How AI is changing small business due diligence

AI-assisted tools are increasingly part of how accountants, lawyers, and buyers work through due diligence on small business acquisitions, and Treadstone Associates, an AI-consulting and business-services firm that works specifically on due diligence and process automation, has written about where these tools genuinely help. What they mainly change is speed and pattern-detection across a large volume of documents. What they do not change is judgment, and the two get conflated more often than they should, particularly in marketing that overstates what an AI tool can actually be trusted to conclude on its own.

Where AI genuinely helps right now

AI-assisted tools can extract and organize data from large volumes of financial statements, bank records, and contracts considerably faster than a person working through them manually, and they can flag anomalies, unusual transaction patterns, inconsistent categorization, gaps between what is reported and what supporting documents actually show, for a human reviewer to look at more closely. Similarly, contract-review tools can scan leases and supplier agreements for specific clauses, such as assignment restrictions or change-of-control triggers, and surface them for a lawyer’s attention rather than requiring someone to read every page cold looking for them. Used this way, the tools function as a fast first pass that widens what a smaller team can realistically get through, not as a replacement for the review that follows.

Where it still falls short, and why that matters

• An AI tool trained on well-organized inputs performs very differently against the commingled, inconsistent bookkeeping many small businesses actually have, and messy source records tend to produce confident-sounding but unreliable output rather than an obvious error message
• Pattern detection is not the same as judgment: flagging that a number looks unusual is different from knowing whether that reflects a real problem, a normal seasonal swing, or a reasonable one-time item
• A tool summarizing a contract or lease can miss context a lawyer would catch, particularly around how a clause has actually been enforced, waived, or interpreted in practice between the parties involved
• Faster document review does not change who is accountable for the numbers or the legal risk in a transaction, and treating a tool’s output as a conclusion, rather than as a starting point for a qualified professional’s review, is where the real risk sits
• A clean, confident-looking summary can create a false sense of completeness, since polished output says nothing about whether the underlying set of documents fed into the tool was actually complete in the first place

What this means for buyers and sellers right now

The sensible way to treat AI-assisted diligence tools is the way any experienced advisor would treat a junior associate’s first-pass review: useful for surfacing what deserves closer human attention, not a substitute for the accountant or lawyer who ultimately has to stand behind the conclusion. A seller with clean, well-organized records is likely to see these tools work in their favour, since there is something reliable underneath for the tool to work from. A business with messier books may find that AI-assisted diligence actually surfaces more questions, not fewer, simply because inconsistencies a rushed manual review might have missed become easier to catch at scale. Either way, the professional review at the end of the process is doing the same job it always did.

Why scepticism is the right default, not resistance

None of this is a case for avoiding these tools, and it is not an endorsement of any particular one either. The more useful frame is the same one experienced advisors already apply to any new junior resource: verify before relying, and be more careful, not less, whenever the underlying records are messy rather than clean. A buyer or seller evaluating whether to use an AI-assisted diligence tool on a specific deal is better served by asking the professional running that review what the tool actually checked and what it did not, than by treating a fast turnaround as evidence the review was thorough. Treadstone Associates' own work in this space is built around exactly that distinction: automation that speeds up structured, repeatable review tasks, paired with a human who remains the checkpoint on anything that requires actual judgment, which is consistent with how due diligence has always needed to work, only faster in the parts that were always mechanical.