Private equity-backed businesses that can demonstrate a working AI programme, with documented use cases, baseline metrics, and evidence of measurable operational improvement, are beginning to achieve better exit valuations than comparable businesses that cannot. That gap was not visible two years ago, but it is now, and it’s widening.
The shift is from policy document to track record. Buyers are no longer asking whether a target has an AI strategy, they want to know is how AI is integrated into their operating plan (and how they measure ROI). They are asking whether it works, evidenced, measured, and whether what has been built is proprietary or easily replicated.
The following draws on a Mayfair Equity Partners’ Boost! event, where company leaders and operators from across Mayfair’s portfolio examined what AI adoption looks like in practice, and from practitioners who conduct buy-side and sell-side diligence across more than 40 funds.
What private equity buyers now expect to see in AI due diligence
Eight months ago, buyers were broadly satisfied if a target could demonstrate AI awareness. A Copilot rollout, some ChatGPT usage, and a written policy were enough, but that baseline has now shifted. The absence of an executed AI strategy is now flagged as a value drag in formal due diligence reports, and in some cases can even trigger a red flag creating downward pressure on achievable exit multiples.
The current expectation operates across three tiers. Firstly, can the C-suite articulate what the business is doing with AI and why, beyond what IT can tell them? Secondly, are there credible, documented use cases demonstrating AI reducing costs, improving efficiency, or driving revenue? Thirdly, and the one that moves the multiple, is whether what the business has built can be replicated by an AI-native competitor. Businesses that can demonstrate the third tier are the ones attracting a meaningful multiple uplift.
The three root causes of AI programmes that fail to deliver before exit
Research from MIT suggests that around 95% of AI projects fail to deliver expected returns. Reasons identified by practitioners working inside private equity-backed businesses point to three consistent root causes, and none of them is the core AI technology.
The first is data quality. AI systems perform to the standard of the data available to them, and fragmented or inconsistent data creates outputs that management teams cannot trust.
The second is integration. Many AI initiatives remain technically isolated, becoming impressive prototypes rather than operational tools.
The third, and the most significant, is organisational misalignment and lack of capability. AI programmes without a clearly accountable owner above the IT function tend to stall in what practitioners call ‘pilot purgatory’. They are funded enough to continue but are too disconnected from the P&L to deliver or simply do not have the knowledge and capability to do so.
BCG research shows that businesses in the top tier of AI adoption generate 3.5 times greater total shareholder return and 1.6 times higher EBITDA growth than laggards. The difference in outcomes relate to whether these root causes have been addressed, not to which specific model the business chose to deploy.
Why AI has not yet moved the revenue needle in most PE-backed businesses
One of the more candid observations from the Boost! discussions was the asymmetry between where AI is generating results and where it is not. Most AI investment in PE-backed businesses is concentrated in back-office efficiency for faster reporting and reduced manual processing. The front office remains largely underdeveloped.
AI could drive revenue through improved lead conversion, richer customer experience, or accelerated product delivery but, in most cases, it has not done so yet. Even in businesses where AI has roughly doubled development velocity, the improvement has not translated into proportionally faster customer-facing output, because the constraint has just moved downstream to the product management, design, and go-to-market functions.
Acquirers who understand this distinction are asking for evidence of AI in revenue-generating workflows, not just cost-reduction ones. Management teams that can demonstrate both sides of the ledger will be in a materially stronger position.
How to build an AI equity narrative that holds up in diligence
The businesses that perform best in diligence are those that started measuring before they started building. Establishing a baseline is the foundation of any credible ROI claim, whether it’s the cost of a manual process or conversion rate on a sales workflow. Without it, productivity gains are merely asserted rather than demonstrated, and buyers may discount accordingly.
A credible AI equity narrative requires three elements: a named owner for AI programmes at C-suite level; documented before-and-after comparisons on specific workflows rather than generalised assertions about productivity; and an active view on whether what has been built is proprietary or easily replicated.
Businesses that begin this work at, or shortly after, investment generate the track record that holds up in diligence. Those that defer it will be constructing a narrative rather than evidencing one, and buyers are becoming sophisticated enough to distinguish between the two.
The firms that will command the strongest exit valuations in the next two to three years are those that can demonstrate AI as a documented, revenue-generating, and proprietary capability, not those that can show it as a policy on a shelf.
