An AI Mindset Shift That Every CEO Needs

There’s a pattern I’ve noticed in AI discussions with business leaders. The energy is real, the intent is strong, and the conversations often kick off with what sounds like a solid question:

"How can we use AI to make our processes faster, cheaper, or more efficient?"

It’s a fair question. It keeps transformation grounded, but is also limiting.

That question assumes the current way of doing things is still valid. The processes that were built over decades are still relevant and the right ones to improve.

But what if they’re not?

What if the real opportunity with AI isn’t to upgrade the system,  but to eliminate the need for it altogether?

This is the Elimination Effect that is increasing possible with AI.  It’s rooted in first-principles thinking. A mindset that challenges assumptions and rebuilds from scratch. And in the context of capabilities that AI brings to the table, it could change the very fabric of how organizations function.

The Elimination Effect emerges when AI doesn’t just automate,  it makes a process obsolete by going back to the basics and rethinking and redesigning the existing approach.

Rethinking Customer Support

Let’s take an example most leaders will relate to: customer support.

The traditional approach is to use AI to cut wait times, free up agent time with AI handling basic queries, or assist agents. And it works  well.

But what if you asked a different question?

Why are customers reaching out in the first place?

Imagine using AI to predict and resolve issues before the customer even knows something is wrong changes the game. Automated refunds, intelligent diagnostics, and self-healing services all powered by data and smart contracts.

That’s not an efficiency gain. That’s a shift from reactive service to an invisible experience that removes friction before it arises.

In that world, the support function shrinks not because it’s automated, but because it’s no longer needed at the same scale.

Retail: Eliminating the Need to Forecast

Forecasting is hard. Even with great data, it’s an exercise in assumptions. So naturally, companies want to use AI to get better at it.

But again, wrong question.

Why do we need to forecast at all?

Today, with AI parsing live signals from consumers, social trends, supply chains, and economic indicators, weather patterns et al, it’s becoming possible to shift from planning to real-time orchestration.

Some retailers are already experimenting with on-demand production models, powered by AI. Some like Zara have succeeded even before the powerful AI tools as they exists today, but opportunity is now for those who have struggled with this approach to use tech and save millions in unwanted inventory and discounts.

When you have visibility and agility, the value of forecasting diminishes. And suddenly, the entire function starts to look like a legacy artifact.

A Strategic Shift for the C-Suite

This isn’t just a tech conversation. It’s a leadership one.

• For CEOs: The challenge isn’t just where to apply AI, it’s what to stop doing altogether.

• For CIOs: Are your AI pilots reinforcing legacy systems or questioning them?

• For CMOs: Are you using AI to push campaigns or reimagining how demand itself is generated, captured, and converted?

The Elimination approach forces leaders to confront a hard truth: much of what exists in your business today exists because it used to make sense then.

But the new world powered with AI brings exciting possibilities.

A Filter Worth Using

At your next strategy offsite or product review, ask your team:

"If this process didn’t exist today, would we build it the same way again?" "Or has AI given us the ability to bypass it entirely?"

The future belongs to those who use AI not just to accelerate, but to challenge.

The boldest organizations won’t be the ones who get better at yesterday.

They’ll be the ones who stop doing what no longer matters.

What are your thoughts?