How AI Is Changing Document Review During M&A Due Diligence

AI due diligence has moved from pilot projects to everyday practice. Bain & Company’s 2026 M&A report found that AI adoption in M&A more than doubled in 2025, with 45% of more than 300 surveyed executives now relying on it. Bain also says sourcing, screening, and diligence are still where AI is used most.

However, wider use does not mean you can trust every output. Vendors promise fast and accurate reviews, but you still have to confirm what a tool flags. Trusting a wrong reading of a material contract term can hurt a deal. Skipping a tool that would have caught what a reviewer missed can hurt just as much.

This guide shows where AI helps in document review, where it still falls short, and how to fit it into your process.

What AI document review tools do well

Machine learning document review works best on narrow, repeatable tasks across large document sets. These tools have seen many versions of the same clause, so they spot patterns quickly. In practice, that helps in three ways:

They pull out specific clause types, such as change-of-control provisions, termination rights, and assignment restrictions, across a large contract set. This is the core of AI contract analysis.
They flag inconsistencies between related documents. For example, a representation in a disclosure schedule may conflict with the underlying contract.
They speed up first-pass review of large, repetitive sets. This frees your reviewers to spend time on documents that need judgment.

Independent testing points the same way. In the Vals Legal AI Report from February 2025, legal AI tools beat a lawyer baseline on tasks tied to document analysis, information retrieval, and data extraction. The study tested legal AI products in general, so treat it as a signal rather than proof for any one data room tool.

Many of these features now sit inside the best virtual data rooms for M&A professionals. An AI-powered data room can let you review files where they already sit, without exporting them to a separate tool.

Where human review remains essential

AI can find a clause. It cannot always tell you what that clause means for your deal. In AI document review M&A work, treat every flag as a lead to check, not a finding to rely on.

Ambiguous contract language needs context. The right reading often depends on deal-specific knowledge, such as how the buyer plans to integrate the target, and a tool does not have that.
Materiality needs business judgment. Deciding whether a flagged issue changes price, terms, or risk allocation goes beyond pattern matching.
Findings need checking against the source. False positives and missed context both happen, so confirm each finding in the original document before you use it in negotiations.

The Vals report shows the same split. Lawyers still outperformed AI tools on redlining, a task that requires judging how a change affects the deal. Bain’s 2026 report makes a similar point about the wider deal process: even fast AI still leaves stakeholder alignment and critical decisions to people.

Common pitfalls in over-relying on AI review

Three habits can cause trouble:

Teams treat an AI summary as a substitute for reading the document. A summary can skip a carve-out buried deep in a long agreement, so it works better as a starting point for targeted review.
Teams assume the tool performs the same on every document type. Accuracy can vary a lot between well-structured contracts and messy, inconsistently formatted files. A model that reads clean, typed agreements well may struggle with scanned pages or irregular templates.
Teams ignore the tool’s training and limits. If you don’t know what a model was built to do, you can’t decide how much weight to give its output. Ask the vendor which document types and data the tool was tested on.

Each habit has the same fix. Ask what the tool was designed to do, and check its work on the documents that matter most.

How to evaluate an AI-assisted review tool before a deal

Test a tool before you rely on it. Before you roll out due diligence automation on a live deal, run three checks:

Test the tool on a known document set where the issues are already identified. Compare its findings to a human reviewer’s baseline.
Check how it handles edge cases and ambiguous language, not just straightforward terms.
Confirm how it presents findings. Your reviewers should be able to verify a flag against the source document quickly.

A tool that scores well on the first check but makes verification slow can still cost you time.

Integrating AI review into the broader diligence process

AI-assisted review works best as a first pass that directs human attention. It does not replace the review itself. To put that into practice:

Let the tool sort and flag, then assign reviewers to the documents it marks as risky or unclear.
Treat AI features as one more criterion when you compare data room platforms, next to security and access controls.
Add a verification step for any AI-flagged finding that will shape your negotiating position. No material decision should rest on unverified AI output.

This workflow also keeps your record clear. If someone later questions a negotiating position, you can show which findings a person confirmed and which came only from the tool.

Conclusion

AI has changed how quickly deal teams can review documents. It finds clauses, spots mismatches, and clears repetitive first-pass work well. It doesn’t judge ambiguity or materiality, and you still need to check its findings against the source. The risks run both ways: trusting it too much and using it too little. The practical path is simple. Test the tool on documents you already know, use it to direct reviewer attention, and verify every finding that affects a negotiating position.

Before your next deal, try running your chosen tool on a closed deal’s data set and see what it catches and what it misses.