The AI myths too many business leaders still believe
(to their detriment)
There are some beliefs about AI that just refuse to die.
They're not malicious, and they’re not entirely irrational. But they’re wrong - and they're holding businesses back.
Here are six of the most persistent myths I’m still hearing from boards, exec teams, and startup founders. If you catch yourself thinking any of these, consider this a friendly course correction.
1. “An AI agent can just run [insert function] for me”
No, it can’t.
We’re nowhere near fully autonomous AI agents that can independently run your sales funnel, HR department or legal team while you kick back with a G&T. Agents are still brittle, hallucinate regularly, and need constant oversight and intensive training.
They can help - dramatically, in fact - but only when they’re well-scoped, monitored, and deployed within clear parameters. Think of them less as “autonomous agents” and more as interns on steroids who need supervision.
2. “AI can’t do [insert thing], so it’s not worth bothering”
This one’s a killer. The assumption is: because a model can’t handle a complex niche task yet, AI must be overhyped.
But here’s the reality: what it can do already is extraordinary. It can parse thousands of documents in seconds, draft credible first versions of strategies or reports, identify inefficiencies across operations, and even spot fraud faster than a human analyst.
If this is the worst AI is ever going to be… why on earth wouldn’t you want to start experimenting now?
3. “I need to hire a Chief AI Officer with decades of experience”
You don’t. They don’t exist.
What you need is someone who understands your business, is genuinely curious, and isn’t afraid to get their hands dirty with experiments. AI literacy matters more than AI legacy. Hire curiosity and give them the room to explore.
4. “We need perfect data before we start anything”
Perfection is paralysis. Yes, garbage in = garbage out. But that doesn’t mean you need a spotless data lake before you begin.
Some of the best AI pilots start with rough data, manual tweaks, and limited scope. What matters is whether you can extract value, iterate fast, and build momentum.
5. “If it doesn’t work immediately, the whole thing’s a bust”
Also false. Most successful AI use cases came after several failed or half-successful attempts. That’s normal.
The first attempt should teach you something. So should the second. The goal isn’t perfection out of the gate - it’s building an internal muscle for experimentation.
The companies winning with AI today aren’t the ones who got lucky with their first use case. They’re the ones who stuck with it long enough to figure out where it worked.
6. “We’ll wait until regulation catches up before doing anything”
A tempting one - but deeply risky.
The companies that wait for regulation to clarify everything will find themselves years behind (because regulation is slow). There are perfectly reasonable, ethical frameworks you can adopt now. And in fact, starting small, documenting your approach, and engaging stakeholders will put you in a far better position when regulation does catch up.
Being frozen by uncertainty is the quickest route to irrelevance.
AI doesn’t require you to reinvent your company overnight. But it does require you to challenge assumptions, rethink processes, and test the boundaries of what’s possible.
Which of these myths have you run into? And what finally helped shift the mindset?