AI Due Diligence: What PE Funds Need to Know in 2026

In short: As more targets build or embed artificial intelligence, a standard technical due diligence is no longer enough. AI introduces a distinct risk surface that traditional diligence was never designed to price: regulatory exposure under the EU AI Act, unclear data provenance and IP, dependence on third-party foundation models, ungoverned "shadow AI" across the business, and the gap between an AI story and an AI reality. This article sets out what AI due diligence actually examines in 2026, how the regulatory picture stands after this year's changes, and why assessing AI as a liability is now as important as assessing it as a value driver. The core point: a fund that prices only the upside of a target's AI, and not its risk, is underwriting a position it has not fully seen.

Key takeaways

• AI due diligence is a distinct discipline from a general technical review, because AI adds risks - regulatory, legal, and operational - that a conventional assessment does not surface.
• The EU AI Act is now partly live: general-purpose AI obligations and Article 50 transparency duties apply as of August 2026, while most high-risk obligations were deferred into late 2027 and 2028, which changes the timeline a target must plan against but not the direction.
• Data provenance and IP are often the largest hidden exposure: how a target's models were trained, on whose data, and under what licences can create liabilities that survive the transaction.
• Dependence on a third-party foundation model is a concentration risk in disguise, affecting cost, continuity, and control in ways a fund should price at entry.
• Assessing AI as a risk complements, rather than replaces, assessing it as a value driver. A complete view needs both lenses.

Why AI Is a New Diligence Problem, Not Just a New Value Lever

For most of the history of technical due diligence, the questions were stable: is the architecture sound, can it scale, is the code maintainable, is the team a risk. AI does not remove any of those questions, but it adds a category the old checklist never had to consider. When a target builds artificial intelligence into its product, or has quietly adopted it across its operations, it takes on exposures that behave differently from ordinary software risk: they are partly legal, partly regulatory, and partly about the provenance of data and models that may not be documented anywhere.

The industry has spent the last two years learning to assess AI as a value driver, asking whether a target's data and capabilities let it compound an advantage. That question matters and is worth asking. But it is only half the picture. The other half is the one funds are less practised at: what could this AI cost us. A model trained on data the target did not have the right to use, a product feature that will fall under a tightening regulatory regime, a core capability rented from a single external provider on terms that can change - each of these is a liability that a value-focused assessment can miss entirely, because it is looking for upside rather than exposure.

The practical implication is that AI due diligence has to be run with a deliberately different posture from the optimistic one that surrounds most AI conversations. Its job is to find what the AI story leaves out.

The Regulatory Picture in 2026

Any fund evaluating an AI-enabled target in 2026 is doing so against a live and still-moving regulatory backdrop, and getting the current state right matters, because it changed materially this year. The EU AI Act, in force since 2024, phases its obligations in stages. Its rules on prohibited practices have applied since early 2025. Its obligations for providers of general-purpose AI models have applied since August 2025, and as of August 2026 the European Commission's powers to supervise and enforce those obligations are active. The Act's transparency duties, which require disclosure of AI interaction and the labelling of AI-generated content, also took effect in August 2026.

The most-watched piece, the regime for high-risk AI systems, moved. Under changes adopted in mid-2026, the bulk of the high-risk obligations for standalone systems were deferred to December 2027, and those for AI embedded in already-regulated products to August 2028. For a fund, the lesson is not that high-risk compliance has gone away. It is that a target now has a defined window to prepare, and that the documentation and governance work behind compliance takes far longer to build than the deadline suggests, so a target that has done nothing is carrying a future cost even if today it is technically compliant. The penalties give the exposure its weight: the Act's fines run to tens of millions of euros or a percentage of global turnover, on a scale comparable to or exceeding data protection law, and its reach extends to providers outside the EU whose systems are used within it.

Because this area is still shifting, the right move in a specific deal is to verify the current position rather than rely on a summary. What does not shift is the underlying point: a target's regulatory classification, and the state of its compliance work, is now a valuation input.

What AI Due Diligence Actually Examines

A focused AI due diligence looks at a risk surface that a general technical review is not built to cover. Several dimensions recur across deals.

The first is regulatory classification and exposure. Before anything else, the assessment establishes what the target's AI actually is in regulatory terms: whether any of its systems fall into high-risk categories, whether it acts as a provider or a deployer, whether it triggers transparency duties, and how far its current documentation and governance would carry it toward compliance. This is the difference between a manageable roadmap item and an unbudgeted programme of work.

