does not replace the Act itself.

A company that treats every new explanatory document as equivalent to a new legal rule will overreact.

A company that ignores how supervisory interpretation is maturing will react too slowly.

## 5. The transmission mechanism

Finally, ask how a potential problem reaches someone capable of acting on it.

This is often the least developed part of corporate regulatory analysis.

Enforcement does not always begin with a regulator independently detecting a violation. Information can arrive through complaints, whistleblowers, customers, competitors, employees, sector supervisors, certification bodies, distributors, platforms, procurement functions, or other intermediaries.

The AI Office, for example, launched a complaint tool for natural and legal persons, a whistleblower mechanism for people professionally connected to providers, and a channel through which downstream providers using general-purpose AI models can report alleged violations by model providers.

That changes the information environment around the rule even before a large body of enforcement precedent exists.

Academic work on regulatory intermediaries explains why this broader map matters. Kenneth Abbott, David Levi-Faur, and Duncan Snidal argue that regulation is often not a simple two-party relationship between regulator and regulated entity. Intermediaries can help interpret rules, monitor targets, provide expertise, and facilitate implementation.

For a business, that means practical regulatory pressure may arrive through a counterparty before it arrives through a formal enforcement notice.

A major customer may add contractual representations. A procurement team may require evidence that goes beyond the company’s existing process. A distributor may refuse a product category it considers difficult to diligence. An employee may use a new reporting mechanism. An auditor may ask for documentation because its own risk framework has changed.

The market clock can run ahead of the enforcement clock.

## Build an enforcement-readiness map, not an enforcement forecast

These five layers can be translated into a working instrument. For each material regulatory obligation, maintain an enforcement-readiness map containing:

- Legal obligation: What exactly applies, to whom, and from when?

- Responsible authority: Which institution has jurisdiction, and where is responsibility shared?

- Authority capability: What powers, people, tools, procedures, and technical infrastructure are visibly in place?

- Guidance maturity: Which interpretive materials exist, how authoritative are they, and what remains unresolved?

- Local variation: What differs across Member States, regions, sectors, or competent authorities?

- First-case hypothesis: Which fact patterns appear most likely to attract initial attention, and why?

- Affected counterparties: Which customers, suppliers, platforms, employees, auditors, certification bodies, or others can transmit regulatory pressure?

- Escalation signals: What observable development would justify greater executive attention, research, engagement, or operational preparation?

- Evidence date and owner: When was each judgment last verified, from which source, and who must update it?

The phrase first-case hypothesis is deliberate.

Companies should not pretend they can predict an authority’s first enforcement action. But they can form testable hypotheses based on factors such as observable harm, complaint pathways, regulator jurisdiction, technical detectability, available investigative tools, and the precedent value of a case.

OECD evidence provides a reason for thinking this way without implying certainty. Modern regulatory practice often uses risk and proportionality to prioritize limited enforcement resources; it also leaves officials varying degrees of discretion about the appropriate response to different kinds of non-compliance.

The map should therefore express uncertainty.

“Known,” “developing,” and “unresolved” are often more useful labels than a false 73% probability that an authority will act.

The objective is not to predict enforcement.

It is to know which assumptions your strategy depends on and what evidence would cause you to change them.

## Separate compliance duty from strategic timing

This framework can be misused if leaders ask the wrong question.

Suppose an obligation applies today, but the relevant national authority appears under-resourced and no cases have yet been announced.

The conclusion cannot be: “Enforcement looks distant, so compliance can wait.”

The correct separation is:

Compliance decision: What does the law require us to do now?

Strategic decision: Given the way enforcement capacity and market behavior are developing, where should we deploy additional management attention beyond baseline compliance?

That second question can change a great deal.

A company may decide to accelerate a technical control because major customers are beginning to ask for it.

It may engage an authority early because guidance remains ambiguous and the company’s business model presents an unusual application.

It may allocate more government-affairs resources to one jurisdiction because national governance has become operational there while another remains institutionally unsettled.

It may prepare evidence packages before investigations begin because the relevant authority has launched a complaint channel and obtained the tools to request detailed documentation.

Or it may decide that a heavily discussed policy development requires no extraordinary intervention beyond an already adequate compliance program.

Regulatory strategy is partly the discipline of not treating every policy signal as equally actionable.

A calendar cannot make those distinctions.

## Update from first signals, not first headlines

The earliest useful evidence of regulatory impact will often be mundane.

Watch the institution being built around the rule.

A new specialist unit is staffed. A technical procurement is launched. A complaints portal starts accepting reports. A regulator publishes a decision tree or evidence template. Sector regulators clarify their division of responsibility. A joint investigation is announced. A customer inserts a new clause into tenders. An industry intermediary changes its certification process. Initial cases reveal what facts authorities consider material.

Each signal changes a different part of the enforcement-readiness map.

The Commission’s August 2026 AI Act rollout illustrates the point. The launch of complaint and whistleblower channels is an enforcement signal even before those channels generate public cases. National designation and resourcing are signals. New evaluation capacity is a signal. A shift from draft to final guidance is a signal.

This leads to a more useful recurring executive review than “What regulations are coming next quarter?”

For the few rules capable of changing the economics or operating model of the business, ask:

### What changed in the path from legal text to practical effect?

Did jurisdiction become clearer?

Did an authority gain capacity?

Did guidance resolve an ambiguity?

Did a new complaint mechanism lower the cost of surfacing alleged violations?

Did a national implementation decision alter the route of supervision?

Did counterparties begin acting?

And, most important: Does any of that change what the company should do?

## Where Enquire fits: keep official rules and operating evidence separate but connected

Research infrastructure can help with this problem only if it respects the distinction between authoritative legal information and field intelligence.

Enquire’s current positioning combines structured AI research with expert perspective for investment, consulting, and policy teams, while preserving research context as questions evolve. Its product materials describe multiple expert perspectives, structured AI synthesis, and an “Evolving Research Context” that carries prior work into subsequent inquiries.

For regulatory strategy, that architecture can support two different evidence streams.

The first is authoritative: regulation, official guidance, regulator announcements, national implementation materials, and other primary sources.

The second is contextual: what regional compliance teams are encountering, how counterparties are responding, where implementation capability appears to be building, and which operational questions practitioners still find unresolved.

Expert input belongs in the second category. It can reveal implementation context and generate hypotheses worth verifying. It should not be treated as a substitute for legislation, official regulatory interpretation, or legal advice.

The value is in preserving the connection between the two.

A regulatory team can begin with the formal obligation, identify uncertainties about its operating environment, gather several informed perspectives around those uncertainties, and then update the map as official practice develops. Enquire’s current materials explicitly emphasize preserving context over time and examining questions from multiple perspectives rather than treating each inquiry as a fresh search.

That is useful for a regulatory regime precisely because enforcement reality is not fixed on the day the regulation is published.

## The regulation has a date. The business effect has a pathway.

None of this makes the regulatory calendar unimportant.

Companies still need dates. Legal teams still need to establish which obligations apply and when. Implementation programs need milestones. Executives need to know when non-compliance would put the organization outside the law.

