AI Maturity Is Now a Valuation Variable. Most PE Portfolios Aren’t Ready.

Companies at the highest "AI maturity" levels traded at a median revenue multiple of 31x vs., 13x for those at the lowest (according to McKinsey’s 2026 study on 471 PE-backed companies across 31 industries). The report also states that AI maturity has to extend beyond productivity (level 1) into revenue maximization (level 3 and above) to truly make an impact – wider embrace of AI results in more than twice the median revenue multiple when using AI only for productivity. 

The study has wide implications for the mid-market companies and PE investors that are shifting focus to AI as a valuation criterion, both for investments and exits.

Productivity AI Does Not Move the Multiple

The most common AI applications in PE-backed companies today — automating invoices, summarizing reports, drafting communications — are level one and level two activities. They reduce cost. They do not move the multiple, and per McKinsey, the valuation premium only becomes material at level three, where AI is embedded in the product or service itself. To take advantage of AI, companies need to think Revenue AI - transformations for growth, not just for efficiency.

The question I ask every Portco leadership is simple: which workflows in your business touch revenue directly, and what would it take to put AI there? In telecom, AI-driven churn prediction models are identifying at-risk subscribers and triggering personalized retention offers, resulting in 10% - 25% improvements across churn and lifetime value. In retail, Zara's use of AI embedded into production and distribution decisions, using real-time sales and social trend signals, resulted in faster inventory turn-rate and lower markdowns than competition. Both are using AI embedded in the revenue cycle, directly improving the margins.

Why Mid-Market Companies Are Especially Exposed

Large organizations have been building the foundations – clean data, insights & use cases, cross-functional impacts, governance and orchestration mechanisms – for such scaled AI applications in the revenue cycle. Most mid-market companies have none of that. Fragmented systems, inconsistent data, manual workarounds are just some of the foundational problems that mid-markets face in AI adoption and execution.

Three success points, common across industries, can help reverse the trend:

• Insights on existing data used to scope use cases based on business outcomes.
• ROI modeled at the workflow (use case) level.
• Named point of accountability to orchestrate roadmaps, governance, delivery and measurement.

I have implemented these at enterprise scale – targeting top customer segments for personalized offers, preferred call routing, digital self-service that saves a support call, are just some of the targeted use cases that drive revenue and engagement KPIs for any customer-focused business. By applying these success points to the top performing core areas of their business, mid-markets can exploit the AI value at level 3 implementation (margin driver per McK study).

The Hold Period Is Not Forgiving

The implied capital cycle for buyouts is now approximately seven years. Assessing AI maturity early at acquisition and adopting “success points” can reduce that capital cycle and provide the runway to accumulate the benefits of implementing an AI program – scored baseline, prioritized use cases, governance structure, and a documented track record a buyer can verify. The firms that start this work pre-acquisition/year-1 are better positioned to drive margin improvements and compress the hold period, without sacrificing the investment goals.

The single most expensive mistake I see is treating AI maturity as an amalgamation of AI productivity tools adopted during the hold period. For example, building a real-time customer journey support engine (for digital, retail and support channels) mandates diligent prioritization, model training, content targeting and governance framework. Revenue margin use cases require development and consistent proof-points over the hold period.

An AI maturity baseline taken at acquisition – scored against industry peers, with the highest-impact use cases prioritized and resourced from day one – is what defines the roadmap for the operating partner to build and scale inherent value through the hold period.

What the Market Is Actually Paying For

BCG's AI adoption research puts the performance gap plainly: companies in the top quartile of AI maturity are 2.3 times more likely to hit their value creation targets than lower-maturity peers. And only 11% of PE investors explicitly link AI progress to exit narratives – that is real value left on the table.

The narrative gap is the most solvable, yet most overlooked. In my experience, I have seen that data exists – it just needs to be organized, developed into use cases, and executed at the workflow level in order to realize the upside. I have helped structure this value-chain through: an AI maturity score at acquisition, workflow-level ROI, and an exit narrative that connects AI activity to verifiable EBITDA lines.

The data is clear on what the market is pricing. The question for every PE operating partner is whether AI maturity has been baselined at acquisition – and if the remaining hold period can accommodate the required AI transformations.

AI’s EBITDA Promise – And, why most companies fail to realize it.

A recent Bain & Company study (Sep ’25) cited 20–25% EBITDA gains from AI adoption, with agentic AI visions starting to take shape. While promising, these numbers largely reflect outcomes from tech-forward companies that have an advantage in mature foundational (data) and processing (talent) capabilities.

For most non-tech-forward enterprises, the challenge lies in translating the belief into reality – defining the right AI strategy, setting realistic ROI expectations, aligning investments to business outcomes, and procuring services that align with their overarching goals. In my view, this is where it becomes imperative to work with AI practitioners, who can collaboratively help “translate” the AI promise into an organization’s specific context. These experienced practitioners help outline AI roadmap for strategic advantage, define the right KPIs for ROI measurement, prioritize a step approach to a pragmatic AI investments philosophy, and can be a great "interlocuters" between the technology providers and the functional teams.

The same Bain study also highlights familiar barriers in data silos, IP and security risks, vendor lock-in, and missing standards. I would argue the reality is often worse – many organizations struggle with a fragmented understanding of the benefits AI can truly deliver (and how to measure it), difficulty prioritizing use cases with credible ROI, limited budget flexibility for experimentation, and executive hesitation to place big bets.

This is where AI practitioners can act as “human in the loop,” who helps “shape decisions” like; defining target use cases, outlining budget-talent requirements, creating flexible architectural views, and avoiding common pitfalls, while navigating fragmented data, modeling efficacy, application relevance, and the absence of industry-defining standards.

AI transformation isn’t a technology problem – it’s a business translation problem.