Picture this: I’m sitting in yet another board meeting where the conversation inevitably turns to “What’s our AI strategy?”
But rarely does anyone ask the more important question: “What problem are we actually trying to solve?”
This distinction matters more than you might think.
That’s where the FOMO trap arises in AI adoption. Here’s how:
In my 13+ years deploying AI across Fortune 500 companies, I’ve witnessed a concerning pattern emerging.
With AI dominating headlines, executives feel the pressure and rush to implement AI solutions because they feel left behind —not because they’ve identified specific problems AI can actually solve.
And that’s how you end up in “perpetual POC purgatory” — running endless proofs of concept that never go live because they were never tied to real, high-priority business problems in the first place.
The solution? Start with the business challenge, not the technology.
And my advice is simple: Why use a sledgehammer to hit a nail when a regular hammer will do the job perfectly?
This isn’t about resisting innovation. It’s about being intentional. There are countless proven technologies that could solve your business challenges more efficiently than forcing an AI solution where it doesn’t belong.
So, here’s how you can approach when leadership pushes AI without purpose:
When your board or exec team insists on “doing something with AI,” pause and redirect the conversation:
-“What business outcome are we trying to drive?”
-“What’s the actual problem we’re solving?”
-“Is AI the most effective tool for that — or just the most exciting one?”
After all, your goal isn’t to use AI for the sake of AI. Your goal is to address real business issues that impact your bottom line, customer experience, or operational efficiency.
A Framework for Decision-Making
Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center:
- Define the problem precisely – What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate.
- Consider traditional solutions first – Could existing technology or processes handle this faster, cheaper, and more reliably?
- Lean on experts – If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype.
- Be brutally realistic about your organization’s maturity – Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation?
Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully.
By starting with business problems and being realistic about your organization’s maturity, you avoid the FOMO-driven mistakes that derail so many AI initiatives.
The companies winning with AI right now?
They’re not the ones with the most advanced technology. They’re the ones methodically addressing pressing business challenges with the most appropriate tools — AI or otherwise.
Until next time,
Sol
P.S. What business problem are you trying to solve that might (or might not) need AI? I’d love to hear your thoughts.
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