Let’s start with the belief itself.
I need to understand AI technically before I can make decisions about it.

This comes from a good place.
You want to be diligent.
You want to avoid costly mistakes.
You’re surrounded by headlines, buzzwords, and people speaking confidently in acronyms.

So, you feel pressure to master the machine before you’re allowed to lead it.
That pressure feels responsible, but it’s a trap.

Because the moment you drop into technical weeds, you stop being a leader and start becoming a student.
At exactly the moment your organization needs you to be a strategist.

Let me be very clear.
Your job is not to know how AI is built.
Your job is to know which problems are worth solving and what a win actually looks like for your business.

That distinction matters more than ever.

So what happens when leaders get this wrong?
They don’t just waste time.
They make real decisions based on imagined capabilities instead of actual ones.

And the consequences aren’t theoretical.

I recently saw a post from an employee who said,
“My company laid off 3,000 people because of AI productivity.”

Pause on that for a second.

A leader hears a phrase like AI productivity and imagines a level of automation and efficiency that simply doesn’t exist yet.
And based on that belief, not reality, thousands of lives are disrupted.

The same thread included another comment that really stuck with me.
CEOs are clueless. If they believe differently, you’re gone.
We all know AI isn’t as good as companies say. But it doesn’t matter.

That’s the danger.

When leadership judgment is distorted by hype, reality becomes irrelevant.
The most confident belief in the room wins regardless of whether it’s true.

And the data backs this up.

A McKenzie survey asked seale executives whether AI had increased their revenues by more than 5%.
The answer 19%.

With all the hype, all the investment, all the promises, less than one in five leaders is seeing meaningful topline impact.

That gap between expectation and reality is where bad decisions live.

So if your job isn’t to become technically fluent in AI, what is your job?

It’s the same job you’ve always had, exercising sound business judgment.

And there’s actually a useful parallel here from the legal world called the business judgment rule.

At its core, it says this.
Courts trust executives to make informed decisions in the best interest of the company.

Even if outcomes aren’t perfect, you don’t need to predict the future.
You need to act in good faith, with diligence, and with a clear business rationale.

Notice what’s missing.
Nowhere does it say must understand neural networks, must know Python, must be an AI expert.

What it does require is judgment.

And AI doesn’t replace that responsibility.
It amplifies it.

So, how does business judgment actually show up in AI decisions?

There are three executive duties that matter.

First, the duty of selection.
Is this tool actually appropriate for the job you’re asking it to do?
Not impressive, not popular, appropriate.

Second, the duty of instruction.
What data context and constraints are you giving the system?
Garbage in is still garbage out. AI doesn’t change that.

Third, the duty of monitoring.
AI systems don’t get set and forget privileges.
They require oversight, feedback, and correction.

That’s not technical work.
That’s leadership.

When you apply judgment properly, AI becomes one of two things.
Either a shortcut to avoid thinking or a tool that helps you think better.

Lazy thinking focuses on cost cutting and replacement.
Strategic thinking focuses on value creation and leverage.

Lazy thinking asks, how do we use AI instead of people?
Strategic thinking asks, what can people do when AI handles the low value repetition?

That difference determines outcomes.

So, let’s be blunt.
Chasing technical fluency as a leader. That’s a distraction.

Your real expertise isn’t in algorithms.
It’s in your customers, your market, your team, and your business model.

If you unlock one idea this year, make it this.
Your business judgment matters far more than your technical fluency.

The leaders actually winning with AI aren’t cramming for machine learning exams.
They’re applying decades of experience to a powerful new tool.

And that leaves you with one final question.

When you look at AI inside your organization, do you see a way to avoid the hard work of leadership or a way to compound it?

Because how you answer that question will define your success in the years ahead.