AI maturity due diligence on a target

AI potential quantified before closing

AI maturity is becoming a valuation variable. In its “2026 Private Equity AI Radar” (May 2026), FTI Consulting finds that only 7% of funds have taken AI to enterprise scale in their portfolio companies. Conventional technical due diligence describes the systems in place and leaves two questions open: can the target capture this value, and is its market at risk of being reshaped by AI-native competitors during the holding period?

The automated workflow

A structured process for fast, reliable results.

Business model exposure

The target is positioned using CVC's method described by Bain & Company in “Harnessing Generative AI in Private Equity” (2024): revolution in the very short term, transformation over the next few years, disruption unlikely, with an analysis of the sector's AI-native competitors.

Data foundations audit

State of the systems, quality and accessibility of data, internal skills available, real ability to move from prototype to day-to-day operation.

Use case identification

Processes where AI has a measurable effect in this specific business are identified and ranked by implementation effort and expected gain.

Quantification and roadmap

The selected use cases are translated into EBITDA impact, with a timetable aligned with the holding period, the associated budget and technical dependencies.

Tangible results

3 weeks

Due diligence run alongside the other workstreams

Of funds have taken AI to enterprise scale (FTI Consulting, “2026 Private Equity AI Radar”, 2026)

Day 0

Roadmap available at closing

Up and running in 3 weeks

From specification to deployment, with visible results from the first few weeks.

Rapid scoping (1 week)

Working prototype at the halfway point

Deployment and training included

Frequently asked questions: AI maturity due diligence on a target

A technical audit describes what exists: architecture, technical debt, security. This due diligence looks at two separate dimensions, the target's ability to capture the value of AI and its exposure to erosion from outside. CVC's method described by Bain & Company in “Harnessing Generative AI in Private Equity” (2024) distinguishes three situations, revolution in the very short term, transformation over the next few years and disruption unlikely, and it is better to qualify them before closing than to discover them during the holding period.

The due diligence takes three weeks, run alongside the financial and legal workstreams, with findings delivered in time for the investment committee.

It becomes the basis of the 100-day plan, with prioritised and quantified use cases. It also feeds the exit story.

The sector grid, the map of AI-native competitors and the comparables enrich your knowledge of the market. The work can be reused on the next deal in the same sector.

More workflows: Due Diligence & Committee

-70% time on the first draft

Investment committee memo

An investment committee memo runs to 15 to 40 pages and draws on the information memorandum, advisers' reports, the financial model, market research and minutes of meetings with management. Preparing it takes 20 to 40 analyst hours per deal, 60 to 70% of them spent gathering and structuring material before the first line is written. For a fund working on several deals in parallel, that assembly becomes the limiting factor on the pace of investment.

4 to 6 weeks

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-70% time on the first read

Data room assistant

A mid-market deal data room commonly holds 500 to 5,000 documents, several thousand pages, and the due diligence cycle runs for 4 to 8 weeks with a peak of requests in weeks 2 and 3. Short of time, teams sample and review 5 to 10% of the documents, betting on those reputed to be sensitive. Issues lodged elsewhere surface late, sometimes after the letter of intent, when renegotiation costs the most.

3 to 5 weeks

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See all workflows: Due Diligence & Committee