AI Is Now a Standard Diligence Question in Private Equity — Here's the Framework

AI has crossed a threshold in private equity: it is now a standard diligence question, and a PE diligence operating assessment that ignores it is incomplete. The shift is visible in the capital. AI-related PE deal value tripled from $41.7 billion in 2023 to $140.5 billion in 2024 and kept climbing in 2025 — reaching 8% of all PE deal value, up from 3% a year earlier. When a category triples and then doubles its share of total deal value, it has stopped being a theme and become a diligence standard.

The question buyers ask has changed shape. It used to be 'what are we piloting?' Now it is sharper and harder: where does AI sit in our value creation plan, who owns it, what numbers is it moving, and what does the next buyer need to see? That reframing moves AI out of the innovation sandbox and into the value creation plan, where it has to carry ownership, metrics, and a credible story for the eventual exit.

AI · 01The three-step framework buyers expect

AI · 02Why the sequence matters

The three steps are a sequence, not a menu, and the order is the insight. Companies that skip straight to building proprietary AI before redesigning the work tend to build sophisticated tools that automate a broken process. Companies that buy general tools but never redesign the work capture little, because the tools queue against unchanged workflows. The progression — buy, redesign, build — front-loads quick wins, then captures workflow value, then creates the defensible advantage that survives diligence. Skipping a step breaks the compounding.

This sequence mirrors the broader logic of value creation plan sequencing: order of operations determines whether effort compounds or stalls. AI is no exception. The buy-redesign-build progression is the AI-specific expression of the same discipline that governs every value creation initiative.

AI · 03What the next buyer needs to see

The final clause of the new diligence question — what does the next buyer need to see — is the one that disciplines everything. AI work undertaken without a view to the exit produces interesting capabilities that don't translate into valuation. AI work undertaken with the exit in view produces evidence: workflows measurably faster or cheaper, a proprietary capability competitors can't easily replicate, and a story anchored to numbers rather than potential. The diligence-ready company can show what AI moved; the pilot-stage company can only describe what AI might.

This is why AI has become a diligence operating assessment rather than a technology checkbox. Buyers digging into a target want to know whether AI is integrated into the operating model and moving real numbers, or whether it is a collection of experiments that will need to be rebuilt under new ownership. The answer is increasingly a valuation input — which means the AI agenda has to be run with the same rigor as any other value creation lever, owned and measured and built toward proof.

AI · 04The numbers that made AI a diligence question

The shift from curiosity to diligence requirement is visible in the deal data. AI-related PE deal value tripled from $41.7 billion in 2023 to $140.5 billion in 2024 and kept climbing through 2025 — reaching roughly 8% of all PE deal value, up from 3% a year earlier. When a category moves from 3% to 8% of total deal value in a single year, it stops being a thematic bet and becomes a standard line of inquiry. Buyers now ask about AI the way they ask about customer concentration or working capital.

The question itself has matured. It used to be 'what are you piloting?' Now it is sharper: where does AI sit in the value creation plan, who owns it, what numbers is it moving, and what does the next buyer need to see? Each clause is a diligence test — strategic placement, clear ownership, quantified impact, and exit-readiness. A company that can't answer all four is signaling that its AI is activity rather than value.

AI · 05The three-step framework buyers expect

The sequence matters as much as the steps. Companies that skip straight to building proprietary AI without first redesigning the work tend to automate broken processes at great expense. Companies that buy tools but never redesign the work get adoption without scale. The framework's logic is that durable AI value is built in order — baseline capability, then workflow redesign, then proprietary advantage — and buyers increasingly diligence against exactly that progression.

AI · 06What the next buyer needs to see

The diligence framework matters because it now runs in both directions: a company being acquired is assessed on its AI position, and a company preparing to exit has to anticipate what its own buyer will ask. The question has shifted from 'what are we piloting?' to where AI sits in the value creation plan, who owns it, what numbers it is moving, and what the next buyer needs to see. AI-related PE deal value tripling from $41.7 billion to $140.5 billion is the market signal that this is no longer optional diligence. A company that cannot answer the AI diligence question crisply signals operating risk, and that signal is priced into the multiple exactly as weak reporting or fragile margins would be.

For the company preparing to sell, the framework is also a readiness checklist. Buyers increasingly expect a clear answer to where AI sits in the value creation plan, who owns it, and what numbers it moves — and a company that has built that answer deliberately enters diligence with an advantage. The framework buyers use to assess AI is the same one a portfolio company should use to build it: buy general tools, redesign the work around them, then build something proprietary on top. A company that follows that arc has a defensible AI story; one that bolted on tools without redesigning the work has a collection of pilots that diligence will discount.