Investment funds that assess a company's technology or aim to speed up their internal processes need to perform AI due diligence. The work consists of two separate evaluations: the functional capabilities of the artificial intelligence and the reliability of the underlying data.
AI due diligence covers two things.
One application of AI tools is to finish due diligence on transactions faster by scanning documents and spotting risks. A second application, increasingly common, is to carry out due diligence on a target company's particular AI systems, data quality, and model dependencies before a fund commits capital.
One phrase carries two meanings
If you ask two members of a deal team what AI due diligence means, you will probably receive two distinct answers, and each will be valid. One perspective treats AI as a tool to speed up the due diligence process, for example by employing it for document review, spotting problematic contracts, and summarising data rooms faster than a junior employee could manage alone. The other perspective treats AI as the object of the diligence, involving a detailed review of a target company's AI systems, data, and model dependencies before an investment fund allocates capital to a business that uses AI in its products or operations.
Both trends are growing for comparable reasons. Deal teams are required to act faster, which has turned AI-assisted diligence tools into a standard part of mid-market processes. Moreover, more target companies now feature AI capabilities in their value propositions. These capabilities include genuine machine learning models as well as chat interfaces linked to databases. An investment fund needs to determine which kind of technology it is acquiring.
This article covers both subjects. It concentrates on the second because most funds are not adequately prepared for it.
What gets assessed when a target's own AI is the question
When conducting artificial intelligence due diligence on a company's technology, the requirements are more detailed than a typical technical checklist. You must ascertain the source of the training data and verify that the usage licences are valid. You need to determine whether the model was created internally, fine-tuned from an open-source foundation, or constructed as a simple interface to a third-party API that does not provide proprietary technical value. You also have to assess how the product would be impacted if the API provider changed its pricing or service terms in the future.
Data quality is equally important as the model itself. A target company might have an impressive AI feature that depends on data that is inconsistent, poorly labelled, or spread across three incompatible systems. These problems are seldom visible during a demonstration. They become clear during the first year of ownership when the fund tries to scale the technology it bought.
Team and governance are also key considerations. Who knows how the model functions, and will those individuals remain involved after the model is completed? Is there a formal process to assess the model for accuracy and bias, or has it only been tested by its creator? The EU AI Act and current UK regulations mandate compliance for specific applications, such as hiring tools, credit scoring, and health applications, and a target organisation may not have taken these requirements into account.
How the process works in practice
An AI due diligence process acts as a supplement to commercial and technical diligence rather than replacing them.
The process usually begins with a data request designed for AI. This request includes model documentation, the source of training data, evaluation metrics, incident logs, and any third-party licensing agreements required for the product.
The next step involves technical interviews with the people who built and maintain the system, rather than just the individuals who present it to investors. These interviews reveal whether a product is a wrapper. A founder may describe a proprietary AI engine in a pitch deck, but technical questions can show that the system consists of three API calls to a foundation model using a prompt template. This discovery does not necessarily end the deal. However, it does change how the company is valued.
The output is typically a short risk register that corresponds to specific findings. It identifies which claims are accurate, which require more testing, and where the valuation or deal structure should be adjusted based on the findings instead of the initial pitch. Some of this work can be completed faster using AI tools. However, technical interviews and final judgements still require human oversight.
The reasons this is important for funds
Errors in this area result in direct costs. If you overpay for AI capabilities that are simply wrappers for another company's model, you create a valuation problem immediately. If you underestimate the amount of data work required to make an AI feature reliable, you will face integration problems within the first year. Failing to identify regulatory risks creates problems that typically emerge 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.
This process also assists with value creation. The findings from the diligence phase can serve as a plan for the first hundred days. These findings identify which data gaps to fill, which vendor contracts to renegotiate, and which compliance tasks require immediate attention. When managed correctly, the diligence report functions as a practical working document instead of a file that remains unused after the deal closes.
Where AI due diligence goes wrong
One 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 matters, because AI-specific risks (data lineage, model drift, licensing exposure, evaluation gaps) don't map neatly onto questions designed for a normal SaaS stack.
Another mistake is relying only on generalist advisers without including anyone on the team who has built or evaluated a machine learning system. Teams often accept vendor benchmarks and demo performance as facts. Instead, they should test these systems against the fund's own data or specific use cases whenever possible.
Rushing the process is a mistake too.
Assessing an artificial intelligence company thoroughly requires more time than a typical fund's due diligence process permits, and shortening the assessment undermines its value.
If you have finished reading and are ready to begin development, our AI development team builds systems like this using governed data. We design these systems for production use from the start of the project.
To conduct a broader review of systems, data, reporting, and security at a target or new holding, use our technology assessment checklist for portfolio companies.
Frequently asked questions
Is AI due diligence just technical due diligence with an AI label?
Standard technical diligence looks at code quality, architecture, and scalability. AI due diligence adds extra assessments that are specific to machine learning. These assessments cover the source of training data, if the model is proprietary or relies on a third-party API, the evaluation methods employed, and the regulatory requirements for the particular application.
How long does AI due diligence add to a deal timeline?
The length of the process depends on how important AI is to the company. If AI is merely a minor feature, a few days of review within standard technical diligence are enough. For a company that is centred on AI, the process lasts two to four weeks. This timeline includes technical interviews and independent testing of model performance with real data.
What should a target prepare in advance?
Founders need to supply model documentation that details the sources and licences of the training data. The documentation must contain any dependencies on third-party APIs, the evaluation results, and a concrete plan for how the company will react if a primary vendor changes its terms. Founders who give clear information on these points usually face fewer issues during the diligence process.
Does this apply to a target that doesn't sell an AI product?
In many cases, the answer is yes. Many companies use AI for internal operations, customer support, or forecasting while not branding themselves as an AI business. You should examine this internal usage when it involves customer data, regulated decision-making, or core operational processes, even if the technology is not mentioned in the company's public messaging.
Can AI tools genuinely speed up the diligence process itself?
Yes, it is effective for tasks that involve large volumes of information, such as reviewing extensive data rooms, flagging contract clauses, and summarising financial documents. It is less reliable for making judgements, for example evaluating whether a company's claims about its AI are accurate. A person must still carry out that specific work.
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