For funds evaluating a target's technology or trying to move their own process along faster, AI due diligence means checking two quite different things: what the AI can actually do, and whether it's built on data solid enough to trust.
AI due diligence covers two things. First, using AI tools to complete diligence on any deal faster, screening documents and flagging risks. Second, and increasingly more relevant, diligence performed specifically on a target company's AI systems, data quality and model dependencies before a fund commits capital.
Two meanings under one phrase
Ask two people in a deal team what AI due diligence means and you can get two different answers, and both of them are right. One treats AI as a tool: using it to speed up the diligence process itself, running document review, red-flagging contracts, summarising data rooms faster than an associate could manage alone. The other treats AI as the subject: diligence performed specifically on a target's AI systems, data and model dependencies, done before a fund commits capital to a business that has AI built into its product or its operations.
Both senses are growing for related reasons. Deal teams are under pressure to move faster, so AI-assisted diligence tools have become normal in mid-market processes. At the same time, a growing share of targets now claim some AI capability as part of their value proposition, whether that's a genuine machine learning model or a chat interface bolted onto a database. A fund needs to know which one it's buying.
This article covers both, but spends more time on the second, because it's the one most funds are underprepared for.
What gets assessed when a target's own AI is the question
When AI due diligence means checking a target's technology, the questions run more specific than a standard technical diligence checklist. Where does the training data come from, and is the licence to use it actually clean? Is the model built in-house, fine-tuned from an open-source base, or a thin wrapper around a third-party API, with no defensible technical asset underneath it? What happens to the product if that API provider changes its pricing or its terms next year?
Data maturity matters as much as the model itself. A target might have an impressive-sounding AI feature sitting on top of data that's inconsistent, poorly labelled, or split across three incompatible systems. That kind of problem rarely shows up in a demo. It shows up fast in year one of ownership, when the fund tries to scale the thing it just paid for.
Team and governance matter too. Who actually understands the model, and do they stay after completion? Is there a proper evaluation process for accuracy and bias, or has the model never been tested outside the founder's laptop? Under the EU AI Act and current UK regulatory guidance, some use cases such as hiring tools, credit scoring and health applications carry compliance obligations a target may not have addressed at all.
How the process actually runs
A proper AI due diligence engagement sits alongside commercial and technical diligence rather than replacing either. It typically starts with a data request built specifically for AI: model documentation, training data provenance, evaluation metrics, incident logs, and any third-party licensing agreements the product depends on.
Next comes technical interviews with whoever actually built and maintains the system, not just the person who presents it to investors. This is where wrappers get exposed. A founder describing their proprietary AI engine in the pitch deck sometimes turns out, once you start asking questions, to be three API calls to a foundation model with a prompt template wrapped around them. That's not automatically a dealbreaker. It does change the valuation conversation.
The output is usually a short risk register mapped to specific findings: which claims hold up, which need further testing, and where valuation or deal structure should reflect what was actually found rather than what was pitched. Some of this can now be accelerated using AI tools themselves, the first sense of the phrase, though the technical interviews and judgement calls still need a person in the room.
Why this matters to funds, not just target companies
Getting this wrong has a direct cost. Overpaying for AI capability that turns out to be a wrapper around someone else's model is a valuation problem from day one. Underestimating the data work needed to make an AI feature reliable is an integration problem in year one. Missing a regulatory exposure is a problem that surfaces later, usually at the worst time, during an exit process or a customer audit.
It matters at the portfolio level too. Funds building a private equity technology strategy across their holdings need an accurate picture of what each company's AI actually does, not what the board deck claims it does, before deciding where to put further money into scaling it. A diligence process that catches the gap between claim and reality at entry saves a far more expensive conversation at exit.
There's a value creation angle as well. Diligence findings often double as a first hundred days plan: which data gaps to close, which vendor contracts to renegotiate, which compliance work can't wait. Handled properly, the diligence report becomes a working document rather than a folder nobody opens again.
Where AI due diligence goes wrong
The most common mistake is treating it as a tick-box addition to a standard technical diligence template, a couple of extra questions about AI usage bolted onto a checklist built for conventional software. That approach misses almost everything that actually matters, because the risks in AI systems, data lineage, model drift, licensing exposure, evaluation gaps, don't map neatly onto questions designed for a normal SaaS stack.
A second mistake is relying entirely on generalist advisers with no one on the team who has actually built or evaluated a machine learning system. Vendor benchmarks and demo performance get taken at face value, when they should be tested against the fund's own data or use case wherever that's possible.
A third: rushing it. AI due diligence on a genuinely AI-native target takes longer than a fund's usual timeline allows for, and cutting it short defeats the point of doing it at all.
Frequently asked questions
Is AI due diligence just technical due diligence with an AI label?
No. Standard technical diligence checks code quality, architecture and scalability. AI due diligence adds questions unique to machine learning systems: where the training data came from, whether the model is genuinely proprietary or a wrapper around a third-party API, how it's evaluated, and what regulatory obligations apply to its specific use case.
How long does AI due diligence add to a deal timeline?
It depends on how central AI is to the target. Where AI is a minor feature, a few days of focused review alongside existing technical diligence is usually enough. For an AI-native target, expect two to four weeks, including technical interviews and, ideally, some independent testing of model performance against real data.
What should a target prepare in advance?
Model documentation, a clear account of where training data comes from and under what licence, any third-party API dependencies, evaluation results, and a plain answer to what happens if a key vendor changes its terms. Founders who can answer these clearly tend to have fewer surprises later, whatever the diligence finds.
Does this apply to a target that doesn't sell an AI product?
Often, yes. Plenty of companies use AI internally for operations, support or forecasting without positioning themselves as an AI business. If that internal usage touches customer data, regulated decisions or core operational processes, it's worth checking even when it isn't part of the pitch.
Can AI tools genuinely speed up the diligence process itself?
Yes, for the parts that are mostly volume: reviewing large data rooms, flagging contract clauses, summarising financials. It's less reliable for judgement calls, such as deciding whether a target's AI claims stand up to scrutiny, which is exactly the work a person still needs to do.
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