The CEOs who get AI right are doing something most of their peers haven’t done yet: owning it.
They’re not delegating it. They’re not sponsoring it from a distance. They’re not accepting activity as evidence of progress. They’re owning it the way a CEO would a revenue target or an acquisition thesis.
And that’s the gap most CEOs haven’t closed. Only 60 percent say they participate in most AI-related decisions, even though 87 percent would stake their job on the results. Which means they’re accountable for outcomes they aren’t in the room to shape. And in PE-backed environments, where buyers are already pricing AI capability at exit, that gap has a dollar figure attached to it. The hold period is finite, and the window for building a track record is narrowing.
The CEOs who act on that window are building something the next buyer will pay for. Here are the six ways they’re doing that:
1. Championing it personally and visibly
Organizational behavior follows CEO attention. When AI gets handed off after the kickoff meeting, the organization reads that signal and responds accordingly. So progress slows, priorities drift, and the initiative too often settles somewhere between IT and middle management. Staying involved means asking hard questions about outcomes at every review and refusing to accept deployment as a proxy for progress.
It also means holding the organization accountable for the connection between AI adoption and business performance, and that accountability can’t be delegated. Because when it is, the data on what happens is stark: according to Accordion’s CFO of 2030 survey, nearly 30 percent of Operating Partners say that the CEO gets in the way of CFO-led transformation, while only 14 percent describe their portfolio CEOs as accelerants. The CEO who owns it personally and visibly is the exception, and their success reflects that.
2. Putting the right operator in charge
The most consequential AI decision a CEO makes is about who leads the transformation. In many organizations, the right leader is the CFO, who sits closest to the financial outcomes transformation is supposed to produce. In PE-backed companies specifically, that connection is even more direct: the CFO owns the metrics that matter most at exit, and AI-enabled finance infrastructure is increasingly what sponsors and buyers are evaluating.
That said, who leads and who’s in the room are two different questions. The CFO owns the outcome, but the room needs range: It needs the people who own the AI value creation and answer for it to the CEO (the business owner), who can define the right problem and prioritize the solution (the product manager), who can quantify the impact and connect it to an EBITDA lever (the business analyst), who can build the solution itself (the tech builder), and who can tell you whether anyone is really going to change their behavior for it to work (the frontline change leader).
Real AI fluency, across the CFO and those in the room with them, gets built through execution: running AI-assisted forecasts, stress-testing autonomous outputs, developing real judgment about where automation creates value and where human decision-making still matters most. Building that capability starts with putting the right person in charge, and ensuring they have the right people around them.
3. Moving beyond productivity gains
There are three levels of AI value creation, and most companies are still operating at level one. Level one is general productivity: time saved and tasks automated. It’s real but it’s essentially invisible to the bottom line. Level two is where the near-term financial impact is: workflow automation in finance, sales effectiveness, and software development as examples. Level three is where the business fundamentally changes: workflows redesigned, operating models rebuilt, AI embedded into how work gets done. And getting there starts with an AI roadmap tied to the value creation plan: assess where the opportunities are, evaluate technical feasibility and value at stake, then prioritize and resource what gets executed first.
The difference in level shows up in our work with clients at Accordion. A PE-backed in-home care provider we worked with reached level three, reducing denials by 80 percent, cutting A/R by 50 percent, and driving $14 million in annual EBITDA uplift. An oncology network we advised on a full digital and operating model redesign unlocked $28 million in annual value. Both were the result of CEOs who defined where AI connected to financial performance, resourced the work accordingly, and held the organization accountable for results.
4. Funding the full transformation
Every $1 invested in AI transformation can deliver an annualized EBITDA uplift of 2 to 4x. The return goes to companies that resource transformation end-to-end: technology build, workflow redesign, and change management, all funded simultaneously. Most organizations resource the first and underinvest in the latter two, and that gap is where the returns go missing.
Businesses can execute one to three meaningful AI transformations per year. That’s a forcing function: prioritization has to follow the value creation plan, and everything outside that plan waits. Which transformation gets funded, and what it has to deliver, is ultimately a CEO decision. The ones who treat it that way are the ones who turn AI investment into exit value.
5. Treating change management as the real implementation challenge
This is the wrench thrown in nearly every AI transformation: a tool gets deployed, outputs improve, but behavior stays the same. The old process runs alongside the new one until someone decides the new one can be trusted on its own. Until that happens, the old one is still the real process.
Most companies still treat this as a tech problem, and they resource it with tech people. But tech people don’t change front-line behavior. That complexity sits in the people and the process, and it needs its own budget and its own owner to get resourced properly.
A PE-backed healthcare company deployed an AI-enabled revenue cycle process and watched its team continue manually reviewing claims, reactively chasing denials and assembling data before acting on it. The shift from doing the work to directing it required new workflows, redefined roles, and deliberate investment in how the team operated. The result was a finance function that managed revenue instead of claims.
The AI tools make that possible. Change management makes that permanent. But it’s the CEO who treats adoption as seriously as deployment that makes it happen.
6. Making it a board topic
AI transformation that isn’t in board reporting isn’t governed like the critical initiative it is. According to Accordion’s PE AI adoption benchmark survey, only 29 percent of companies currently include it. Most AI investments are running without the accountability structure needed to measure performance.
And while productivity stats matter, the profitability metrics are what belong in the boardroom: EBITDA uplift, margin expansion, cash flow improvement, and revenue retention. In our own work with clients at Accordion: one saw $15.5 million in new revenue from an AI agent that watched competitor pricing and adjusted rates in real time. Another recovered $18 million in retained ARR through churn prediction. These are the numbers that tell boards something real about AI’s role in changing the business.
In PE-backed environments specifically, the stakes are moving fast. Eighty-six percent of sponsors expect buyers to place a premium on AI-enabled finance infrastructure within the next two years. The track records that command those premiums get built in the boardroom, and getting AI onto that agenda is a CEO decision.
The cost of waiting
The leaders who get this right treat AI transformation as a leadership decision first and a technology decision second. Every CEO has the ability to make that call. The ones who do, and who put in place the resources to execute properly, are the ones who win.
Make the call.
