A few years ago, the AI question executives traded over coffee was simple: are you doing anything with it yet?
That question has quietly expired. Almost everyone is doing something with AI now, and saying so no longer tells a peer anything useful. What has replaced it is sharper, more specific and often more uncomfortable. Leaders want to know what actually worked, what it cost, who signed off, and what went wrong when nobody was watching.
That shift matters. The AI questions executives ask each other are a fairly honest signal of where the hard problems have moved, and right now they've moved from technology choices to accountability, measurement and judgment.
Adoption Stopped Being the Interesting Question
The numbers explain why. Stanford's 2026 AI Index Report puts organizational adoption of AI at 88%. When nearly nine in ten organizations report using it, asking whether a peer's company has adopted AI is like asking whether they use email.
The pace has been just as striking. Research from MIT Sloan Management Review and Boston Consulting Group found that adoption of traditional AI climbed to 72% over eight years, generative AI reached 70% adoption in just three years, and agentic AI hit 35% adoption in two years, with another 44% of organizations planning to deploy it soon.
Each wave arrived faster than the last, and each one brought a new set of decisions before the previous set was settled. That's the context executives carry into a room with their peers. They aren't looking for reasons to start. They're looking for help with what starting created.
The Questions Executives Are Asking Now
Listen to a group of CIOs, CTOs or CFOs talk candidly about AI and a handful of questions come up again and again. None of them are about which model is best.
"What Did It Actually Change?"
This is the value question, and it's usually asked with some skepticism. Plenty of organizations have pilots that demoed well and dashboards full of usage metrics. Fewer can point to a process that runs differently, a cost line that moved or a decision that got made faster because of AI.
Executives ask peers this question because the answer is hard to get anywhere else. Vendors report on adoption. Internal teams report on activity. A peer who has been through a full budget cycle with an AI program can tell you whether the savings showed up in the numbers or only in the business case.
"Who Answers for It When an Agent Acts?"
Agentic AI changed the accountability conversation. A system that can plan, take action and adapt as it goes doesn't fit neatly into existing ownership models. In the MIT Sloan Management Review and BCG survey, 76% of respondents said they view agentic AI as more like a coworker than a tool.
That framing raises questions most org charts can't answer yet. Who approves what an agent is allowed to do? Who reviews its decisions? What happens when it makes a mistake that no individual person made?
Governance hasn't caught up. Deloitte's 2026 State of AI in the Enterprise research, based on a survey of 3,235 IT and business leaders in 24 countries, found that only 21% say their organizations have a mature governance model for agentic AI. Yet 74% expect their companies to be using AI agents at least moderately by 2027. That gap is exactly where peer conversations get practical.
"How Do You Know the Output Is Right?"
Verification has become one of the most common questions in technology leadership circles, because AI performance is uneven in ways that are hard to predict. The Stanford AI Index describes a "jagged frontier": a model earned a gold medal at the International Mathematical Olympiad, yet the top model reads analog clocks correctly just 50.1% of the time. AI agents improved sharply on real computer tasks, but still fail roughly one in three attempts on structured benchmarks.
For executives, that unevenness is the whole problem. A tool that is brilliant at one task and unreliable at a nearly identical one needs review processes, testing and clear human checkpoints. Peers want to know how others have set those up without slowing everything to a crawl.
"What Did You Stop Doing?"
This one rarely shows up in conference keynotes, but it comes up constantly in private conversation. AI programs add tools, contracts, data pipelines and review steps. Leaders who have been at it for a while start asking which experiments they retired, which licenses they let lapse and which manual processes they finally turned off.
It's a mature question. It signals that an organization has moved past experimentation and into portfolio management, where the discipline of saying no matters as much as the enthusiasm for saying yes.
"How Are Your People Taking It?"
Workforce questions have become more direct. Executives ask each other how teams have responded, which roles changed, how training is structured and how honest they've been with employees about where AI is headed. These are delicate topics inside a company, which is precisely why leaders prefer to test their thinking with someone outside it first.
Why These Questions Go to Peers First
Every one of those questions could, in theory, be answered by a consultant, an analyst report or a vendor. In practice, executives often take them to peers before anyone else, and for good reasons.
Vendors have a stake in the answer. Analyst research aggregates experience across many organizations, which is useful for spotting trends but less useful for deciding what to do on Monday. Internal teams know the company well, but they also have budgets, reporting lines and reputations attached to the AI projects they champion.
A peer who runs a similar function at a different company has none of those conflicts. They can say a pilot failed, explain why, and move on. That candor is hard to find, and it's one reason peer-led events help executives cut through technology hype in a way a product demo can't.
