The AI Question in Every Deal: What Buyers Now Ask Before They Pay

Bain & Company released its 2026 M&A Midyear Report on June 29, and it contains plenty of large numbers: global deal value up 41 percent through May, a market on pace for its second-highest year on record. The more useful finding for business owners sits underneath the totals. AI has moved into the deal thesis, the basic argument for why a buyer pays what it pays, and it is showing up in transactions far outside the technology sector.

What the Midyear Numbers Actually Show

Global deal value reached $2.4 trillion in the first five months of 2026, up 41 percent from the same period last year. At the current pace, full-year dealmaking would top $5.3 trillion, just below the 2020 record of $5.6 trillion. Strategic acquisitions, meaning deals done by operating companies rather than investment funds, rose 36 percent in value even though the number of deals grew only 2 percent.

One figure deserves particular attention: valuations did not move. The median deal priced at 11.6 times enterprise value to EBITDA (a company's total value measured against its earnings before interest, taxes, depreciation, and amortization, the standard yardstick for deal pricing), essentially flat year over year. Buyers are writing bigger checks for bigger companies, not paying richer prices for the same companies. Pricing discipline has survived the rebound.

We wrote earlier this week about the two-speed structure of this market, where a small number of very large transactions carry the headline totals. The midyear report adds a second observation that matters to companies of every size: a common thread now runs through the reasoning behind these deals, and that thread is AI.

Nearly half of all technology deals carry an AI angle, which surprises no one. The more telling examples come from elsewhere. The proposed $119 billion merger of NextEra Energy and Dominion Energy, two utilities, is driven in part by the power demands of data centers. When electric utilities structure their combinations around AI infrastructure, the technology has stopped being a sector and started being a market condition.

The Winner's Paradox, Translated

Bain frames the central challenge of 2026 dealmaking as a "winner's paradox." Companies that win large deals must integrate them while simultaneously running the AI transformation their industries now demand. Both programs compete for the same capital, the same management attention, and the same appetite for organizational change.

The timelines explain why this is hard. Deals above $10 billion take roughly seven months from announcement to close, and another 24 to 36 months to deliver the bulk of their cost synergies. No board wants to freeze its AI agenda for three years while an integration runs its course, and Bain's advice is blunt: waiting is not an option. The firms handling this well treat the integration itself as the moment to redesign workflows around AI rather than bolting the technology on afterward.

That is the acquirer's problem. Here is why it matters if you are a seller. Buyers juggling both agendas now evaluate targets partly by how much a company helps or hinders the dual effort. A business that arrives with systems a buyer can build on advances the buyer's AI plans. A business that arrives with workarounds and undocumented customer relationships adds to the integration bill at the worst possible time.

How AI Is Changing Diligence Itself

The technology is not just a subject of deals. It is now a tool used to examine them. Bain reports that leading acquirers are running AI-enabled analysis across procurement contracts, supply chain data, and charts of accounts, and confirming cost synergy opportunities two to three times faster than traditional outside-in diligence allowed, often with more ambitious targets.

For sellers, this cuts two ways. Due diligence, the investigation a buyer conducts before closing, has historically been constrained by time and staffing. Analysts could sample contracts and test a subset of transactions. AI-assisted review removes much of that constraint. Buyers can now read every customer contract for termination clauses, reprice every vendor agreement against market, and trace earnings adjustments back to source entries. Sellers whose add-backs (the adjustments made to reported earnings to show normalized profitability) rest on optimistic assumptions should expect each one to be tested.

The second edge is more strategic. Buyers are forming an explicit view on every target's AI exposure: which revenue streams are vulnerable to AI-driven substitution, which cost lines should compress as tools mature, and what the business would need to invest to keep pace. That assessment feeds directly into price and terms. A distributor whose margin depends on manual order processing reads very differently from one whose systems already automate it, even if their income statements look similar today.

Answering the AI Question Before a Buyer Asks It

Every buyer who looks at your company from now on will arrive with an answer to a simple question: what does AI do to this business over the next five years? Owners planning a sale in the next one to three years are better off developing their own answer first, with evidence.

Start with an honest map of exposure on both sides. Identify where AI threatens demand for what you sell, and where it could expand your margins or capacity. Buyers respect sellers who name their risks accurately; discovering an unacknowledged risk in diligence damages trust along with price.

Then get the data foundation in order. Clean, accessible operating and financial data has become a valuation asset in its own right, because it lowers a buyer's integration cost and shortens the path to their own AI plans. Companies still running on fragmented spreadsheets are handing buyers a reason to bid lower.

Where you have adopted AI tools, document the results in operational terms: hours saved, error rates reduced, cost per order, customer response times. Measured gains are credible. Broad claims are not. And resist the temptation to build AI-flavored projections that outrun the evidence. An "AI-adjusted" forecast without operating history behind it invites skepticism, and skepticism in diligence translates into lower bids, larger escrows, or heavier earnout structures.

What to Watch in the Second Half

Three signals are worth tracking between now and year-end. First, the funding mix. Stock-plus-cash consideration reached 35 percent of megadeal funding, a historical high, while all-cash deals fell to 55 percent of value, a cyclical low. When buyers pay partly in shares, sellers are being asked to make an investment decision about the acquirer, not just accept a price. That practice tends to migrate down-market when it persists at the top.

Second, financial sponsors. Private equity deal value fell 9 percent through May while corporate acquirers surged ahead. Sponsors still hold considerable uninvested capital, and their return to a faster pace would broaden the buyer pool for mid-sized companies in the second half.

Third, the diligence norms themselves. The AI-assisted review techniques Bain describes at large acquirers are not expensive to replicate, and mid-market buyers and their advisors are adopting them quickly. The depth of scrutiny that once accompanied only nine-figure deals is arriving in transactions an order of magnitude smaller.