Your CEO just got AI FOMO. Here are 6 tips on what to do next.

Every CIO I know has had some version of this conversation: their CEO comes back from a golf trip with their buddy, or a conference with peers, and is told AI is about to automate everything at their company, from HR to marketing and finance. No humans in the loop, just AI. The CEO then calls an all-hands Monday morning, and the CIO is suddenly on the hook to make it all happen.

The instinct for CEOs to chase unsubstantiated claims is understandable since they’re responding to competitive pressure. But that leaves CIOs responsible to close the gap between ambition and reality. Making AI work in an organization with decades of accumulated process, permission frameworks, and cultural inertia is very different from deploying it in a demo.

The best response isn’t to push back on the ambition, but redirect it. Translate the CEOs vision into an honest map of what has to happen for the organization to get there, including the infrastructure, governance, and training. That helps to convert the kneejerk compulsion to move faster into a concrete plan that leadership can get behind.

Here’s what CIOs should actually be focused on to get where their CEOs want them to go, regardless of what’s discussed on the links.

1. Start where AI can build its own credibility

The hype machine wants you to climb Everest on day one. Instead, identify the repetitive tasks where AI can prove itself on familiar ground — the workflows your team already knows well, where results are easy to verify and the bar for trust is attainable.

The goal is the Eureka moment when a skeptic on your team sees a real result and becomes a believer. Those moments compound. When someone has seen AI make their work easier in a context they understand, they’re more likely to help you move things forward. You can’t force that change, but you can engineer the conditions for it.

2. Models will commoditize. Context will not.

Every few months, a new model claims to be smarter, faster, and cheaper than the last one. Don’t be distracted by that race. The lasting advantage in enterprise AI doesn’t just come from which model you’re running, it’s in the quality, governance, and semantic clarity of the data feeding it. Enterprises that invest in consistent business definitions, well-structured data, and clear lineage will outperform those that don’t, regardless of which model is in fashion. Context is your competitive moat. Focus on building that.

3. Nail down the permissions

In a world of dashboards, you know exactly what data will appear on a given page, so you can set permissions in advance for who can access it. In an AI world, the system can generate outputs that were never pre-designed. So how do you determine who has the right to see a result that was never anticipated?

Before deploying any agent that acts on someone’s behalf, such as filing a request, surfacing payroll data, or populating a record, first determine whether your existing permissions and access control frameworks can handle outputs that were never planned for. Most can’t. This is a prerequisite of what your CEO is asking for: the unglamorous infrastructure work that determines whether your AI is trustworthy in production. It needs to happen before you scale, not after.

4. Build an editing culture, not a writing one

For decades, engineers, analysts, and operations teams have been trained to write code, build reports, and define new processes. AI upends that. The skill now is editing — auditing what the system produces, catching what it got wrong, and knowing where to push back.

The truth is most people aren’t naturally good at editing because they’ve never had to be. That’s a skills gap that needs to be closed early on. Invest in helping engineers, analysts, and managers develop the judgment to evaluate AI outputs, not just generate them. Editing must become a core enterprise competency.

5. Measure behavior change, not tool adoption

Login data is a vanity metric. If your engineers are accessing AI coding tools but aren’t changing how they build, you haven’t adopted anything. The metric that makes more sense is productivity output. In agile terms, a team that completes 20 story points per sprint should hit about 28 with AI, not because the tools are magic, but because the repetitive work gets faster. If you’re not seeing that, you’re measuring the wrong thing. Pay attention to output, not usage metrics.

6. Reframe your organization’s relationship with failure

The instinct to de-risk everything made sense when software deployments were expensive and slow to reverse. AI works differently. The outputs are probabilistic, the iteration cycles are fast, and being overly cautious can cost valuable time. CIOs need to give teams permission to experiment in ways that feel uncomfortable by traditional enterprise standards, all while building the feedback loops that make fast failure safe. That culture shift has to be modeled from the top.

FOMO isn’t going away

CEOs will keep getting pulled into cycles of urgency and FOMO, and that pressure will keep landing on CIOs. The organizations that make real progress will be the ones that redirect that energy into infrastructure that makes AI trustworthy, measurement systems that show what’s working, and cultural changes that make adoption stick. That’s the agenda that’ll move your organization forward.