How AI Is Becoming Core Infrastructure for M&A Deals

AI’s role in mergers and acquisitions is expanding from a document-review shortcut into deal infrastructure. The important change is not simply that a model can read contracts faster. It is that information extracted during diligence can be organized, checked, and carried forward into drafting, issue escalation, and negotiation preparation.

That makes AI most valuable when it operates as a controlled workflow under professional supervision—not as an autonomous decision-maker.

Why AI is moving into the center of M&A

M&A teams work under pressure from large data rooms, compressed timetables, and the need to connect legal, financial, commercial, regulatory, and operational findings. AI systems can ingest and classify documents, extract structured terms, compare provisions, identify gaps, and surface anomalies across a transaction’s information set.

The market context is adding to that pressure. Mergermarket reported global M&A value of $3.16 trillion in the first half of 2026, up 44% year over year and the strongest first half on record. A Columbia Law School analysis citing a 2025 Deloitte survey reported that 97% of surveyed companies and private-equity firms used AI or automation in due diligence, compared with 69% in 2022.

In a faster, more concentrated deal market, the advantage is not merely completing the same review with fewer clicks. It is getting to decision-relevant risks sooner and allowing senior professionals to spend more time on interpretation and action.

What AI can do across the deal lifecycle

1. Data-room triage and diligence

AI can help deal teams:

• ingest, classify, and organize data-room contents;
• identify duplicates and missing or inconsistent materials;
• summarize documents and extract key commercial and legal terms;
• locate change-of-control, assignment, termination, exclusivity, liability, intellectual-property, privacy, and regulatory provisions;
• compare similar agreements and flag unusual or non-standard language;
• cross-reference information across documents to identify conflicts, gaps, and anomalies; and
• generate structured issue lists, trackers, and draft sections of diligence reports.

These tasks are particularly suited to AI because they involve high-volume retrieval, classification, extraction, and comparison. Legal and industry sources describe AI-enabled diligence as a workflow in which automated analysis is followed by human validation and interpretation.

AI can also flag the absence of expected evidence. For example, a reference to a transaction or asset in one document may prompt a search for related supporting materials elsewhere in the data room. That kind of gap detection can help teams focus their requests and management questions.

2. Drafting and document management

When diligence findings are structured and linked to their source documents, they can inform the next stage of the transaction. AI can assist with:

• first drafts of routine documents and checklists;
• disclosure-schedule inputs;
• clause comparisons against precedents, templates, or playbooks;
• summaries of redlines and changes between drafts;
• extraction of representations, warranties, covenants, and exceptions; and
• tracing proposed protections back to the evidence that prompted them.

The strategic value is continuity. A finding should not remain trapped in a diligence spreadsheet; it should be available when the team decides whether to seek a representation, covenant, consent, indemnity, closing condition, or other protection. Sources describing current M&A workflows identify this movement of information from diligence into drafting and negotiation as a major part of AI’s impact.

3. Negotiation preparation

AI can assemble a deal-team briefing from approved source materials, summarize the counterparty’s proposed changes, identify deviations from a playbook, and organize open issues by topic or priority. It can also help prepare questions and alternative language for human review.

What it cannot do reliably on its own is determine the client’s commercial objective, risk tolerance, fallback position, or bargaining strategy. Those decisions depend on context that may include management relationships, timing, financing, regulatory exposure, competitive dynamics, and the client’s broader priorities.

How responsibilities change by seniority

Junior associates: structured first-pass work

AI is well suited to repetitive, source-linked tasks that traditionally consume large amounts of junior time. With a defined workflow, junior associates may use it to:

• review contracts against a specified set of provisions;
• extract parties, dates, terms, thresholds, and exceptions;
• populate diligence trackers;
• summarize agreements and identify missing information;
• prepare initial disclosure-schedule inputs; and
• draft routine diligence questions for attorney review.

The junior lawyer’s role does not disappear. It shifts toward checking whether the output is complete, confirming that every important conclusion is supported by the underlying document, and escalating ambiguity or material exceptions.

