Gen AI in M&A: What the Numbers Actually Mean for Boutique Advisors

The numbers are out — and they're hard to ignore. In a recent McKinsey report on generative AI in M&A, survey respondents using gen AI in active deal processes reported an average cost reduction of roughly 20 percent, with 40 percent saying it enabled 30 to 50 percent faster deal cycles. That's not a vendor marketing deck. That's practitioner data from live transactions.

For a large investment bank with hundreds of analysts, those gains are welcome efficiency improvements. For a boutique advisory firm running three or four mandates simultaneously with a team of six, they're existential. Every hour saved on document analysis, information-memorandum drafting, or buyer outreach is an hour redirected toward origination, relationship management, or closing a deal that's already in flight.

Where the gains are actually coming from

McKinsey identifies the highest-value gen AI applications in M&A as clustering around three phases: target identification and screening, due diligence, and communication drafting. The common thread is that these are all information-dense, repetitive, and time-sensitive tasks — exactly the work that consumes junior and mid-level deal professionals on boutique teams.

In practice, firms using AI in due diligence are feeding financial statements, VDR documents, and management presentations into models that flag inconsistencies, summarise key risks, and answer specific questions in seconds rather than hours. Communication drafting tools are producing first-draft client updates, process letters, and buyer summaries in minutes. These aren't perfect outputs — they still require review — but they remove the blank-page problem and the grinding compilation work.

The boutique advantage — and the risk of falling behind

Boutique advisory firms have always competed on speed and attention. Clients choose boutiques because they get more senior bandwidth, faster turnaround, and sharper focus than they'd get at a bulge bracket. Gen AI tools reinforce that edge — but only if boutiques adopt them. Large firms have dedicated AI transformation programmes and the budget to build proprietary tooling. Boutiques that don't adopt purpose-built AI tools in the next 12–18 months risk ceding the speed advantage that defines their value proposition.

Forty percent of respondents said gen AI enabled 30 to 50 percent faster deal cycles. For boutiques, that's not a statistic — it's a competitive moat.

Where to start

The McKinsey guidance for firms early in their gen AI journey is pragmatic: don't try to automate everything at once. Start with one high-friction, high-volume task — information memorandum drafting, due diligence question lists, or buyer universe research — and build process discipline around one tool before expanding. The firms reporting the best outcomes are those that have defined clear prompting standards and review protocols, not those that have simply given everyone a ChatGPT licence.

For boutique advisors, the most practical entry points are integrated platforms that embed AI directly into deal workflow — so the output lives in the right place, is tied to the right mandate, and is reviewable by the right people, without requiring a separate AI workflow bolted onto an existing stack.

Source: Gen AI in M&A: From theory to practice to high performance — McKinsey & Company.