But that is compliance infrastructure, not the whole strategy.

The regulatory strategist has a different task: trace the mechanisms through which a formal requirement becomes an operating constraint.

Which institution owns it?

Does that institution have the capability to act?

What interpretive layer is still forming?

How does a concern reach the authority?

Where does national or sectoral implementation differ?

Which counterparties may effectively enforce a higher standard through commercial behavior?

And what observable signal would justify changing the company’s posture?

The best regulatory teams will not try to predict every enforcement decision. They will build a map that tells management where uncertainty sits and how to recognize when it is resolving.

The next time a major rule appears on the corporate calendar, the important question is therefore not just, “When does this take effect?”

It is:

If this rule became operational around our business tomorrow, through which actor would we feel it first, and what evidence do we have for that answer?

## Sources and further reading

- European Commission, “Commission starts enforcing AI Act rules and new transparency requirements on 2 August” digital-strategy.ec.europa.eu

- European Commission, “The enforcement framework of the AI Act” digital-strategy.ec.europa.eu

- European Commission, “Governance and enforcement of the AI Act” digital-strategy.ec.europa.eu

- OECD, Regulatory Policy Outlook 2025: Regulating for effectiveness oecd.org

- Kenneth Abbott, David Levi-Faur, and Duncan Snidal, “Theorizing Regulatory Intermediaries: The RIT Model” ora.ox.ac.uk

- Bundesnetzagentur, “Bundesnetzagentur takes on key role in implementation of AI Act” bundesnetzagentur.de

- Spanish Congress, draft Organic Law on the proper use and governance of artificial intelligence congreso.es

- Enquire, current product overview enquire.ai

### The Thesis Is a Hypothesis, Not a Narrative — https://enquire.ai/blog/the-thesis-is-a-hypothesis-not-a-narrative

Author: Enquire Team · Published: 2026-03-26 · Topic: Public Equities

An investment narrative can explain why a position makes sense. It is much worse at telling a team when that view should change. Before capital and identity attach to a thesis, decompose it into assumptions, evidence tests, and explicit…

An investment narrative can explain why a position makes sense. It is much worse at telling a team when that view should change. Before capital and identity attach to a thesis, decompose it into assumptions, evidence tests, and explicit rules for updating conviction.

“Is the thesis still intact?”

It is one of the most common questions in portfolio reviews, and one of the least precise.

A company misses a quarterly target but maintains guidance. A private investment gains customers more slowly than expected but improves unit economics. A macro position moves against the portfolio while one of its underlying indicators strengthens. The team gathers, revisits the original rationale, and asks whether anything fundamental has changed.

Usually, the answer arrives as another narrative.

The analyst explains why the disappointing evidence may be temporary. The portfolio manager introduces a new development that supports the longer-term view. Someone observes that valuation has become more attractive. By the end of the discussion, the original thesis may sound different from the one that justified the investment, yet the conclusion remains the same: intact.

The problem is not that stories are inherently bad. Investing requires causal reasoning, and a coherent narrative is one of the best ways to communicate a complex view.

The problem is using the narrative as the monitoring system.

Stories are built for coherence. Monitoring systems need to preserve uncertainty, contradictions, and changes of mind. A thesis that exists primarily as prose can absorb new information almost indefinitely because the explanation can be rewritten faster than the investment can be falsified.

A better discipline is to treat the thesis as what it really is: a set of hypotheses about how the world works.

Before committing capital, teams should separate those hypotheses into their critical assumptions, assign an explicit level of confidence, identify evidence that would support or weaken each one, specify leading signposts and review thresholds, and record who is responsible for revisiting them.

The practical output is not another investment memo. It is a living thesis register.

Its purpose is not to mechanize portfolio management. It is to make changes in conviction visible before hindsight, price moves, and ownership of the position rewrite the reasons the investment was made.

## Narratives explain. Hypotheses can be tested.

An investment narrative compresses a great deal of reasoning.

A software company will sustain growth because its product is becoming embedded in customer workflows. A roll-up will create value because the fragmented market permits continued acquisitions at attractive multiples. A bank will rerate because credit normalization will reveal structurally higher earnings power.

Each statement can be perfectly sensible. Each also hides several separate propositions.

For the software company, customer dependence must actually be increasing. Higher dependence must translate into retention or pricing power. Competitive products must not erase the advantage. The resulting economics must persist long enough to justify the valuation paid.

If those propositions remain bundled inside one story, evidence against one can be offset rhetorically by evidence for another. Retention weakens, but new-logo growth is strong. Pricing disappoints, but usage is rising. The original source of value can quietly migrate.

This is not merely a problem of poor discipline. Commitment itself can affect subsequent judgment.

In Barry Staw’s classic experiment on escalating commitment, 240 business students participated in a simulated business investment decision. Participants who were personally responsible for an earlier choice and then received negative results subsequently committed more resources to that course of action than other groups. The experiment was stylized and should not be treated as a direct model of professional portfolio managers, but it demonstrated a mechanism investment organizations should take seriously: adverse evidence does not necessarily weaken commitment when the decision maker owns the prior choice.

That is why “remain intellectually flexible” is weak advice. Flexibility is hardest precisely when it is needed.

A more robust response is structural: externalize the assumptions before ownership hardens.

MIT CISR makes a similar distinction in its work on organizational learning. Ross and Fonstad argue that large initiatives often embed a whole set of assumptions inside what appears to be one hypothesis. Breaking those assumptions apart makes it possible to conduct smaller tests that confirm or disconfirm specific beliefs rather than waiting for the entire initiative to succeed or fail.

The same principle applies to investing.

The question is not merely, “Is our thesis right?”

It is:

Which propositions have to be right for the investment to work, and what evidence would cause us to change our confidence in each one?

## Decompose the thesis before commitment

The most important work on a living thesis happens before the trade, acquisition, or funding decision.

At that point, the team still has an unusual advantage: it can define what future evidence should mean before knowing what that evidence will be.

Start with the causal chain.

Suppose the proposition is that a company’s margins will converge toward peers. That is a forecast, not yet a thesis. The causal claim might be that current margins are depressed by investments whose costs will grow more slowly than revenue. That explanation in turn depends on assumptions about customer acquisition, retention, pricing, support costs, operating leverage, competitive responses, and management’s willingness to let incremental revenue reach the bottom line.

Those assumptions do not deserve equal attention.

Some may be observable and low consequence. Others may carry most of the expected return. A useful thesis register therefore forces the team to identify the small number of assumptions for which being wrong would materially change valuation, position size, or the decision to own the asset at all.

The exercise also separates the inside view from the outside view.

Kahneman and Lovallo describe the inside view as forecasting from the details and scenarios of the case itself, while the outside view begins with the outcomes of a relevant reference class. Their argument originated in project forecasting rather than securities analysis, but the mechanism transfers naturally: an especially persuasive company-specific narrative should not make the investor forget what has historically happened to comparable businesses, transactions, technologies, or turnarounds.

A useful pre-investment record might therefore distinguish between:

- What must be true: the causal assumptions that drive the investment.

- What is currently observed: facts available today rather than forecasts or interpretations.