Senior associates: quality control and synthesis

Senior associates are likely to become more important as the workflow becomes faster. Their responsibilities include:

• testing the accuracy and consistency of AI outputs;
• deciding which findings are material;
• synthesizing risks that appear across multiple documents or workstreams;
• translating diligence findings into agreement protections;
• supervising review protocols and escalation paths; and
• converting deal playbooks into clear instructions and validation standards.

This is the layer where raw extraction becomes legal analysis. AI can identify that a contract contains a consent requirement; the senior lawyer must assess its practical significance, determine whether consent is obtainable, and recommend how the issue should affect the transaction.

Partners: advice, risk allocation, and strategy

Partners can use AI-generated issue maps, summaries, and source-linked analyses to focus attention on client advice and the highest-value decisions. Those decisions include risk allocation, commercial tradeoffs, negotiation posture, escalation, and whether a transaction remains attractive.

AI may improve the partner’s access to the facts, but it does not assume responsibility for the advice. Professional judgment remains the control point.

How much faster can AI make diligence?

There is no dependable universal percentage for time saved or for reductions in corporate-diligence headcount. Results depend on the quality and completeness of the data room, the type and complexity of the transaction, the review criteria, the tools used, and the extent of human validation.

A Berkeley analysis reports efficiency gains of 40–70% for some legal and financial diligence work, while emphasizing that the range varies with deal complexity and data quality. Other sources describe substantial reductions in reading, extraction, and compilation time, but these figures are not interchangeable benchmarks for every transaction.

The defensible conclusion is narrower: AI can materially compress the “find, sort, extract, and compare” phase of diligence. It can reduce the lawyer-hours required for routine first-pass corporate review and move senior analysis earlier in the process. It does not remove the need for targeted legal, tax, regulatory, financial, cyber, commercial, or operational diligence.

Nor does faster first-pass review automatically mean a faster closing. Regulatory approvals, financing, negotiations, counterparty responsiveness, signing conditions, and incomplete source information can remain on the critical path.

Effects on M&A teams and law firms

Timelines

Earlier issue spotting can bring management questions, risk decisions, and drafting forward. Teams can spend less time waiting for basic extraction and more time resolving the issues that could affect price, structure, protections, or execution.

Staffing

The likely change is a shift in the composition of work rather than the disappearance of M&A lawyers. Leaner teams may handle routine review, while associates and specialists move sooner to exception analysis, synthesis, and client-facing work. AI-assisted review platforms are already changing how teams allocate responsibility for large document sets.

Pricing

When high-volume review becomes more repeatable and measurable, hourly billing faces greater pressure. Fixed, capped, phased, or value-based arrangements may become easier to structure, but only when firms can measure the work, govern the workflow, and stand behind the quality of AI-assisted outputs.

Associate training

The traditional apprenticeship model—learning transactions through exhaustive document review—becomes less complete when machines perform more of the first pass. Firms will need to teach associates deliberately how to:

• connect contract language to business and legal risk;
• test outputs against source documents;
• recognize omissions and false confidence;
• distinguish a flagged issue from a material issue; and
• turn findings into advice and negotiation options.

The training challenge is therefore not simply learning how to prompt a model. It is learning how to exercise better judgment with faster access to information.

Why human judgment still controls the deal

AI can retrieve, organize, compare, summarize, and suggest. It can process more documents than a human team can review manually in the same period and can make patterns easier to see. But it can also miss nuance, misunderstand context, produce errors, or give an apparently complete answer when the underlying data is incomplete. Human oversight is therefore essential.

Deal professionals remain responsible for:

• setting the scope, objectives, and constraints;
• verifying that conclusions are supported by source documents;
• interpreting findings in the transaction’s factual and legal context;
• deciding what is material;
• choosing which protections to seek;
• assessing the client’s risk tolerance; and
• developing and executing negotiation strategy.

The strongest model is a human-led workflow in which AI handles scale and structure while lawyers and other specialists supply context, accountability, and judgment. AI is becoming core M&A infrastructure precisely because it can make expert attention more targeted—not because it can replace the experts who decide what the deal should mean.