Nearly every survey shows the same thing: around 95% of CEOs at fast-growing companies say they’re optimistic about AI. But when you look closer, there’s a gap. Many of those same leaders haven’t actually used AI themselves.
That disconnect is more than a curiosity — it’s a credibility problem. How do you lead people through something you’ve never done? You don’t.
Why Delegating AI Leadership Doesn’t Work
Here’s the paradox: most leaders wouldn’t dream of signing off on a new ERP rollout without first digging into its implications. Yet they’re fine mandating AI projects they’ve never touched.
I call this the delegation fallacy — believing you can lead transformation from the sidelines.
The truth is, you can’t. The only way to lead AI adoption is to get your hands dirty. CEOs who use AI in their own workflow quickly see where it helps, where it stumbles, and where human judgment still matters. That’s what builds real credibility.
Adoption Has to Flow Both Ways
AI adoption isn’t a top-down memo or a grassroots experiment. It has to move in both directions.
- From the top, leaders set the tone and show commitment by actually using AI.
- From the bottom, employees discover practical use cases and roadblocks.
- In the middle, managers turn strategy into execution and filter employee insights back up.
When those layers reinforce each other, adoption sticks.
Problem First. Tech Second.
Too many AI projects start backwards. They chase a shiny tool instead of a real problem.
The better question isn’t “How can we use AI?” It’s “What outcome do we need, and is AI the right way to get there?”
The discipline looks like this:
- Pinpoint the problem — and make it specific, not vague.
- Define success — what changes, how you’ll measure it.
- Check alternatives — sometimes process fixes or training are smarter than AI.
- Test for fit — does this problem have the data and scale AI needs to work?
Only then do you talk about tools or vendors.
The Reality Check
The hardest truth: most AI failures aren’t technical. They happen because companies overestimate their readiness.
- Data: It’s not about volume. Is it clean, accessible, governed — and do people care about quality?
- Infrastructure: Beyond storage, can your systems secure models and embed them into workflows?
- People: Do your teams have the literacy and culture to adapt? Do you have the change muscle to absorb disruption?
Sometimes the right first step isn’t a pilot, it’s six months of fixing data governance. That’s not a delay — it’s a smart move that saves you from failure.
The Leadership Imperative
The AI transformations that work share a pattern: leaders treat AI with the same rigor as any other strategic initiative. They start small, learn quickly, and scale carefully. They build internal capability instead of outsourcing everything.
And most importantly, they lead by example.
When a CEO uses AI to prep for board meetings, summarize research, or draft strategy notes, people notice. When that same leader talks honestly about prompt failures or why oversight matters, they earn trust no consultant can deliver.
Boards and investors are already starting to expect this level of fluency.
Moving Forward
Adopting AI isn’t about keeping up with the Joneses. It’s about rethinking how your company creates value. That demands leadership that’s hands-on, focused on outcomes, and brutally honest about readiness.
The question isn’t if your company adopts AI — it’s whether you lead from the front or play cleanup from behind.
In AI, credibility isn’t declared. It’s practiced.
Alan Bukrinsky 
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