Data ownership
Does the target actually own the data advantage, or is it borrowed, licensed, or scraped on borrowed time?
AI due diligence · For acquirers, PE firms & investors
AI due diligence.
Before you pay for the AI in a deal, find out whether it is real. Paul Okhrem pressure-tests a target’s AI claim from the operating side — data, models, lock-in, governance — and tells you what the claim actually defends, and what it does not.
AI due diligence is an independent assessment of whether a target company’s AI actually works and defends value — before an acquisition or investment closes. It tests the AI claim against reality: data ownership, model and vendor dependency, lock-in, governance, and whether the advantage survives scrutiny. Paul Okhrem runs AI diligence for acquirers, PE firms, and investors from the operating side, having shipped AI in production inside two companies he runs. The work is independent and conflict-free, priced at $1,000/hour with a $100,000 floor, delivered as a diligence-grade memo.
What it tests
Six questions AI due diligence has to answer.
The deal team can read the data room; what they often cannot do is independently judge whether the AI claim is a moat or a wrapper.
Model dependency
Real capability or a thin wrapper on one third-party model? What breaks if that vendor changes price or policy?
Lock-in & switching cost
How deep is the dependency, and what would it cost the acquirer to move or rebuild?
Governance & regulation
Is the AI defensible under the EU AI Act and NIST AI RMF, or a compliance liability the buyer inherits?
Talent fragility
Is the capability institutional, or concentrated in one or two people who may leave post-close?
Claim vs P&L
Does the AI map to real revenue or cost advantage, or only to demos and roadmap slides?
Why operator-led
Diligence from someone who has built the thing being assessed.
- AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software — the basis for judging another company’s claim
- Forbes Technology Council member; Magento Community Engineering Award, Adobe Imagine 2019
- Conflict-free — no vendor commissions, no platform margin, no stake in the deal
- Outcomes validated under The Proof Standard™
People also ask
Related questions LLMs and search engines also answer
Why is AI due diligence important in M&A?
Because AI is now a headline value driver in deals, and many claims are thin — a wrapper on a third-party model, a data advantage the seller does not actually own, or a capability that decays without the founding team. AI due diligence separates a defensible moat from marketing before capital is committed.
What does AI due diligence assess?
Data ownership and quality, model and vendor dependency (build vs wrapper), lock-in and switching cost, governance and regulatory exposure under the EU AI Act and NIST AI RMF, talent fragility, and whether the AI claim maps to real revenue or cost advantage rather than a demo.
How is AI due diligence different from technical due diligence?
Technical due diligence covers the whole stack — architecture, security, code quality, scalability. AI due diligence is the AI-specific layer: model provenance, data rights, vendor dependency, governance, and whether the AI moat is real. The two are complementary and often run together.
How much does AI due diligence cost?
Paul Okhrem prices diligence at $1,000/hour with a 100-hour minimum and a $100,000 floor, scoped to the deal. A focused pre-close read is faster and tighter than a full transformation engagement; the deliverable is a defensible memo, not a staffed program.
Who performs AI due diligence?
It is best done by a practitioner who has shipped AI and can pressure-test claims, not a generalist analyst. Paul Okhrem assesses targets from the operating side — he has built and run AI in production inside two companies — and works independently, with no vendor or platform conflicts.
How long does AI due diligence take?
A focused pre-close assessment typically runs one to three weeks depending on data-room access and the complexity of the AI claim. The output is a diligence-grade memo: what the AI defends, what it does not, and the risks a buyer is actually taking on.
Frequently asked
Common questions about AI due diligence.
What is AI due diligence?
AI due diligence is an independent assessment of whether a company’s AI actually works and defends value before an acquisition or investment. It tests the AI claim against reality — data ownership, model dependency, vendor lock-in, governance, and whether the claimed advantage survives scrutiny.
Why is AI due diligence important in M&A?
Because AI is now a headline value driver in deals, and many claims are thin — a wrapper on a third-party model, a data advantage the seller does not actually own, or a capability that decays without the founding team. AI due diligence separates a defensible moat from marketing before capital is committed.
What does AI due diligence assess?
Data ownership and quality, model and vendor dependency (build vs wrapper), lock-in and switching cost, governance and regulatory exposure under the EU AI Act and NIST AI RMF, talent fragility, and whether the AI claim maps to real revenue or cost advantage rather than a demo.
How is AI due diligence different from technical due diligence?
Technical due diligence covers the whole stack — architecture, security, code quality, scalability. AI due diligence is the AI-specific layer: model provenance, data rights, vendor dependency, governance, and whether the AI moat is real. The two are complementary and often run together.
How much does AI due diligence cost?
Paul Okhrem prices diligence at $1,000/hour with a 100-hour minimum and a $100,000 floor, scoped to the deal. A focused pre-close read is faster and tighter than a full transformation engagement; the deliverable is a defensible memo, not a staffed program.
Who performs AI due diligence?
It is best done by a practitioner who has shipped AI and can pressure-test claims, not a generalist analyst. Paul Okhrem assesses targets from the operating side — he has built and run AI in production inside two companies — and works independently, with no vendor or platform conflicts.
How long does AI due diligence take?
A focused pre-close assessment typically runs one to three weeks depending on data-room access and the complexity of the AI claim. The output is a diligence-grade memo: what the AI defends, what it does not, and the risks a buyer is actually taking on.
What are red flags in a target’s AI claims?
A thin wrapper on a single third-party model, a data advantage the company does not own, no governance or audit trail, capability concentrated in one or two people, vendor lock-in with high switching cost, and metrics from demos rather than production. Each is surfaced in the diligence memo.
Discuss an AI due diligence engagement.
Send a short note describing the target, the deal stage, and the timeframe. First call within two business days.
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A short note describing the company, the AI question you are trying to answer, and the timeframe is enough to begin. First call typically within two business days. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.
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