- What the base rate says: relevant outcomes outside the specific case.

- What would discriminate: future evidence that should look different if the assumption is wrong.

- What the investment implies: how changes in the assumption should affect expected return, valuation, sizing, or ownership.

This is more demanding than attaching a “key risks” section to the end of an investment memo.

A risk factor can exist without threatening the causal thesis. A falsifiable assumption must connect directly to the logic that generates the return.

That distinction also reinforces a basic professional obligation. CFA Institute’s current Standard V(A) requires members and candidates to exercise diligence, independence, and thoroughness and to maintain a reasonable and adequate basis supported by appropriate research and investigation for investment recommendations and actions. It does not prescribe a thesis register, but the standard emphasizes process and evidentiary basis rather than confidence alone.

The practical implication is simple: conviction should be decomposable.

If no one can identify the assumptions whose failure would reduce conviction, “high conviction” is describing an attitude, not a research conclusion.

## Decide what new evidence should mean before it arrives

The most difficult part of updating is not collecting new information. It is deciding how much weight the information deserves.

Investors face two symmetrical errors.

One is rigidity: the team explains away every adverse observation because a quarter is noisy, management needs more time, the market misunderstands the opportunity, or the original thesis is “long term.”

The other is hyperreactivity: every data release, channel check, price move, or management comment causes conviction to lurch.

Research on real-world forecasting illustrates the balancing problem. Atanasov, Witkowski, Ungar, Mellers, and Tetlock analyzed more than 400,000 probability forecasts covering almost 500 geopolitical questions in a four-year forecasting tournament. More accurate forecasters tended to update frequently but in relatively small increments, while weaker forecasters were more prone either to confirm their prior views or to make infrequent, large revisions. The domain is forecasting rather than investing, and association should not be confused with a universal trading rule. But the finding is useful because it rejects both caricatures of good updating: neither stubborn conviction nor constant reversal performed best.

Investment teams need the equivalent of an update function.

For each critical assumption, specify a leading signpost: an observable that provides information about the underlying mechanism before the final financial outcome is known.

Then distinguish a signpost from a threshold.

A signpost might be customer retention. The hypothesis concerns whether product integration is deepening. The team should decide in advance what pattern in retention would materially change its confidence: one weak monthly observation may be noise; deterioration across multiple cohorts, accompanied by a specific change in customer behavior, may be evidence against the mechanism.

The threshold is therefore not simply “retention below 90%.”

It is an answer to a harder question:

What amount and quality of evidence would be surprising enough under our current hypothesis that we should reconsider it?

McKinsey makes a related recommendation in its work on strategy under uncertainty: organizations should track the assumptions underlying a plan over time and establish explicit trigger points for revisiting decisions as new information becomes available. The authors note that assumptions often fade from memory even while variance against budgets remains closely monitored.

Investment teams should go one step further and distinguish an evidence threshold from a decision threshold.

The first says: this evidence is strong enough that our belief should change.

The second says: conviction has changed enough that the portfolio decision should change.

Those are not the same thing.

A thesis can weaken while a security simultaneously becomes cheaper. A negative operating development may reduce estimated intrinsic value but leave expected return attractive after a much larger price decline. A venture thesis can weaken without creating an opportunity to exit. A private-equity team may learn that one underwriting assumption is wrong while determining that a different value-creation path is sufficiently attractive to justify continued ownership.

The register should make those changes visible rather than forcing every piece of information into a binary “hold or sell” trigger.

## A threshold should trigger judgment, not replace it

This distinction addresses the strongest objection to a formal thesis register.

Many important drivers are not measurable with precision. Data arrive with error. Leading indicators change meaning across regimes. Management teams alter behavior. Markets incorporate information that the investor cannot directly observe. Strict rules can make an organization act confidently on weak signals simply because a number crossed a line.

That would substitute false precision for narrative flexibility.

The answer is to design thresholds as review obligations, not automatic commands.

Crossing a threshold should require the team to reopen the relevant assumption, examine whether the signal is reliable, search for corroborating and disconfirming evidence, and explicitly record the resulting change, or non-change, in conviction.

Some evidence will justify a large revision. Most will not.

The register should also distinguish between what is unknown and what is unknowable at useful precision. Forcing a numeric probability onto every qualitative judgment can produce the appearance of rigor without additional information.

Probabilities are most helpful where the team can define the event clearly and expects to revisit the estimate. Elsewhere, calibrated confidence bands may be more honest. The important discipline is preserving the prior judgment so that the direction and magnitude of the update remain observable.

A register is therefore not an algorithm for investing.

It is a constraint against unconsciously changing the rules after observing the result.

## Do not let a broken thesis become a new thesis without saying so

The most revealing moment in a portfolio review often occurs when an original assumption fails but the position still appears attractive.

Imagine that an investment was initially underwritten on accelerating market growth. Growth disappoints. But margins improve substantially faster than expected, and the valuation falls.

It may be perfectly rational to continue holding, or even to add.

But the team should not describe the original thesis as “intact.”

It has discovered a different investment.

This distinction matters because otherwise a portfolio can accumulate what might be called thesis drift: the reasons for owning an asset continuously change while the organization records an uninterrupted line of conviction.

The danger is not changing your mind. Changing your mind is the point.

The danger is changing the rationale without recognizing that a fresh underwriting decision has occurred.

A living register should therefore contain a status beyond “confirmed” and “disconfirmed”: re-underwrite.

That status is appropriate when a critical link in the causal chain has failed but new information creates a materially different source of expected return.

The team then asks the question it would ask if it did not already own the position:

Given what we now know, would we initiate this investment today, for these new reasons, at this price and size?

That question cannot completely neutralize ownership effects. But it makes the transition from old thesis to new thesis explicit.

This is where an outside view becomes useful again. When the causal explanation changes, the relevant reference class may change with it. A growth investment turning into a margin-recovery investment should not automatically inherit the base rates, horizon, or risk budget of the original idea.

## Separate process quality from investment outcome

A second reason to preserve the original thesis is that realized returns are an unreliable record of decision quality.

Baron and Hershey demonstrated this in five experiments in which participants evaluated decisions made under uncertainty. Even when participants understood that the original decision maker had the same ex ante information in the good-outcome and bad-outcome versions, they tended to judge the decision or decision maker more favorably when the realized outcome was favorable.

That finding is particularly uncomfortable for investment organizations because the P&L is both indispensable and noisy.

A position can make money for reasons the investor did not anticipate. A strong process can produce a loss when a low-probability adverse event occurs. A weak thesis can survive for years because the relevant falsifying evidence has not arrived. A correct directional forecast can still be a bad investment if it was already embedded in the price.

The answer is not to ignore returns.

It is to evaluate two things separately:

Was the original decision reasonable given what was knowable at the time?

And:

## What did subsequently happen?

The living thesis register supplies the first record.

That record is useful at the portfolio-review level as well as for individual investments. CFA Institute Research Foundation’s 2026 work on investment committees argues that unstructured deliberation can weaken accountability when individual inputs are not explicitly recorded and therefore cannot be evaluated later. The monograph proposes a much more formal portfolio-aggregation model than most firms will want to adopt, and part of its analysis relies on simulated committee settings, but the accountability principle is relevant: ex ante judgments need to exist in inspectable form if an organization wants to learn from them.

