The Hidden Risks Of AI-Generated Due Diligence

If you’ve closed a deal in the past year, there’s a good chance your outside counsel, your counterparty’s advisors or maybe your own team used an AI tool to review part of the data room.

The appeal is obvious. A Series B data room might contain 300 contracts. A middle-market acquisition data room can easily include 1,500 documents across corporate records, IP filings, employment agreements and commercial contracts. AI can churn through that volume in hours instead of weeks, flagging change-of-control triggers, assignment restrictions and consent requirements with impressive speed.

But speed is not accuracy. And in transactional practice – whether you’re a founder navigating a financing, an investor running diligence on a target or counsel advising on an acquisition – accuracy is what keeps you out of trouble. The risks of AI-generated due diligence are hidden precisely because the work-product looks so polished. Here are five risks you should know about.

Hallucinated Provisions

Large language models are generative by design. That means they can fabricate content that looks plausible but doesn’t exist in the source documents. In a due diligence context, this manifests as AI “finding” clauses that aren’t there, misquoting contractual language or conflating terms from entirely different agreements.

Imagine your AI tool flags 47 change-of-control provisions across a data room. The citations look right. But three of those “provisions” were hallucinated, created from language in neighboring contracts that the model treated as relevant context. Meanwhile, two actual change-of-control triggers buried in side letters were missed entirely.

In M&A and capital markets, a single invented or overlooked provision – an anti-assignment clause, a most-favored-nation right, a consent requirement – can have material consequences at closing or, worse, post-closing. The model doesn’t know which provisions are deal-critical. It’s pattern-matching, not reading.

Cross-Reference Blindness

Legal documents are full of cross-references. A non-compete provision might be subject to a carve-out defined six pages earlier. A termination clause might have been superseded by an amendment executed two years later. A “Material Adverse Effect” definition might incorporate fifteen sub-exceptions that dramatically narrow its apparent scope.

AI tools tend to treat provisions in isolation. They extract a clause, summarize it at face value and move on. They rarely trace a defined term back to its definition to assess whether the plain meaning of the extracted language actually reflects the operative legal effect. The result is a technically correct extraction that’s substantively misleading because it ignores the contractual architecture around it.

Here’s a concrete example: an AI-generated summary might report that a license agreement contains a broad IP assignment clause. What it won’t tell you is that the assignment is conditioned on a defined term (e.g., “Developed IP”) whose definition, three cross-references deep, limits the assignment to improvements made during the first contract year only. A human reviewer who understands the deal’s timeline would catch that. The model won’t.

False Confidence and Reduced Oversight

This may be the most insidious risk. When AI produces a well-formatted diligence memo, complete with headers, issue categories, risk ratings and contract citations, it carries an air of authority that a junior associate’s rough first draft rarely would. The professional presentation masks the underlying uncertainty.

The consequence is predictable: reviewers apply less scrutiny. Partners skim rather than dig deeper. In-house teams forward AI-generated summaries to their boards without the kind of stress-testing they’d perform on work product from a second-year associate. The irony is that we’d scrutinize every line of a junior lawyer’s memo, but we accept the AI’s output because it looks like a fully vetted product after several drafts. The cleaner the output, the more dangerous the gaps become.

Inconsistent Treatment of Materiality

Due diligence is not just about identifying issues; it’s about triaging them. What matters depends on the deal. Is the buyer acquiring the whole company or a single product line? Is the investor’s risk tolerance conservative or aggressive? Does the business operate in a regulated industry where a specific type of clause carries outsized consequences?

AI tools lack this context. They flag issues based on pattern recognition, not deal logic. The result is often a memo that treats a boilerplate notice provision with the same prominence as a genuinely consequential exclusivity arrangement. Or worse, it flags fifty immaterial findings while burying the three that actually matter under a wall of noise.

For founders and investors, this creates a particular risk: you may walk away from a diligence review feeling comprehensive when you’ve actually been comprehensive about the wrong things. The AI review gave you breadth without depth where depth was needed.

Liability and Privilege Concerns

When AI-generated diligence misses a critical issue, who bears the responsibility? The lawyer who supervised the review? The firm that deployed the tool? The vendor that built it? These questions are not yet fully resolved, but they’re coming into sharper focus as AI tools become standard in transactional practice.

Consider malpractice standards. Rule 1.1 of the ABA Model Rules of Professional Conduct requires lawyer competence, which courts have increasingly interpreted to include competence in understanding the tools you use. If a lawyer relies on AI-generated diligence without adequate verification and a material issue slips through, the defense that “the AI missed it” is unlikely to carry much weight. The duty of supervision doesn’t evaporate because the “associate” is a machine.

Then there’s the privilege question. Many AI tools operate through third-party application programming interfaces or APIs. When confidential deal documents pass through external servers for processing, does attorney-client privilege survive? The answer depends on the jurisdiction, the engagement structure and the specific tool, but the mere existence of the question should give pause to any lawyer handling sensitive M&A diligence.

Practical Recommendations

None of this means you should avoid AI in due diligence. The efficiency gains are real, and they’re not going away. But treating AI-generated diligence as a finished product, rather than a first draft requiring human review, is courting avoidable exposure. A few principles worth adopting:

Human review is critical. Every AI-generated finding that could affect deal terms, closing conditions or risk allocation should be verified against the source document by a qualified reviewer. Full stop.

Sample and spot-check systematically. Don’t just review what the AI flagged. Go back to the source documents on a random-sample basis to check for issues the AI may have missed.

Document what was AI-assisted versus human-verified. Maintain clear records of your workflow. This protects you in the event of a dispute and demonstrates the kind of supervisory diligence that regulators and courts will increasingly expect.

Update engagement letters. If your firm uses AI tools in diligence, your engagement letters should disclose that fact and address how AI-assisted work product is supervised and verified. Silence on this point is an increasingly untenable position.

Bottom Line

AI is a powerful accelerant for due diligence. It compresses timelines, reduces costs and identifies patterns across documents that no human team could match at speed. But it is a tool, not a substitute for professional judgment.

The risks described here are “hidden” in a very specific sense: the outputs look authoritative. They read like the work product of a careful lawyer. And that’s precisely what makes them dangerous when they’re wrong. A sloppy first draft gets scrutinized. A polished AI memo gets forwarded.

For founders navigating a financing, investors diligencing a target or counsel advising on a transaction, the message is the same. Use the tools. Benefit from the speed. But verify the outputs with the same rigor you’d apply to any work product that carries material consequences. Because in this domain, the cost of being confidently wrong is measured in deal terms, liability exposure and professional reputation.