AI Assessment Tool · CEO & General Management Edition
Assessing AI Competency in CEO & General Management Hires: A Scoring Tool for Boards, Investors, and Founders
We’re tackling AI competency from the top. At the CEO and general management level, AI competency is going to be a lot more than just tool familiarity or a transformation narrative. It is a high-stakes judgement about where AI changes the economics of a specific business, followed by the willingness to fund that judgement and the discipline to measure whether it worked.
This is still a growing and evolving discipline. But as an executive search firm working with high-growth VC- and PE-backed companies, we are in the room when these mandates get framed. This tool is built on that vantage point: what separates a leader who has actually re-underwritten a P&L with AI and moved the business with it from one who only has the story.
A note on scope: This is written for the CEO seat first, with some considerations and applications for divisional GMs, presidents, and other P&L owners. This guide may be used to assess external candidates and/or to assess the bench you already have.
Please note: Every business and every mandate is different. This is a directional instrument for sharpening your own process, not a rubric to apply wholesale. If you want calibrated support on a specific executive search, reach out to Hunt Club.
What’s Shifting in the CEO & General Management Landscape, and What Each Shift Changes About Hiring
- 01What’s shifting in marketMost companies have nothing on the P&L to show for their AI spend. In PwC’s 29th Global CEO Survey of 4,454 chief executives across 95 countries, only one in eight (12%) said AI had delivered both cost and revenue benefits, while 56% reported neither. Confidence in revenue growth fell to a five-year low at 30%. MIT’s Project NANDA research found roughly 95% of enterprise generative AI pilots produced little to no measurable impact on the P&L. The technology largely worked; most orgs didn’t drive it to returns. How this impacts hiringYour baseline is going to have to move. The market is full of executive leaders with a credible transformation story and no delta. So don’t test whether they did AI work (because, well, nearly everyone did to some extent). Test whether a number moved. PwC found the leaders reporting real returns were 2–3x more likely to have embedded AI across products, demand generation, and decision-making rather than sporadic piloting. Sharper hiring processes are testing for exactly this: Evidence and consistency over vision.
- 02What’s shifting in marketThe companies that did get returns changed their org chart, not their tool stack. The organizations that captured value changed shape and footing; the ones that did not more or less just bolted AI onto an existing operating model. Consider this: average span of control moved from 8.1 in 2013 to 12.1 in 2025, with credible projections nearer 25 by 2028 as directors, managers, and individual contributors collapse into lead roles paired with AI. Revenue per employee at the largest firms has been climbing, and AI-native companies now run roughly $1.5M–$3.5M per employee against $310K–$610K for traditional SaaS. That’s not a tooling gap but a different shape of company altogether. How this impacts hiringThis is tricky because most CEOs got the job on rollout: they can get an organization to execute a decision. That still matters, but it is not what produced the returns above. The returns came from companies where layers came out and roles changed, so you need someone who has done exactly that to a degree of magnitude. Your pool may shrink when you hold that line. Hold it anyway (if you want those same returns and business impact).Further, ask what actually changed in the redesign. Which layer came out? What work stopped being necessary altogether? Then push one step further since this is where the expensive mistake happens. Cutting middle management removes the layer that produces your senior leaders later. The candidate you want can tell you what they did/would do about that. The one who only reports the cost saving might not.
- 03What’s shifting in marketThe SEC is now charging companies for AI claims they can’t back up. The SEC brought its first AI-washing cases in March 2024, settling with two investment advisers for $400,000 over claims they could not back up. Investors are moving too. Cornerstone Research and Stanford Law School counted 15 AI-related securities class actions in the first half of 2026, nearly matching all of 2025 and on pace to double it. Those cases were 13% of filings but roughly three quarters of all alleged investor losses. How this impacts hiringCharisma and storytelling are quintessential signals on most CEO and GM scorecards. Leaders need them to sell a vision, recruit, and hold a room. With AI, that same strength turns into exposure and potential liability, because the story now gets checked against what the company can prove. Hiring processes are increasingly going to have to account for this. Alongside the usual read on presence and narrative, you need a read on substantiation: does this person know the difference between what they believe and what they can prove?For sponsors, this is also a heightened diligence and exit-story question. An AI value-creation narrative that cannot be documented may be a liability.
- 04What’s shifting in marketInvestors are repricing whole business models on AI, and fast. In February 2026, investors sold off SaaS companies priced per seat and moved money into hardware and AI infrastructure. Software stocks lost over $1 trillion in 2026 on this. The reason is simple: if AI does the work, orgs need fewer seats, and the pricing model breaks. Investors now ask about your data moat, what happens to your pricing if your customers need fewer people, and what happens to your product if a big AI model ships your core feature for free. How this impacts hiringThis is the question most candidates have never been asked, and it separates a very good operator from an AI-competent chief executive. Ask where their own cost curve and moat sit: what happens to gross margin as usage scales, what their defensibility actually rests on, what breaks if a frontier model ships their core feature next quarter. A leader who talks about AI only as an internal capability has not connected it to the P&L they were expected to run.