Without such a record, postmortems tend to become storytelling exercises.

The winner “worked because our thesis was right.”

The loser “failed because conditions changed.”

Both explanations may be true. Neither is a process evaluation.

## Run the living-thesis review

The register itself should be deliberately small. If maintaining it requires rewriting the investment memo every week, teams will stop using it.

For each assumption material enough to alter the portfolio decision, record seven fields:

- Assumption: the causal proposition that must hold, stated precisely enough to be wrong.

- Current confidence: the team’s present degree of belief, using a probability or calibrated band appropriate to the evidence.

- Supporting and disconfirming evidence: what has arrived on both sides since the previous review, with provenance where material.

- Leading signpost: the observable most capable of providing information about the assumption before the final outcome.

- Decision threshold: the condition that requires formal reconsideration and the portfolio decisions it could affect.

- Owner: the person accountable for updating the evidence, not for defending the assumption.

- Review date: when the assumption will be reconsidered even if no threshold has fired.

The review meeting should begin with <mark>what changed in the register</mark>, not with a fresh retelling of the thesis.

Which assumptions moved?

What new evidence caused the movement?

Which assumption has accumulated contradictory evidence without a corresponding change in confidence?

Did a threshold fire?

Has the causal chain changed enough that the investment requires re-underwriting?

Which material unknown is receiving no new information and therefore deserves additional research?

The result is not a perfectly Bayesian portfolio manager. Human judgment will still determine how evidence is interpreted, how much weight to place on management behavior or qualitative field research, and when a noisy signal has become meaningful.

But the team gains something valuable: a visible history of its own reasoning.

The investment thesis stops being a document that was written once.

It becomes a record of what the team believed, why it believed it, what evidence arrived, and how that evidence changed, or failed to change, the decision.

## Where Enquire fits: preserve the reasoning, not just the latest answer

A living-thesis process creates an information-management problem of its own.

Evidence accumulates across earnings calls, expert conversations, diligence, portfolio reviews, adjacent-company research, regulatory developments, and internal debate. If the original context fragments across notes and meetings, the team eventually knows its latest opinion but loses the path by which it arrived there.

Enquire’s current capital-markets offering is explicitly oriented toward preserving that context. Its capital-markets page describes workflows for pressure-testing investment narratives, identifying assumptions that must hold, tracking how thinking evolves over time, and maintaining continuity across earnings, diligence, and portfolio reviews.

Its current product page also describes an “Evolving Research Context” in which context is preserved across inquiries as new information and expert insights emerge, alongside individual and organization-level Research Centers.

That can support a living-thesis workflow if the technology is used as a memory and research layer rather than as an oracle.

The initial assumptions can provide structure for subsequent research. New expert evidence can be gathered against specific unresolved questions. Contradictory perspectives can be retained rather than collapsed into a single synthesis. Later reviews can revisit earlier analysis with its original context intact.

The important outcome is not that a system determines whether conviction should be 62% rather than 58%.

It is that the team does not have to rely on memory to reconstruct what it once believed and why.

## Conviction should be allowed to leave evidence behind

A coherent investment narrative remains valuable.

Analysts need to explain opportunities. Portfolio managers need to make choices. Committees need a compressed account of why an investment belongs in the portfolio.

But compression should come at the end of the reasoning process, not replace it.

The underlying investment thesis should remain decomposed: a set of causal assumptions with identifiable evidence, confidence levels, signposts, and conditions for revision.

That changes what it means to have conviction.

Conviction is no longer the ability to defend the same story through volatility.

It is the willingness to state precisely what you believe, expose that belief to evidence, and revise it without quietly rewriting the past.

A good investment process should therefore be able to answer a question more demanding than “Is the thesis intact?”

What do we believe now that we did not believe when we invested, and what, exactly, changed our mind?

## Sources and further reading

- Pavel Atanasov et al., “Small Steps to Accuracy: Incremental Belief Updaters Are Better Forecasters,” Organizational Behavior and Human Decision Processes sciencedirect.com

- Jonathan Baron and John C. Hershey, “Outcome Bias in Decision Evaluation,” Journal of Personality and Social Psychology bear.warrington.ufl.edu

- Barry M. Staw, “Knee-Deep in the Big Muddy: A Study of Escalating Commitment to a Chosen Course of Action” web.mit.edu

- Jeanne W. Ross and Nils O. Fonstad, “Learn from Hypotheses, Not Failures,” MIT CISR cisr.mit.edu

- Chris Bradley, Martin Hirt, and Sven Smit, “How to Confront Uncertainty in Your Strategy,” McKinsey mckinsey.com

- Dan Lovallo and Daniel Kahneman, “Delusions of Success: How Optimism Undermines Executives’ Decisions,” Harvard Business Review store.hbr.org

- CFA Institute, Standard V(A): Diligence and Reasonable Basis cfainstitute.org

- Bernhard Scherer, Investment Committees: Governance and Design Choices, CFA Institute Research Foundation rpc.cfainstitute.org

- Enquire, Capital Markets enquire.ai

### The Investment Committee Needs a Rival Case — https://enquire.ai/blog/the-investment-committee-needs-a-rival-case

Author: Enquire Team · Published: 2026-03-05 · Topic: Private Equity

A skeptic reacting to the sponsor’s thesis is not the same as independent challenge. For consequential investments, committees should build a rival explanation before discussion begins, then make the evidence discriminate between them.

A skeptic reacting to the sponsor’s thesis is not the same as independent challenge. For consequential investments, committees should build a rival explanation before discussion begins, then make the evidence discriminate between them.

By the time an investment reaches committee, one side of the argument usually has a formidable advantage.

The deal team, analyst, or portfolio manager has spent weeks building the case. It has chosen the comparables, refined the model, spoken with management or industry contacts, reconciled inconvenient data, and turned a messy set of observations into a coherent story. The investment committee sees the output of that accumulated work.

Then someone is asked to challenge it.

The imbalance is obvious. One side has a research program. The other has a meeting role.

Calling that person a devil’s advocate does not fix the problem. The challenger is still reacting to the sponsor’s evidence, terminology, causal model, and definition of the important uncertainties. Even a talented skeptic can spend the meeting debating whether the sponsor has the right answer to the wrong question.

For high-consequence investments, the stronger design is to create two independently researched cases before the committee deliberates.

The second case need not be bearish. It need not reach the opposite recommendation. It should instead offer the strongest plausible rival explanation of the same facts: a different account of what drives the asset, what the market is pricing, which evidence matters, and what would have to happen for the sponsor’s thesis to be wrong.

The committee can then do something more useful than “challenge the deal.” It can determine which observations discriminate between competing explanations.

That is a different form of dissent. And it requires a different investment-committee process.

## The sponsor always has a head start

An investment thesis does more than summarize information. It determines what subsequent information means.

