Private equity spent 2024 and 2025 running AI pilots. The interesting question in 2026 is no longer whether firms are adopting the technology. It is which parts of the deal process actually got faster, and which claims have quietly failed to hold up.
The honest answer is that the returns are concentrated, not general.
Where the returns are showing up
In Bain and StepStone's 2026 Private Equity GP Outlook, general partners reported their highest returns from generative AI in deal sourcing and due diligence. That is a narrow finding and worth reading carefully. The gains are landing in the research-heavy, document-heavy, front-end stages of a deal, which is exactly where a mid-market deal team burns analyst weeks producing work that looks similar from one deal to the next.
Adoption reflects that. Bain's Global M&A Report 2026 found AI adoption in M&A more than doubled during 2025, with one in three dealmakers now deploying it systematically or redesigning their processes around it. Systematic deployment is the phrase that matters. The gap between a firm where two associates use a chatbot and a firm that has rebuilt its screening workflow is the gap that produces measurable advantage.
Where it is not working
The same survey is blunt about portfolio companies. Nearly 40% of GPs do not expect any material financial impact from AI inside their portfolio companies during 2026, and where benefits do appear, they skew toward cost savings rather than growth.
So the pattern is: real returns at the deal level, thin returns at the asset level. If your investment committee is being sold an AI story that runs the other way round, the survey data does not support it.
Why diligence quality carries more weight now
Bain's Global Private Equity Report 2026 puts the underlying pressure in a form that is hard to argue with. The report's framing is that "12 is the new 5," meaning today's deals demand faster EBITDA growth than they used to, and achieving it requires sharper value creation and a clearer, data-backed edge. A deal that needed roughly 5% annual EBITDA growth to produce a 2.5x return a decade ago now needs something closer to 10% or 12%.
That changes what commercial diligence is for. When multiple expansion did the work, an approximately correct market view was survivable. When the return depends on operating growth you have to underwrite, the quality of your market and customer evidence becomes the return.
Conditions have not made this easier. Bain's 2026 Private Equity Midyear Report describes a recovery that stalled again in the first half of the year after three rapid market shocks dampened dealmaking, fundraising and exits, with NDA volume data from Ontra pointing to deal flow staying roughly flat through July 2026. Fewer processes, more competition inside each one, and higher entry prices. Speed and conviction are what you have left to compete on.
What to automate first
The sequencing question is more useful than the tooling question. Work that is high volume, document heavy, and repeated across deals produces savings you can measure inside one quarter. Work that depends on judgment about a specific management team does not.
That puts industry primers and market sizing at the front of the queue. Every deal in a sector needs the same baseline view, and rebuilding it from scratch each time is pure waste. Financial statement standardisation is next, because extracting and normalising figures out of inconsistent PDFs is mechanical work that consumes senior time for no reason. Then first-draft screening memos, where the value is a structured, comparable summary of every inbound deal rather than a finished opinion. Competitive landscape mapping belongs here too.
What should stay human: the read on management, the judgment call on whether a growth plan is credible, the decision itself.
The accountability problem
The failure mode with AI diligence is not bad output. It is output that reads well enough that nobody checks it. A market size figure with no traceable derivation is worse than no figure, because it gets into the model and then into the bid.
Three requirements make the difference. Every claim needs a citation you can open. Every assumption a human signs off on needs to be named as an assumption. And the tool has to be consistent across deals, because the point of automation is comparability, not just speed.
Bain's Hugh MacArthur described the industry as having reached an inflection point, noting that multiples are no longer doing the heavy lifting and new deals are harder to pencil out. Firms will not close that gap with faster document review alone. They will close it by knowing more about a market than the person bidding against them, and knowing it sooner.
