M&A in Q4: How AI Is Reshaping Value Creation

Houssard, Kordestani, Liu & Lundberg (McKinsey) — M&A in Q4 and how AI is reshaping value creation

▶ Watch on YouTube · McKinsey & Company · 47:14

As M&A enters the fourth quarter, dealmakers face a critical question: How can they finish the year strong while positioning their organizations for long-term growth? AI is reshaping the traditional M&A playbook, shifting the focus beyond transaction scale toward intelligent platforms and capabilities that compound value over time.

In this episode of Inside the Strategy Room, McKinsey’s Benjamin Houssard, Kameron Kordestani, Alex Liu, and Tobias Lundberg discuss the state of the M&A market heading into Q4 and how AI is changing the way companies approach value creation. They explore how dealmakers can move beyond a series of individual transactions to build a self-reinforcing growth engine.

TL;DR

A 47-minute episode of Inside the Strategy Room on the McKinsey & Company channel, published 30 October 2026, host Sean Brown. Guests: Kameron Kordestani (senior partner, New York; leads North American merger management), Tobias Lundberg (managing partner, Sweden; co-leads M&A in Europe), Benjamin Houssard (partner, Paris; co-leads the global M&A AI initiative) and Alex Liu (partner, Minneapolis). Two halves: the market, then AI. Five claims:

1. A market of mega deals. Deals above $10 billion reached $1.1 trillion in the first half of 2026, up almost 140% on a year earlier and the strongest half on record: 36 such deals, against 60 in all of 2025, holding 42% of global deal value. Corporates lead; private equity, with record dry powder and holding periods about two and a half years longer, waits. Premiums are steady at about 26%; higher multiples reflect the sector mix, not overbidding.
2. AI drives deals. Seven of the year’s ten largest deals are linked to AI, including utilities consolidating because “data centers need power.” AI appears in the narrative of about half of all deals, but far fewer are pure AI plays: much of it is “to get excitement around AI.”
3. Four archetypes of AI M&A. Core AI labs buying ten-person teams that solved one problem; tech-adjacent firms (IT services, software) repositioning, as in Synopsys–Ansys and Capgemini–Syniti; non-tech corporates buying capabilities (the most common for industrial and healthcare companies, e.g. Schneider Electric’s many AI acquisitions); and the talent acquisition through hiring plus licensing, as when Amazon hired Adept’s founding team.
4. Integration as a lever for rewiring. Using the integration to “rewire” the combined company can raise synergies “by at least 50” percent, in some cases double them. Three conditions: a leadership mindset to “swing for the fences,” AI talent, and a clean data foundation.
5. AI inside the deal process. Sourcing goes “always on” (patents, web signals, transcripts); due diligence covers all documents rather than the 15% a team could sample; integration runs on a library of agents per workstream. Awareness of AI for upstream deal work reached 88%, up 51 points in a year; adoption is lower.

Key claims

What AI changes for a deal team

Houssard lists five implications: redefine the target universe (“it might be a 30 person startup that you have never heard about in a sector that you have never screened”); talent is the asset and does not respond to a three-year earn-out but to autonomy and access to compute (“if an AI expert doesn’t have access to the right GPUs he might … go”); speed over thoroughness; integrate lightly (“you don’t want to kill what you are buying with your corporate processes”); and consider joint ventures, partnerships and licensing instead of full ownership. On due diligence: “We could expect faster due diligence. What we actually see is deeper due diligence.”

Where value shows up

Liu: “a fair amount of experimentation” and only point examples of “hard dollar impact.” The mature use cases are software development (“upwards of 45% productivity gains and 70% reduction” in the end-to-end life cycle), customer service (virtual agents and after-call notes for human agents) and finance (invoices, closing the books, forecasts). He describes three levels: make a process faster; virtual agents doing human work; AI plugged into a whole ecosystem, such as inventory forecasting with automatic replenishment. One client is building an “executive cockpit” with agents that recommend direction to functions.

Closing views

Kordestani: “we’re seeing a gap between those companies that are experimenting and adopting, which are now leading the pack, and those that are waiting.” He hopes AI can help understand two cultures before they combine; Lundberg, that it lifts integration capability, the one area where corporate deal skills have stayed flat.

Dynamic-capabilities reading

• digital-seizing/balancing-digital-portfolios: buy, hire, license or partner for AI capability, an explicit internal-versus-external choice with speed of execution as a criterion.
• digital-transforming/redesigning-internal-structures: the integration used to rebuild the data platform, performance management and ways of working of the combined firm.
• digital-sensing/digital-scouting: always-on, AI-driven scanning of patents, web signals and transcripts for targets outside the usual sector.

How it connects

• ai-washing — AI in half of deal narratives, with fewer real AI plays: the same over-attribution the concept documents for layoffs, here in deal rationales.
• strategy — acquisition archetypes as routes to AI capability.
• enterprise-ai-adoption — a transaction used as the forcing event for transformation.
• ai-coding-productivity-evidence — a consultancy’s 45% and 70% figures for software development.

Related in this wiki

• McKinsey’s CEO agenda (the 300th episode, three days earlier) — deploying tools gets “20 cents on the dollar”; redesign gets multiples. The rewiring-through-integration argument here is the deal-team version.
• BBC, What if we’re wrong about AI layoffs? — AI as cover for decisions made for other reasons; McKinsey’s partners say the same of deal announcements.
• West, Lacroix & Zimmerman (McKinsey) — the same series on the step between strategy and execution.

What was actually ingested

The full episode (47:14, length_seconds: 2834) from the auto-generated transcript, 447 lines. ASR corrected against the description and public record: Benjamin Husar is Benjamin Houssard, Cameron Cordistani and Cam are Kameron Kordestani, Alex Louu is Alex Liu, Sha Brown is Sean Brown. The transcript has “synopsis spend 35 million to … acquire NC”: Synopsys’s acquisition of Ansys was about $35 billion. “Capgeemini acquiring Cinity” is read as Capgemini–Syniti. The premium figure is said as “around 20 26%”, read as about 26%. No stills: the video is a static title card over audio.

Linked entities and concepts

• McKinsey & Company — publisher and the speakers’ employer. Updated in this ingest.
• Sean Brown — host. Updated in this ingest.
• ai-washing, strategy, enterprise-ai-adoption, ai-coding-productivity-evidence — updated in this ingest.
• dynamic-capabilities — the three cells tagged above.

Dangling (single-source mention, deferred): Benjamin Houssard, Kameron Kordestani, Alex Liu, Tobias Lundberg, Synopsys, Ansys, Capgemini, Syniti, Adept, Schneider Electric.

Scope and reliability

Consultants describing a market they advise in, with figures from McKinsey’s own deal analysis and surveys, none shown on screen. The AI-and-value half is explicitly early (“a fair amount of experimentation”), and the synergy uplift is a claim about selected situations, not a measured average. The 45% and 70% software figures are stated without a source.