Suppose a private-equity team believes a target’s slowing growth is primarily a temporary sales-execution problem. Customer attrition becomes evidence of poor account management. Weak bookings become evidence of an immature sales organization. Competitor wins become a hiring problem. The facts may all be accurate, but once the causal frame is established, each new fact can be interpreted inside it.

A rival case might start elsewhere: perhaps the company is not suffering from temporary execution problems but from a weakening product advantage. The same attrition, bookings, and competitive losses now point toward a different mechanism, with very different implications for underwriting and post-close value creation.

This is why investment challenge cannot be reduced to a checklist of biases. The problem is structural: the first coherent explanation organizes the search that follows.

Research on judgment and group decision making supports that concern, although none of it should be treated as a laboratory replica of an investment committee. In a 2011 experiment involving 144 participants performing estimation tasks, Lorenz and colleagues found that exposure to others’ estimates could reduce the diversity of judgments without producing a corresponding improvement in collective accuracy. Their broader warning was that visible convergence can make a group appear more certain without making it more correct.

More directly relevant to committee design, Minson, Mueller, and Larrick found across collaborative estimation experiments that discussion could either improve or degrade judgment. A critical condition was timing: when participants first formed independent estimates, subsequent discussion could help eliminate large errors; when they did not, discussion narrowed the range of answers considered and substantially hurt accuracy in the tasks studied.

The investment implication is not that IC members should avoid talking to one another. It is nearly the opposite.

## Discussion becomes more valuable when there is something independent to discuss.

If every member encounters the opportunity through the sponsor’s completed narrative before forming a view, apparent agreement after a vigorous debate may contain less independent information than the room assumes.

A new 2026 CFA Institute Research Foundation monograph makes a related argument specifically about investment committees. Bernhard Scherer describes traditional discussion-based ICs as vulnerable to speaking-order effects, hierarchy, narrative coherence, and the loss of identifiable individual views during consensus formation. He recommends, among other measures, obtaining independent judgments before consensus.

Scherer’s quantitative committee simulations should be interpreted conservatively: they use an LLM-based “narrative lab,” not observed field decisions, and the monograph itself emphasizes the difficulty of obtaining the necessary counterfactual data. But the governance diagnosis is useful. An IC cannot preserve independent information that was never elicited in the first place.

## Devil’s advocacy is not independent evidence

There is a respectable intuition behind assigning someone to oppose the investment.

Dissent can expand information search. Charlan Nemeth’s work on minority influence argues that exposure to genuine disagreement can stimulate people to consider more alternatives and search more broadly, even when the dissenter is ultimately wrong.

But that literature also exposes the weakness in ceremonial devil’s advocacy: being instructed to disagree is not the same thing as arriving at disagreement through independent evidence.

In an experiment with 201 employees and managers, Schulz-Hardt, Jochims, and Frey compared groups whose members genuinely entered with different preferred alternatives against groups using an assigned devil’s advocate. Genuine preference heterogeneity was more effective at reducing confirmatory information search than the contrived dissent intervention.

Nemeth, Brown, and Rogers similarly found in experimental work that authentic minority dissent generated better effects on the thinking they measured than several versions of role-played devil’s advocacy.

These studies have important boundaries. They examine relatively constrained experimental tasks, not multibillion-dollar acquisitions or concentrated public-equity positions. And contrived dissent is not useless: other group-decision research finds that structured disagreement can sometimes improve information search and decision quality.

The practical distinction is therefore not dissent versus no dissent.

It is reactive dissent versus independently generated information.

A devil’s advocate who receives the sponsor’s 80-page memo on Monday and is told to “push hard” on Tuesday is operating downstream of the sponsor’s framing. A rival-case team that starts with the investment question and conducts its own research has at least a chance of finding a different frame.

That difference is especially important in investing because the sponsor is not merely another committee participant. The sponsor has often accumulated informational, emotional, and reputational ownership of the proposal. Walking away can mean admitting that weeks of work produced no transaction or position.

A good IC should not expect personal willpower to neutralize that asymmetry. It should design around it.

## Build two explanations, not a bull case and a bear case

A two-case protocol can easily become theater of its own.

If Team A is instructed to produce the bullish case and Team B the bearish case, both can simply construct advocacy briefs. The committee receives two polished collections of selectively chosen evidence and learns little about which explanation is more likely.

The purpose is not to force symmetry. It is to produce competing causal models.

Imagine a long-only manager considering a concentrated position in a company whose margins are substantially below those of its peers.

The sponsor’s case might be:

Margins are temporarily depressed because management is investing ahead of growth. Revenue scale will reveal operating leverage, taking earnings materially above consensus.

The rival case might be:

The margin gap is structural because the company requires greater customer-acquisition and support spending than peers. Revenue growth will not produce the assumed incremental margins.

Both cases accept much of the same historical data. The disagreement is over what generates it.

The useful IC question is no longer “Are we comfortable with the margin assumptions?”

It becomes: What evidence should look different if one mechanism is true rather than the other?

Customer acquisition costs by cohort might discriminate. So might support intensity among mature customers, incremental gross margin, sales productivity after territory maturation, or the experience of former operators who have observed how the cost base behaves as the business scales.

The same logic applies in private markets. A sponsor may argue that a company is taking share because its product is superior. A rival explanation may attribute the growth to temporarily aggressive pricing or channel incentives. Rather than exchanging opinions about competitive quality, the committee can seek evidence that distinguishes product pull from commercial subsidy.

The rival case has done its job even if the sponsor wins decisively.

The objective is not balance. It is identification: finding evidence capable of telling the committee which explanation better describes the world.

That makes the second case fundamentally different from a risk section in an investment memo. A risk section asks what could damage the preferred thesis. A rival case asks whether the preferred thesis is the right explanation in the first place.

## A four-step protocol for consequential decisions

The full protocol should be reserved for decisions important enough to justify duplicated effort. When it is used, four disciplines matter.

## 1. Separate the research paths before the narrative hardens.

Give two teams, or for smaller organizations two researchers, an identical decision question and access to the relevant evidence universe.

The sponsor develops its investment case normally. The rival path is tasked with finding the strongest plausible alternative explanation, not rebutting individual slides in the sponsor’s presentation.

Some common factual infrastructure can be shared to avoid pointless duplication. But the rival researchers should make their own decisions about which comparables matter, which experts to seek, which historical analogues are relevant, and which causal mechanism best fits the evidence.

The separation need not be absolute. Its purpose is to protect enough independence to permit a genuinely different model of the situation to emerge.

This is also where an outside view can be useful. Lovallo, Clarke, and Camerer studied private-equity investment judgments and found evidence that forming a broader reference class of analogous cases could improve forecasting relative to relying on a few salient analogies familiar to the decision maker.

The rival team should therefore be allowed to challenge not only the sponsor’s forecast, but its chosen reference class.

## 2. Require discriminating evidence from both cases.

Every case should identify a small number of observations for which the rival explanations make meaningfully different predictions. For each one, ask:

- If Case A is correct, what should we expect to observe?

- If Case B is correct, what should we expect instead?

- What evidence do we currently have?

- What evidence could we obtain before the decision?

- What future signal would cause us to update after investing?

