AI FOMO: The Boardroom Panic Attack Nobody Wants to Admit They’re Having

The Boardroom Script Everyone’s Getting Tired Of

You know the scene. Board meeting kicks off with, “So… what’s our AI strategy?” Cue the uncomfortable silence. Nobody dares ask if that’s even the right starting point.

Here’s the reality: 63% of IT leaders worry their company will fall behind without AI—but they can’t actually name the problem AI would solve. Meanwhile, 55% are scrambling because customers are asking about AI, even if those customers don’t quite know what they want either.

Spotting an AI FOMO Project in the Wild

The warning signs are hard to miss:

• Projects kicked off with “because we need AI” as the entire business case
• Objectives so vague they could mean anything
• Press releases timed to earnings calls instead of actual results
• Everyone nodding along while privately wondering what the point is

If this sounds familiar, here’s the uncomfortable truth: 95% of enterprise AI pilots fail. Only 5% actually deliver the revenue growth everyone’s PowerPoint promised. The rest? Expensive learning experiences and burned-out teams.

FOMO: Not All Bad, But Mostly Problematic

AI FOMO does get things done—it unlocks budgets and creates space for teams to experiment. Some companies even stumble into early wins.

But here’s the problem: Despite $30-40 billion in annual enterprise AI spending, most initiatives deliver nothing to the bottom line. And while leadership pilots approved AI tools, 90% of employees have already gone rogue—using unsanctioned AI in what’s called “shadow AI.” Your people are innovating. Just not in the ways you’re tracking.

The Tale of Two Approaches

The numbers speak for themselves.

* The GenAI Divide: State of AI in Business 2025, MIT NANDA

What the Winners Actually Do

The companies getting AI right aren’t doing anything revolutionary. They’re doing the fundamentals:

• Starting with business pain, not technology. If the problem isn’t costing you money or customers, maybe it’s not the priority.
• Mapping real workflows to find where things actually break down.
• The honest test: “Would we do this project if it didn’t have ‘AI’ in the name?” If not, you might be chasing hype.
• Getting buy-in from executives AND practitioners. Plus having data infrastructure that can actually support what you’re building.
• Accepting that most experiments will fail. But planning to learn from them instead of just moving on to the next shiny thing.

Your Boardroom Survival Kit

Next time the AI pressure mounts, try this approach:

1. Acknowledge reality. Yes, urgency is real. Yes, competitors are moving. No, that doesn’t mean we should panic.
2. Pitch the dual-track. Run disciplined experiments/pilots while solving actual business problems.
3. Make metrics mandatory. Every project needs them—real ones that tie to business outcomes, not just “successful deployment.”
4. Build capability, not theater. Focus on developing internal AI skills instead of just the appearance of innovation.

The 7 Questions That Separate Strategy from Hype

Before you greenlight another AI project, get clear answers to these:

1. What problem are we actually solving? (Be specific)
2. How will we measure success? (Quantifiable metrics only)
3. What’s our baseline? (You need a before picture)
4. Why AI and not another solution? (Sometimes simpler tools work better)
5. Can our data and team handle this? (Be honest)
6. If this fails, what’s the impact? (Know your downside)
7. Does this build long-term capability? (Or just check a box?)

Focus Beats Fear

Executives get paid to see through the noise. AI FOMO is contagious, but the antidote is straightforward: Focus on where AI creates undeniable business value first.

The companies winning tomorrow won’t be the first ones announcing “We did AI!” They’ll be the ones who implemented it wisely, with results to show for it.

Ready to Move Past the Hype?

If you’re tired of AI initiatives that go nowhere, Precocity can help. Using Service Design principles, we work with leadership teams to identify high-impact opportunities, build the data foundation you need, and run disciplined pilots that answer the only question that matters: does this create real value?

Let’s build your AI strategy the right way.