Generative AI in M&A: 2026 McKinsey Update
- Jan Tomaszewski
- 2 hours ago
- 2 min read
What McKinsey & Company found about Gen AI in M&A
January 14, 2026
Value is already showing up in real-deal work, not just pilots. In McKinsey’s survey, respondents using gen AI in M&A reported an average ~20% cost reduction, and 40% said gen AI enabled ~30–50% faster deal cycles.
Adoption is still shallow. Even with the hype, McKinsey found that only ~30% of respondents engage with gen AI at moderate to high levels, and many still rely on generic chatbots rather than proprietary, deal-specific tools, often because of a lack of expertise.
Today’s “highest-traction” use cases: target ID + diligence. Among moderate/high adopters, the most common uses are target identification and due diligence, with tools that combine LLMs trained on strategy/deal materials with ML clustering to sort large universes of targets.
The practical implications across the deal lifecycle
1) Target identification is moving from “search” to “scouting at scale.”
McKinsey describes gen-AI-enabled scouting platforms that can evaluate huge target universes quickly—one example scored 500+ targets in <1 day, narrowed to 15 deal leads, and contributed to three acquisitions within months.
They also predict that tools will evolve into more “strategic partner” systems that can ingest strategic signals (earnings calls, patents, etc.) and propose targets—estimating that dependable end-to-end gen-AI-powered M&A tools could be available in ~2–5 years.
2) Diligence is being compressed—and will become “continuous + connected.”
McKinsey frames diligence as a heavy coordination + document burden (meetings, emails, data-room content, Q&A), and notes current tools already help search/summarize/organize diligence materials and answer common questions.
They estimate that within ~2 years, gen AI will be strong enough to make diligence a continuous, connected part of the deal cycle, feeding insights into screening and post-close integration planning (and learning across deals).
3) Integration: automation is coming fast, but needs playbooks and oversight
McKinsey emphasizes the importance and scale of integration (dozens of teams, multi-year timelines) and says that gen AI agents can already draft “day one” readiness plans and communications—though they still require human judgment.
They estimate that in ~2–3 years, tools may automate more than half of integration-related tasks and recommend that companies refine and document their integration playbooks now to benefit immediately as tooling improves.
How this tie directly to RedlineDCS as end-to-end M&A execution software
McKinsey’s throughline is simple: gen AI accelerates M&A when your deal work is already structured—with clean inputs, consistent workflows, and reusable playbooks. That’s exactly where DCS sits:
System of record for diligence execution: a secure VDR + deal workspace that keeps documents, requests, versions, and approvals in one place—so AI (now or later) can work on organized deal artifacts instead of chaos.
Repeatable workflows (the “playbooks” McKinsey is calling for): standardized checklists, trackers, templates (NDA → diligence → signing) that create consistent process data deal over deal.
Speed + risk control where it matters day-to-day: fast onboarding, permissions, redaction/watermarking, and signature workflows—so you compress cycle time without increasing leak/risk.
AI-enabled assistance where it’s most valuable today: summarizing, extracting, and explaining document terms (your “diligence assistant” layer) while the core platform keeps everything governed and auditable.
In other words: if gen AI is the acceleration engine, DCS is the roadbed—the execution layer that makes diligence and integration work ready for automation, and measurably faster right now.