This prevents the two-case process from degenerating into an accumulation of arguments.

It also changes how primary research is commissioned. Instead of asking ten experts, “How attractive is this market?”, one research path may need to test whether customers view the product as technically differentiated while the other tests whether adoption is actually driven by switching incentives. The same respondents should not automatically be used for both questions.

The goal is not expert consensus. It is information gain.

### 3. Elicit an independent first judgment from every IC member.

Before the group discussion begins, each voting member should record a view.

The exact instrument can vary by asset class: invest or pass; expected return; probability of thesis success; preferred position size; valuation range; or a short distribution across outcomes. Members should also identify the assumption they believe has the greatest ability to change their judgment.

The scores are not the decision. They are an information-preservation device.

The Minson-Mueller-Larrick findings make the logic concrete: independent estimates preserve a broader set of initial judgments before discussion begins compressing them. CFA Institute’s 2026 work likewise recommends having members independently assess the same case before declaring that an IC has a collective view.

For an IC chair, the dispersion is itself useful information. A 7–0 vote after discussion can look like overwhelming conviction. Seven materially different initial views that converge after the CIO speaks tell a different governance story.

### 4. Converge only after the alternatives are visible.

Only now should the committee attempt synthesis.

The chair’s job is not to give equal airtime to bullish and bearish speakers. It is to organize the discussion around the disagreements that could actually change the investment decision.

Where do the cases agree?

Where do they assign different meanings to the same evidence?

Which disagreement comes from different forecasts, and which comes from different objectives or risk tolerances?

Which observation could resolve the largest amount of uncertainty?

What did individual members change their minds about after hearing the competing cases, and why?

The committee should finish with one portfolio decision, not permanent philosophical pluralism. Deliberation has value: experimental research has found circumstances in which structured small-group discussion improves aggregate judgment, particularly when participants bring information or estimates worth combining.

Independence and discussion are therefore complements, not substitutes.

## First preserve the alternatives. Then exploit the group.

## Not every investment deserves two research teams

The strongest objection to this protocol is practical.

Duplicating research costs money. It consumes scarce senior attention. It can delay a decision. It can also manufacture false balance: when one explanation is overwhelmingly supported by evidence, an institutional requirement to construct an equal opposing case can elevate fringe arguments that deserve little weight.

Those are real costs.

A full rival-case process is most defensible when several conditions coincide: the decision is consequential; reversal will be costly; the position is concentrated; the evidence is incomplete; the sponsor has invested substantial time or reputation; and the thesis depends heavily on a narrative about the future rather than on a readily observable fact.

A platform acquisition deserves more independent challenge than a 30-basis-point liquid position.

A venture investment built around uncertain technological adoption deserves more than a small follow-on round where the critical milestones are already measurable.

A concentrated long position whose valuation depends on one non-consensus causal thesis deserves more challenge than a diversified exposure driven principally by systematic factors.

For lower-stakes decisions, lighter mechanisms are appropriate.

A premortem, asking the team to imagine that the investment has failed and explain why, can surface risks that members may otherwise hesitate to raise. An outside-view exercise can test the sponsor’s base rates. Independent member scoring can preserve disagreement at very little cost. A designated challenger can still expose neglected weaknesses.

These are useful tools.

They are simply not substitutes for an independently researched case when the decision warrants one.

## The committee should spend less time debating and more time discriminating

One implication follows from the two-case design that is easy to miss.

A better IC may actually need less open-ended debate.

Investment organizations often equate a long discussion with rigorous challenge. But once rival cases have been researched properly, much of the committee’s work can become more precise.

The sponsor believes retention will improve because implementation problems are being fixed. The rival believes attrition reflects a product gap.

What measure distinguishes them?

The sponsor believes a market slowdown is cyclical. The rival believes the category is approaching saturation.

What leading indicator should differ?

The sponsor believes management guidance is conservative. The rival believes management is extrapolating a temporarily favorable cohort.

Which historical or cross-sectional comparison would separate those interpretations?

This style of discussion also makes post-investment learning more useful. The original cases and their discriminating predictions can be revisited after six or twelve months. The committee does not have to reconstruct what “the thesis” was from a retrospective narrative.

It can ask which model predicted the evidence that subsequently arrived.

That matters because decision processes are otherwise remarkably good at rewriting history.

McKinsey research on 1,048 major corporate decisions found a strong association between decision-process practices, including explicit consideration of contradictory viewpoints and major uncertainties, and decision outcomes. The study was survey-based and observational, so it does not establish that those practices caused better performance. But its central implication remains relevant: strong analysis does not guarantee a strong decision process.

IC design deserves the same seriousness firms apply to financial modeling.

## Where Enquire fits: create independence before asking for synthesis

The operational difficulty in a two-case protocol is obvious: independent research is expensive precisely because it duplicates search.

That is where research infrastructure can help, provided it preserves rather than erases differences between the cases.

Enquire’s current capital-markets offering combines structured research with expert perspective and explicitly supports teams in identifying where views diverge, pressure-testing investment narratives, and surfacing assumptions that may be mispriced. Its product also supports gathering responses from multiple vetted experts and AI-led asynchronous interviews.

For a rival-case process, the useful application is not asking one system to generate a bullish paragraph and a bearish paragraph.

It is maintaining separate research paths.

One path can investigate the sponsor’s causal explanation and seek evidence that would validate or falsify it. Another can research a rival mechanism, drawing on different operators, customer perspectives, industry specialists, or adjacent-market evidence. The resulting expert perspectives can then be compared to see where independent sources converge and where the divergence itself deserves further investigation.

AI can make it cheaper to maintain those parallel searches. It should not prematurely merge them.

The committee benefits from synthesis only after it has preserved something worth synthesizing.

## The purpose of dissent is not disagreement

Investment committees do not need more people performing skepticism.

They need a process that gives a plausible alternative enough time, evidence, and independence to become genuinely informative.

Sometimes the rival case will overturn the sponsor’s thesis. More often, it may strengthen the investment by identifying the precise assumption that needs additional diligence, changing the price at which the risk becomes attractive, reducing position size, or establishing a clearer monitoring trigger after investment.

And sometimes it will lose completely.

That is not wasted work.

A committee that considers two serious explanations and finds one overwhelmingly better supported has learned something quite different from a committee that begins with one explanation, challenges it vigorously, and survives the challenge.

The first has compared models of the world.

The second has tested the sponsor’s skill at defending one.

For the investments that matter most, the question for the IC chair should therefore come before anyone enters the room:

What is the strongest independently researched explanation under which this investment is a mistake, and what evidence would let us tell the two cases apart?