The Value of a Failed Pilot
Some of the most useful AI conversations between executives are about things that didn't work. A CIO describing why an internal assistant never gained traction, or a CFO explaining why an automation project cost more to maintain than it saved, can spare a peer months of effort. Those stories almost never appear in case studies, which is exactly why they're worth so much in a closed room.
How the Question Changes by Role
The same technology produces different questions depending on who's asking.
CIOs
CIOs tend to ask about architecture, data and operating models. Where should AI capability live, in a central platform team or embedded in business units? How much of the data estate is actually ready? How do you keep dozens of AI tools from becoming the next generation of technical debt? Those questions are a staple of CIOMeet conversations, and they connect directly to deciding where agentic AI fits in enterprise IT and where it can go wrong.
CTOs
CTOs focus on how AI changes the way software gets built and shipped. They ask about code quality, testing practices, developer productivity claims and how to evaluate AI features before they reach customers. Engineering leaders comparing notes at CTOMeet often find that peers solved a reliability problem in a way their own teams hadn't considered.
CFOs, CISOs and GRC Leaders
Finance leaders ask how to fund AI without losing control of spend, and how to tell real productivity gains from accounting optimism. Security leaders ask about new attack surfaces and data exposure, and the Stanford AI Index gives them reason to: documented AI incidents rose to 362, up from 233 in 2024. GRC leaders ask what evidence they'll need when auditors or regulators come asking.
Many of these teams anchor their thinking in the NIST AI Risk Management Framework, released in January 2023 and extended with a Generative AI Profile in July 2024. NIST notes that the framework is now being revised. Because these concerns overlap so heavily, it's no surprise that CIOs, CISOs, CFOs, CTOs and GRC leaders are converging around AI governance.
Asking Better Questions in the Room
Peer conversations about AI are only as useful as the questions that start them. Executives who get the most out of these exchanges tend to follow a few habits.
- Bring context, not just curiosity. "How are you using AI?" invites a generic answer. "We have an agent drafting vendor contracts and legal wants every output reviewed. How did you handle that?" invites a real one.
- Ask about the second year. Launch stories are easy to tell. What happened after the pilot team moved on is where the lessons live.
- Ask what they'd do differently. It's the fastest way to get past the polished version of a story.
- Ask about numbers carefully. Peers will often share ranges and direction even when they can't share specifics. Respect the line they draw.
- Follow up. The most valuable conversations rarely finish at the event. A short note a week later often gets the answer that didn't fit into the first conversation.
None of this is complicated. It just reflects the reality that AI has moved from a topic executives discuss in the abstract to one they're managing every week.
What Hasn't Changed
For all the new questions, the underlying reason executives turn to peers is the same as it's always been. Leaders face decisions with incomplete information, and the most trusted input often comes from someone who has already faced a similar decision and has nothing to sell.
AI has raised the stakes and shortened the timelines, but it hasn't replaced judgment. If anything, it has made judgment more valuable, because so much of the work now involves deciding what a system should be allowed to do and when a human needs to step in.
That's why CXO Inc. builds its communities around candid, peer-led conversation rather than presentations. The questions are changing quickly. The value of asking them among trusted peers isn't.
FAQ: AI and Executive Peer Conversations
How is AI changing the questions executives ask their peers?
Executives have moved past asking whether peers use AI. They now ask what it actually changed, who is accountable for agentic AI decisions, how output gets verified, what was retired and how the workforce is responding.
Why do executives ask peers about AI instead of vendors?
Vendors have a stake in the answer and internal teams have projects to defend. A peer running a similar function elsewhere can share what failed and why, without any conflict of interest.
How widely have organizations adopted AI?
Stanford's 2026 AI Index Report puts organizational AI adoption at 88%. MIT Sloan Management Review and BCG found agentic AI reached 35% adoption in two years, with another 44% of organizations planning to deploy it soon.
Is governance keeping pace with agentic AI?
Not yet. In Deloitte's 2026 State of AI in the Enterprise research, only 21% of leaders surveyed said their organizations have a mature governance model for agentic AI, while 74% expect to use AI agents at least moderately by 2027.
What makes a peer conversation about AI useful?
Specific context, questions about what happened after launch, honesty about failures and a follow-up after the event. Generic questions get generic answers, while detailed ones surface lessons that rarely appear in case studies.
Better Questions, Better Decisions
The AI conversation among executives has grown up. The AI questions executives ask today are less about excitement and more about ownership, evidence and tradeoffs, which is exactly the kind of conversation that benefits from people who have already lived through it.
For leaders who want to compare notes with peers working through the same questions, CXO Inc. brings CIOs, CTOs, CISOs, CFOs and GRC leaders together in focused, peer-led communities. More executive perspectives are available through CXO Inc. Insights.
