The modern M&A process starts the same way it always has. Someone believes one company should buy another, and a small group of people works to support that idea. What sets today apart is how quickly these justifications can be put together.
A banker can now pull up a decade of pitch materials, carve out the relevant pieces, and produce something that looks like experience. A diligence team can ask a system to scan thousands of documents for unusual clauses and missing consents, then spend its time arguing about what matters, not where it is. An executive can stress test a synergy thesis in an afternoon, then show up to the next meeting with a confidence that feels earned, even when it is partially borrowed from a model.
AI is creeping into deals the way spreadsheets once did: first as convenience, then as default.
The new front end of dealmaking
The biggest shift is upstream, before anyone signs an NDA. Deal sourcing has always been a mix of relationship and pattern recognition, with a lot of pattern recognition hiding inside relationships. AI is starting to widen that funnel, turning what used to be a whisper network into something closer to a searchable map.
That shift is visible in the tooling vendors and the acquisitions around them. When dealmaking platforms buy “private markets intelligence” businesses that run on AI, they are betting that the next edge comes from getting to the right target earlier, with a clearer picture of where value might be hiding. Datasite’s acquisition of Grata, described as an AI-driven private markets intelligence platform focused on deal sourcing and due diligence, is a clean example of that direction of travel.
The promise is not that an algorithm discovers the perfect target on its own. It is that the long list gets longer, cheaper, and less dependent on who happens to know whom.
Due diligence gets a first draft
The most practical change is in diligence. This work has always been both costly and distinctly human. It involves reading, summarizing, checking, re-reading, and explaining your findings to people who lack the time to read what you read.
McKinsey’s recent work on “outside-in diligence” describes the new workflow plainly: generative AI can take the first pass, synthesizing large volumes of public and proprietary data, identifying trends and outliers, and proposing hypotheses that analysts might not have considered. The caution in that same framing is important too. Many organizations have not yet cracked the operating model that consistently turns tools into impact.
Vendors are racing to make that first pass feel native inside the deal room. Virtual data rooms, historically built for secure sharing and permissions, are adding assistive AI features like automated redaction and review workflows, with the positioning that speed is valuable only if it stays controlled. Datasite, for example, markets AI-enabled tools such as automated redaction, and has pushed newer “Redaction AI” features that still require human review and confirmation.
This is the dealmaker’s version of a change happening across the knowledge economy: AI compresses the time between raw information and a usable narrative. The advantage goes to the team that can validate the narrative fastest, not the team that can generate it.
“In M&A, the winner is often the side that turns information into conviction first.”
Bankers and lawyers become editors of machines
If diligence is where AI changes the muscle, banking and legal teams are where it changes the cadence.
Reuters has reported that Goldman Sachs rolled out a firmwide AI assistant, with around 10,000 employees already using it at the time of the internal memo cited by Reuters. More broadly, large U.S. banks have been describing measurable productivity gains from AI in operations and coding, and executives have been blunt about the implication that doing more with fewer people is now part of the plan.
In M&A work, this means a subtle shift in how labor is distributed. Junior staff still create models and presentations, but their value now lies in verifying, contextualizing, and identifying what the model missed. The focus shifts from creating the initial version to stress testing it. Teams that use AI effectively begin to resemble newsrooms rather than factories. They produce quick drafts, make constant edits, and maintain a strong emphasis on what is true.
Deal structure is changing because regulators are watching
The most underappreciated disruption is not in the spreadsheet. It is in the legal form of the deal.
As AI becomes strategically central, acquisitions and partnerships in the sector have drawn unusually intense scrutiny. The FTC launched a 6(b) inquiry in early 2024 into generative AI investments and partnerships, sending orders to Alphabet, Amazon, Anthropic, Microsoft, and Open AI. In January 2025, the FTC issued a staff report on certain cloud provider and AI developer partnerships, highlighting concerns like lock-in, switching costs, and access to sensitive technical and business information.
That scrutiny has real behavioral consequences. Companies have learned that if a traditional acquisition triggers a fight, there are other ways to get what they want: talent, models, and distribution rights.
Microsoft’s unusual arrangements with Inflection AI became a reference point for this style of “almost acquisition.” Reuters reported that U.S. regulators were looking into the Microsoft Inflection deal in part over concerns it might have been designed to skirt merger disclosure requirements. The UK’s Competition and Markets Authority opened an inquiry and later cleared Microsoft’s hiring of certain former Inflection employees and associated arrangements, treating the situation as a merger inquiry under UK rules even as it ultimately closed the case.
M&A lawyers are already absorbing the lesson: the substance of control, not just the paperwork, is becoming central. The deal is no longer only what you buy. It is how you access models, compute, talent, and data without triggering the harshest form of review.
AI compliance becomes a diligence item
AI is also changing what counts as a material risk.
A decade ago, diligence obsessed over revenue recognition, change-of-control clauses, and litigation exposure. Now, teams increasingly need to understand a target’s model dependencies, data rights, and governance practices. In Europe, this intersects with the EU AI Act’s general-purpose AI obligations, and the voluntary General-Purpose AI Code of Practice published on July 10, 2025, which is positioned as a tool to help providers demonstrate compliance on transparency, copyright, and safety and security.
Even for buyers outside the EU, this matters because global products rarely stay local, and compliance expectations travel through customers, partners, and regulators. In practical terms, AI diligence starts to resemble cyber diligence: less about whether risk exists, more about whether the company has a credible system for managing it.
In the best deals, this does not slow things down. It changes what “fast” means. Fast becomes the ability to answer hard questions with evidence, not the ability to move past them.
AI will not eliminate the most human parts of M&A: the politics, the ego, the leap of faith.
What it will do, and is already doing, is make every stage more legible and more contestable. When both sides have machines that can draft the story, the advantage shifts to the side that can prove it.