## Sources and further reading

- Bernhard Scherer, Investment Committees: Governance and Design Choices, CFA Institute Research Foundation rpc.cfainstitute.org

- Julia Minson, Jennifer Mueller, and Richard Larrick, “The Contingent Wisdom of Dyads,” Management Science pubsonline.informs.org

- Stefan Schulz-Hardt, Marc Jochims, and Dieter Frey, “Productive Conflict in Group Decision Making” psych.uni-goettingen.de

- Charlan Nemeth, “Minority Influence Theory,” UC Berkeley irle.berkeley.edu

- Jan Lorenz et al., “How Social Influence Can Undermine the Wisdom of Crowd Effect,” PNAS pnas.org

- Dan Lovallo, Carmina Clarke, and Colin Camerer, “Robust Analogizing and the Outside View,” Strategic Management Journal sms.onlinelibrary.wiley.com

- Dan Lovallo and Olivier Sibony, “The Case for Behavioral Strategy,” McKinsey mckinsey.com

- Gary Klein, “Performing a Project Premortem,” Harvard Business Review hbr.org

- Enquire, Capital Markets enquire.ai

### When the First Draft Is Cheap, Evidence Becomes Premium — https://enquire.ai/blog/when-the-first-draft-is-cheap-evidence-becomes-premium

Author: Enquire Team · Published: 2026-02-12 · Topic: Consulting

AI is compressing retrieval and synthesis faster than it is eliminating uncertainty. Consulting firms should redesign delivery around verifiable evidence, field validation, and named accountability, not output volume.

AI is compressing retrieval and synthesis faster than it is eliminating uncertainty. Consulting firms should redesign delivery around verifiable evidence, field validation, and named accountability, not output volume.

A consulting team can now move from a broad question to a respectable first synthesis in minutes. Market structure, competitor positions, regulatory themes, likely strategic options: an AI system can assemble the analytical map before an analyst would once have finished collecting sources.

That is a genuine productivity gain. It also creates a less comfortable question.

If every credible firm can produce a competent first draft quickly, what is the scarce part of the work?

Increasingly, it is not the draft. It is establishing which parts of the draft deserve to be believed.

For consulting firms, that changes the logic of AI adoption. The objective should not be to automate the largest possible share of an engagement. It should be to automate the parts where output is inexpensive to check, then redirect scarce human effort toward the parts that determine confidence in the recommendation: framing the problem correctly, establishing provenance, finding disconfirming evidence, obtaining operating context that is absent from published material, and taking responsibility for the judgment that remains.

In other words, AI is making synthesis abundant. Evidence is becoming the bottleneck.

## The first-draft advantage is disappearing

The productivity case for AI in knowledge work is already substantial. In a preregistered experiment developed with Boston Consulting Group, 758 knowledge workers using GPT-4 completed 12.2% more tasks and worked 25.1% faster on tasks judged to lie within the model's capability frontier, while producing higher-quality outputs. On a more complex task deliberately chosen to sit outside that frontier, however, participants using AI were less likely to reach the correct answer.

More recent evidence suggests the productivity effect can reach beyond individual drafting. A 2026 Organization Science field experiment with 791 Procter & Gamble professionals found that individuals using AI produced work of comparable quality to two-person teams without AI in the product-development tasks studied. AI also helped participants span functional expertise more effectively.

These findings should make consulting leaders optimistic about automating a large portion of the work that historically consumed junior capacity: searching, summarizing, structuring, generating alternatives, and producing a first analytical narrative.

But the BCG experiment contains a more consequential finding for delivery design. On the task where AI reduced accuracy, AI-assisted participants still produced recommendations that evaluators found more coherent and persuasive, even when the underlying answer was wrong.

That matters because consulting has traditionally relied on visible friction as one informal quality-control mechanism. Weak analysis often looked weak: gaps appeared in the logic, a junior team member struggled to defend a claim, or an incomplete workstream arrived visibly unfinished.

Generative AI can remove some of those warning signals. A questionable conclusion can now arrive polished, internally consistent, and boardroom-ready.

The cost of producing plausible analysis has therefore fallen faster than the cost of establishing its validity. That is the source of the evidence premium.

## The jagged frontier is a workflow problem, not a job-description problem

It is tempting to translate current AI limitations into a reassuring division of labor: machines draft; humans judge.

That distinction is unlikely to survive.

The more useful lesson from current evaluations is that AI capability remains jagged. Performance varies materially across tasks that may look similar to the user, which makes occupational labels such as “consultant,” “researcher,” or “analyst” poor units for automation decisions.

The 2026 APEX-Agents benchmark, for example, tested agents on 480 long-horizon tasks across investment banking, management consulting, and corporate law. The highest overall first-attempt completion rate in the paper was 24%; the strongest reported result on management-consulting tasks was 22.7%. Multiple attempts improved performance, but inconsistency remained substantial.

Those percentages should not be treated as a forecast of how much consulting work AI can automate. APEX is deliberately constructed: 22 of its 33 work environments are entirely fictional, web search is disabled for reproducibility, and agents work without the accumulated context of a real engagement team. Its value is different. It demonstrates how quickly performance can degrade when work requires sustained planning, navigation across tools and files, and correct execution over many linked steps.

At the same time, capability is moving quickly.METR's task-horizon evaluations continue to find an exponential trend in how long a well-specified task frontier models can reliably complete, although METR stresses that its tests concentrate on software, machine learning, and cybersecurity and should not be read as direct measures of real occupational automation. It also notes that real jobs contain tacit context, interpersonal interaction, and success criteria that cannot be cleanly scored.

The implication is not to protect a permanent category of “human work.” It is to redesign engagements at the level of individual workflow steps.

A practical distinction is whether a step is observable, reversible, and consequential.

When the correct output can be cheaply checked, errors are easy to reverse, and the context can be made explicit, aggressive automation makes sense. When quality is difficult to observe, evidence is incomplete, or an error could redirect a major recommendation, the workflow needs stronger verification, additional evidence, or named human ownership.

That boundary should move as models improve.

## Evidence becomes more valuable precisely because plausible synthesis becomes abundant

Most client decisions are not constrained by a total absence of information. They are constrained by uncertainty about what information means, whether it applies to the situation at hand, and which assumptions will survive contact with operating reality.

AI can summarize ten reports about a new market. That is different from establishing whether distributors actually behave as those reports imply.

It can identify the consensus view of an acquisition target's competitive advantage. That is different from finding the former operator who can explain the operational exception that invalidates the consensus.

It can generate six reasons a transformation program may fail. That is different from determining which failure mechanism is active inside the client's organization.

The highest-value evidence in these situations tends to possess one or more characteristics that generic synthesis cannot manufacture: provenance, specificity to the decision, independence from the prevailing narrative, or access to information that has not already been encoded into the public corpus.

Primary research is one way to obtain such evidence, but consulting firms should resist an equally simplistic conclusion: an expert interview is not automatically evidence merely because a human said something.

Qualitative-research methodology is instructive here. In one study of 25 in-depth interviews, Hennink, Kaiser, and Marconi distinguished between code saturation, having identified the range of issues being discussed, and meaning saturation, understanding the nuances and dimensions of those issues. In their particular sample, the broad themes emerged considerably earlier than the deeper conceptual understanding. The authors explicitly caution that saturation depends on study purpose, population, sampling strategy, data quality, and the type of issue being explored.

The consulting parallel is important. Three calls with well-chosen operators may be sufficient to expose the basic structure of a problem. They may be nowhere near sufficient to understand an ambiguous incentive, detect an edge case, or distinguish a sector-wide pattern from one executive's experience.

