How AI is Quietly Transforming M&A Behind the Scenes
AI is already reshaping how M&A deals are executed—often without fanfare. This article examines where it’s being adopted today and what it means for legal and private equity professionals.
M&A has always been a discipline of judgment under time pressure. What’s changing isn’t the need for judgment—it’s the amount of work required to reach it.
Across law firms, PE funds, and in-house deal teams, AI is being adopted less as a headline-grabbing “robot lawyer” and more as a behind-the-scenes capability: speeding up review, improving issue-spotting, organizing knowledge, and reducing the friction that accumulates across diligence, drafting, and integration. McKinsey notes that generative AI can support the end-to-end M&A process—from strategy through due diligence and integrations/separations—precisely because these workflows are information-heavy and resource-intensive.
At the same time, the legal profession is tightening the guardrails around how AI is used. In the U.S., the American Bar Association’s Formal Opinion 512 emphasizes that lawyers using generative AI must account for core duties, including competence, confidentiality, client communication, supervision, and reasonable fees.
What follows is a practical, real-world view of where AI is already reshaping M&A—quietly, but materially.
1) The new engine of diligence: faster triage, not “automatic answers.”
The most immediate, high-impact use of AI in M&A is not replacing diligence teams—it’s helping them triage.
Diligence is fundamentally a scale problem: thousands of documents, tight timelines, and recurring clause families (assignment, change of control, termination, MFN, exclusivity, data/security, IP, etc.). Modern “document intelligence” tools can cluster similar provisions, extract key terms, and generate structured summaries that help lawyers focus time where it matters most. Thomson Reuters has described this trend specifically in the context of M&A due diligence and bulk clause review (including change-of-control/assignment concepts).
What’s quietly changing in practice
Fewer hours spent finding relevant provisions; more hours spent evaluating what they mean for the deal.
Faster “first pass” identification of outliers and anomalies across a contract set.
More consistent issue lists and diligence trackers because extraction is less dependent on individual reviewer variability.
This matters to PE funds and corporate acquirers because the economics of deal execution are sensitive to time and resourcing. And it matters to law firms because clients increasingly expect speed without sacrificing rigor.
2) Contract review is moving from “document-by-document” to “portfolio-level” analysis
Traditional diligence is linear: open a document, review it, write it up, repeat. AI enables a different posture: portfolio-level visibility.
Instead of asking, “What does this one agreement say?”, teams can ask, “Across all agreements, where are the change-of-control consent requirements concentrated?” or “Which contracts have assignment prohibitions tied to vendor approval?” This is a subtle shift, but it changes how quickly a deal team can understand patterns, not just documents.
This trend is a major reason many legal leaders anticipate AI changing staffing and workflow models. A Reuters report (Dec 11, 2025) on large law firms describes how AI can take over foundational tasks often assigned to junior lawyers, nudging firms toward flatter staffing structures and shifting where training and leverage happen.
3) AI is creeping into drafting—but mostly as “structured acceleration.”
In M&A, drafting is rarely a “blank page.” It’s precedent, market practice, negotiated playbooks, and deal-specific risk tolerance.
AI is already being used to accelerate:
first drafts of ancillary documents and checklists,
clause comparison across precedent sets,
“playbook-style” redlining suggestions inside controlled workflows.
But the professional consensus is clear: the lawyer remains responsible for accuracy, risk judgment, and client counseling. Formal Opinion 512 underscores that ethical obligations don’t disappear because AI is used—particularly around competence, supervision, and communication.
A practical takeaway: AI is most defensible when it supports a process you can explain—where humans review, verify, and own the work product.
4) Knowledge management is finally becoming usable at deal speed
Most firms and deal teams have “knowledge.” The issue is retrieval. AI is changing that by making internal know-how searchable in natural language—if governance and security are handled properly.
This is not theoretical. Major legal tech providers are explicitly positioning AI to connect research, drafting, and document analysis into integrated workflows.
For M&A teams, this is particularly valuable in:
quickly finding prior positions taken on recurring negotiation points,
locating fallbacks tied to deal size, industry, or buyer/seller posture,
standardizing disclosure schedules and closing checklists.
5) AI risk is now part of diligence—especially around IP, data, and training rights
As more targets build products with AI or use AI in core operations, deal teams are expanding diligence to include:
data provenance (what data is used, what rights exist),
open-source dependency and license obligations,
model training practices and contractual restrictions,
privacy and security posture as data volumes and automation expand.
Law firms are publishing practical diligence guidance in this direction—for example, on evaluating AI assets, open-source components, and training data acquisition issues in M&A transactions.
And the broader legal environment is evolving quickly. A notable example is the Thomson Reuters v. Ross Intelligence litigation, where Reuters reported a U.S. court ruling (Feb 2025) finding that Westlaw editorial material was protected and rejecting a fair-use defense for certain AI training uses—highlighting that training data rights can become a material legal risk.
Why this matters in M&A: if a target’s AI was built on uncertain rights, that can affect reps & warranties, indemnities, valuation, and even the viability of the product.
6) Ethics and confidentiality: AI adoption is forcing “operating model” decisions
For lawyers, confidentiality is not a feature request—it’s a duty. ABA Formal Opinion 512 explicitly calls out confidentiality and the need to consider ethical obligations when using generative AI tools.
State and local bar guidance has been converging on similar themes. For example, the New York State Bar Association’s Task Force on AI report (April 2024) provides recommendations and cautions about responsible adoption in legal practice.
Operational implications for deal teams
You need clear rules on what can be entered into AI tools.
You need approved, secure tooling (or private deployments) for sensitive matters.
You need training so lawyers understand limitations, not just features.
This is one of the reasons AI is “quietly” transforming M&A: a lot of the real work is governance, procurement, security review, and workflow design—not demos.
What this means for law firms and PE deal teams
AI is not changing the purpose of M&A practice. It’s changing the economics of time in deal execution—how quickly teams can reach high-confidence judgments, how consistently they can manage large volumes of information, and how they staff and price the work.
If you’re advising on transactions today, the most strategic posture is:
adopt AI where it measurably reduces friction (diligence triage, clause extraction, knowledge retrieval),
build controls around confidentiality and verification,
treat AI outputs as accelerators—not authority.
The winners won’t be the teams who “use AI” the most loudly. They’ll be the teams who redesign workflows so that humans spend more time on what only humans can do: negotiation strategy, risk calibration, and client counsel—while the machine handles the repetitive scaffolding in the background.
This article is for informational purposes only and does not constitute legal advice.