The second, and often the largest hidden exposure, is data provenance and intellectual property. The questions are uncomfortable and frequently unanswered inside the target: what data were the models trained on, was it lawfully obtained and licensed for this use, does it contain personal data that raises data-protection issues, and could any of it expose the company to intellectual-property claims. A model is only as clean as the data underneath it, and that data rarely appears in a data room.

The third is model dependency and concentration. Many products described as AI are built on a third-party foundation model accessed through an API. That is a legitimate architecture, but it is also a concentration risk that a fund should see clearly: it affects cost as usage scales, continuity if the provider changes terms or deprecates a model, and control over a capability the company presents as its own. A target whose core differentiation is rented rather than owned is a different asset from one that controls its own models, and the diligence should say which it is.

The fourth is shadow AI and governance. Beyond the product, AI tools have often spread through a target's own operations - in engineering, support, sales, and back office - without a policy, an inventory, or oversight. That creates data-leakage and compliance exposure that no one has mapped. A mature target can show an AI governance posture: an inventory of where AI is used, controls over data fed into external tools, and human oversight where it matters. Its absence is itself a finding.

The fifth is reliability and evaluation. For any AI that makes or informs decisions, the assessment asks how the target knows the system works: whether there is systematic evaluation, monitoring for drift and degradation, and human oversight proportionate to the stakes. An AI feature with no evaluation behind it is a reliability risk wearing the costume of a capability.

Underlying all five is a simpler question that a good AI due diligence keeps returning to: how much of the target's AI story is real. Some products presented as AI-native are a thin layer over a third-party model with little defensibility; others have genuine data assets and engineering behind them. Telling the two apart is often the single most valuable thing the assessment does.

Risk and Value Are Two Lenses on the Same Asset

None of this displaces the value question. Whether a target's data and AI capabilities can compound an advantage remains central to the investment thesis, and a fund should assess it. The point is that the value lens and the risk lens look at the same asset and see different things, and a complete diligence uses both. The maturity view asks what this AI could be worth. The risk view asks what it could cost. A target can score well on one and badly on the other: a genuinely capable AI product can also carry serious data-provenance exposure, and a modest AI capability can be entirely clean. Only holding both lenses at once gives a fund the full picture it is actually paying for.

How Altimi Approaches AI Due Diligence

Altimi delivers buyer-side technical due diligence for private equity, venture capital, and growth investors across Europe, and AI and data maturity is one of the eight assessment areas built into it, covering AI adoption, data pipeline readiness, model governance, and value-creation potential. In practice this means the assessment reads a target's AI through both lenses described above: as a value driver, through the AI maturity score, and as a risk surface, through the questions of provenance, dependency, governance, and regulatory exposure that a fund needs answered before it signs. Findings arrive in the same committee-ready form as the rest of the diligence: a RAG-scored report, a severity-ranked risk matrix with a go or no-go recommendation, and a 90-day roadmap that turns exposures into a post-close plan.

Independence is an operating principle: Altimi acts only for the investor, on a fixed fee with no follow-on incentives, discloses any prior relationship with the target, and declines mandates where a material conflict exists. The assessment is delivered at a fixed price within a scope agreed before kickoff and completed in two weeks, drawing on more than a hundred buyer-side assessments and over 150 engineering engagements. As an EU-based, ISO 27001-certified organisation, Altimi keeps source code and deal data inside the European data protection perimeter throughout a confidential process, which matters especially when the subject of the diligence is the target's data and models themselves.

A Note for European and DACH Funds

For funds active in the DACH region, Central and Eastern Europe, and the wider European market, the AI regulatory dimension is not a distant concern but a near-term valuation factor, precisely because the EU AI Act reaches any AI system used in the EU regardless of where its provider sits. A target selling into Europe is inside the regime whether or not it has noticed. That makes a European provider, familiar with the Act's phased obligations and able to assess a target's exposure against them, more than a convenience in cross-border AI deals. It also makes where the assessment itself runs relevant: examining a target's models and training data is sensitive work, and keeping it inside the European data protection perimeter, with a certified partner, is part of doing it responsibly.

So What Are You Actually Buying When You Buy an AI Company?

The honest answer is that you do not fully know until you have looked underneath the story. An AI-enabled target can be exactly what it presents itself as: a genuine data and engineering asset with a defensible capability and a clean provenance. It can also be a thin wrapper over someone else's model, trained on data of uncertain origin, operating inside a regulatory regime it has not prepared for. From the outside, and often from the pitch deck, the two look remarkably alike. AI due diligence exists to tell them apart, and in 2026, with the regulatory picture live and the technology moving faster than most governance can keep up with, telling them apart is no longer optional.

If you are evaluating an AI-enabled target and want its AI assessed as both a value driver and a risk, the fastest way to start is a short conversation about the company and the thesis in front of you.