The goal of fieldwork therefore should not be “get more expert calls.” It should be to design an evidence portfolio around the decision: Who is in a position to know? Whose incentives or experience might systematically distort the answer? Which perspective is missing? What observation would disprove the working hypothesis?

That last question deserves more attention as AI increases analytical speed. MIT CISR's research on organizational learning makes a related argument: teams learn faster when they articulate and test explicit hypotheses rather than merely generating solutions and hoping to learn from failure.

AI makes hypothesis generation inexpensive. Firms should spend some of the saved effort making hypothesis testing more demanding.

## A four-part division of labor for AI-enabled engagements

For most consulting workflows today, the useful split is neither “AI does analysis” nor “humans stay in the loop.” It is a four-part operating model.

## 1. Automate retrieval and the first analytical map.

AI should take an increasingly large role in searching authorized sources, extracting relevant information, comparing documents, summarizing established positions, building issue trees, generating preliminary hypotheses, and identifying obvious gaps.

The output should be treated as a map of what to investigate, not as accumulated proof.

This distinction prevents an engagement team from wasting expensive human time reconstructing information that a machine can assemble efficiently, while avoiding the equally costly mistake of treating synthetic fluency as confidence.

## 2. Instrument verification instead of adding generic human review.

“Have a manager check it” is not a scalable control system. As the volume of AI-generated work expands, undifferentiated review simply moves the bottleneck upward.

Verification should instead be embedded in the workflow. Material claims should carry source provenance. High-consequence assertions should be separated from low-consequence descriptive material. Contradictory evidence should be surfaced rather than averaged away. Assertions that cannot be tied to an adequate source should remain explicitly unresolved.

This is consistent with the NIST Generative AI Risk Management Profile, which recommends documenting upstream data sources and content provenance and assessing output accuracy and reliability against known ground truth using multiple evaluation methods. BCG similarly argues that organizations redesigning work around agentic AI need testing, monitoring, evaluation, and auditability built into the operating model rather than added after deployment.

The standard should become: the more effortlessly a claim was produced, the easier its evidentiary trail should be to inspect.

## 3. Invest field research where it can change the answer.

AI-generated research should make primary research more targeted, not eliminate it.

Before an expert conversation, the team should already know what it believes, why it believes it, and which unresolved assumptions matter to the recommendation. Interviews can then be designed to discriminate between competing explanations.

That changes recruitment, too. The most useful respondent may not be the person most likely to confirm the current synthesis. It may be the operator from the failed implementation, the former buyer who rejected the category, the regulator with a different interpretation, or the executive whose market behaves differently from the apparent norm.

Field research becomes valuable when it introduces new information or disconfirmation, not when it merely adds quotations to a conclusion already reached.

## 4. Preserve named human accountability for framing and tradeoffs.

A recommendation is more than the sum of verified facts.

Someone has to decide what problem the engagement is actually solving, which uncertainties matter, what level of evidence is sufficient, which stakeholder tradeoffs are acceptable, and what action should follow when the evidence remains incomplete.

For high-stakes work, that responsibility should be explicit. The relevant partner or engagement leader should be able to answer not only “What do we recommend?” but “Which assumptions does this recommendation depend on, which evidence could overturn it, and why are we comfortable acting before the remaining uncertainty is resolved?”

Human accountability matters here not because machines are constitutionally incapable of judgment. It matters because advice creates consequences, and governance requires an identifiable owner of the decision standard.

## Measure confidence gained, not content produced

Consulting firms can easily measure the wrong benefits from AI.

Tokens generated, research hours saved, slides produced per analyst, and time to first draft all capture activity. They say relatively little about whether an engagement reached a better-supported answer.

That measurement gap is visible in adjacent professional services. Thomson Reuters'2026 AI in Professional Services Report, based on more than 1,500 professionals in legal, tax, accounting, risk, fraud, and government, not management consulting, found that 40% said their organizations were using generative AI, while only 18% said their organizations tracked AI return on investment.

Consulting firms should supplement productivity metrics with a small set of confidence metrics:

- Material-claim support: What share of claims that could change the recommendation have adequate, inspectable evidence?

- Critical-assumption coverage: Which high-impact assumptions have been tested against independent or primary evidence rather than repeated across secondary sources?

- Disconfirmation yield: Did the research process actively surface evidence against the preferred hypothesis, and what changed because of it?

- Late-stage correction: How much work is being reopened during partner review or client challenge because an earlier factual or interpretive error survived the workflow?

The target is not zero uncertainty. That would make consulting impossibly slow.

The target is to know where uncertainty remains and whether the decision justifies taking it.

## Where Enquire fits: use synthesis to decide what deserves field evidence

A research system can support this model when it connects synthetic analysis to evidence collection rather than treating them as substitutes.

Enquire's current positioning combines structured AI research with expert perspective, and its product offering includes text-based input from vetted experts, AI-led asynchronous interviews, and research context that can persist across successive inquiries.

The useful workflow is straightforward. AI research produces the initial analytical landscape: major explanations, known evidence, disagreements, and missing information. Those gaps then determine where operator or specialist perspective is worth acquiring. New field evidence is fed back into the synthesis, where it can confirm, qualify, or overturn the starting view.

Neither side of that loop is self-validating. AI-generated synthesis still requires provenance and verification. Expert statements still require appropriate sampling, interpretation, and triangulation.

The value lies in making those two forms of research inform each other faster.

## The boundary will keep moving

Any workflow designed in 2026 should assume that today's allocation of work will become obsolete.

The evidence already points in both directions. Current systems remain inconsistent on some long-horizon professional tasks. They can also match or exceed human performance on increasingly consequential bounded tasks, and evaluations such as METR's indicate continued rapid capability improvement.

Verification will become more automatable. Primary research will become easier to conduct and analyze automatically. Models will gain more organizational context. Some judgments that seem irreducibly human today will become routine machine work.

That does not weaken the operating principle. It strengthens it.

Consulting firms should continuously move automation toward tasks whose correctness can be observed and whose failure can be contained, while moving scarce professional attention toward whatever remains difficult to verify, expensive to misunderstand, and consequential to the client.

The strategic question is therefore not how many analyst hours AI can remove from an engagement.

It is more demanding:

## Where does the confidence behind our recommendation actually come from, and who owns it?

## Sources and further reading

- Dell'Acqua et al., “Navigating the Jagged Technological Frontier,” Organization Science hbs.edu

- Dell'Acqua et al., “The Cybernetic Teammate,” Organization Science pubsonline.informs.org

- Vidgen et al., “APEX-Agents” arxiv.org

- METR, “Task-Completion Time Horizons of Frontier AI Models” metr.org

- Vaccaro, Almaatouq, and Malone, “When combinations of humans and AI are useful,” Nature Human Behaviour nature.com

- Hennink, Kaiser, and Marconi, “Code Saturation Versus Meaning Saturation” journals.sagepub.com

- NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile nist.gov

- BCG, “Reinventing the Operating System of Work with AI” bcg.com

- Thomson Reuters Institute, 2026 AI in Professional Services Report thomsonreuters.com

- Enquire, current product overview enquire.ai