Portcos Are Being Scored on AI Maturity. Get Ready for the Conversation.

My earlier post on AI Maturity made the case that AI maturity is now priced into exits. This one dives into what buyers are specifically evaluating, and what it actually takes to build a position that survives the scrutiny.

In 2025, 28% of global M&A activity was AI-related, 76% of buyers are already using AI in their own due diligence, and 83% expect it to improve post-merger integration. The buyers across the table from your Portcos at exit are running structured AI maturity assessments. The question is how to prepare your companies for that conversation.

What the Assessment Actually Covers

A structured AI maturity evaluation scores a company across several key dimensions: AI strategy alignment, use case deployment depth, governance and decision rights, data infrastructure quality, talent and capability, technology architecture, and ethical and regulatory compliance.

The distinction that matters most is that AI maturity is not a measure of software purchased. A company with three well-run, well-governed use cases embedded in the revenue model is well ahead of one focused solely on deploying the latest technology. Maturity measures whether AI is changing how the company works and showing up in results. Buyers know how to tell the difference. And they are pricing it accordingly.

Why Mid-Market AI Programs Stall Before They Can Be Scored

Enterprise organizations institutionalized AI discipline through Centers of Excellence — dedicated teams with cross-functional mandates, executive sponsorship, and the organizational staying power to move from pilot to production at scale. That is the north star model: a centralized, accountable function that owns prioritization, governs execution, and measures outcomes against business ambitions — not just technology adoption.

For $20M–$500M PE-backed companies, that discipline is rarely formalized. The operating partner is stretched across a portfolio, and the portco CFO is under pressure to show AI progress without a clear path to begin. The result is point-solutions adopted function by function, without a connecting logic or shared accountability — progress that doesn't compound and doesn't tell a coherent story at exit.

The answer is not to build a COE. It is to execute with COE discipline: a smart, sequenced AI roadmap with clear ownership, governed delivery, and a measurement framework calibrated to the company's EBITDA goals. That is an achievable version of the north star — and precisely the kind of structured approach that translates enterprise-scale AI experience into mid-market results.

Four check-points for a Credible AI Roadmap

For a company heading toward exit in the next two to four years, the real question is if AI investments create a narrative with and EBITDA attached. What builds that credibility can be broken into 4 buckets:

• A scored baseline. A structured AI maturity score to evaluate, benchmarked against industry peers, and can outline the before-state of the value creation story – a verifiable starting point.
• A prioritized use case portfolio. Ranked by business impact, not technical novelty, with ROI modeled at the workflow level before any build decision is made – workflow level models, not category-level estimates, survive diligence.
• A named owner with governance that holds. The use cases that reach production are the ones with a named owner, pre-defined success criteria, and accountability that outlasts the launch energy – the structural gains are easy to show and defend.
• An exit narrative connecting AI to EBITDA. This needs to be built over the hold period, as in a claim that AI drove margin improvement means something supported by two years of documented measurement. 

A Note on Where I've Seen This Work

Much of what I've described above is the framework I've been applying through kriAItiv — starting with a structured AI maturity diagnostic benchmarked against industry peers, translating that into a prioritized roadmap with EBITDA impact modeled before a dollar is spent, and staying engaged through execution until the gains are measurable and the exit narrative holds up.

The firms that get this right, start at acquisition, so there is a recorded baseline that anchors the value-creation story at exit.