By Kayla Monroe

There is a lot to say about AI right now.

I have been wanting to write about it for a few months but narrowing it down to what got a newsletter spotlight was the hard part. I spent two days at a manufacturing summit last week and knew exactly where to start. 

The leaders that stood up at the front of the room all had real results to share. One company was using AI to catch defects on the line that people had been missing. Another had taken its quote turnaround from days down to hours. The technology was doing what it promised, and they had the numbers to prove it.

But I walked away with perspective than what they intended. 

The companies that were getting results were doing differently. 

Turns out, finding something useful for AI to do is the easy part.  The harder part is whether or not companies are asking the right question. 

Most companies are still asking, “Where can we use AI?”

It is a reasonable question.  You find a task, point the tool at it, and that task gets faster, easier, or cheaper.

So companies keep finding more tasks to run more pilots and more opportunities to apply the technology.

The value of AI does not come from the tool. It comes from redesigning the work around it.

Not just speeding up tasks, but rethinking who does what, which steps still need to exist, how decisions get made, and what the team is actually shaped to do now that the work itself has changed.

When a report that used to take a day now takes an hour, the saved time is the smallest part of the opportunity.

The more important question is what that role, process, or team should look like now that the old constraints are gone.

That is the difference between efficiency and capability. Efficiency gets you time back. Capability gets you an organization that can operate in ways it could not before. Most companies are stopping at the first one. 

They add the tool and leave everything around it exactly as it was.

MIT's NANDA initiative found that 95% of enterprise AI pilots delivered no measurable P&L impact.  So is it the tool itself? Is it lack of data readiness? Is it a change management issue? 

Deloitte found that companies prioritizing redesigned roles, processes, and operating models alongside AI are significantly more likely to realize measurable returns compared to those taking a technology-first approach 

So the issue may not actually be any of those things.  

Companies seeing a real return started somewhere else.

Before they rolled AI out broadly, they looked at the work it would touch and asked whether that work still made sense as it was. They changed the shape of the work first, then let AI operate inside the new design instead of the old one.

The tool created the opening.

What they built around it is what produced the return.

The decision to prioritize redesigning work does not come from a technology implementation team.  It comes from leadership.  

It requires leaders to rethink roles, decision authority, accountability, and how work moves across the organization.

Almost everyone says they intend to work this way.

Accenture found that 84% of executives plan to redesign how their teams work around AI. Only a fraction have actually started.

The intention is to redesign.

The practice, so far, is to add.

That gap is where organizations either get ahead or fall behind.

The next wave of value will not come from another pilot or another tool.

It will come from leaders willing to ask a harder question.

Not “Where can we use AI?”

“What should the work actually look like now that it exists?”

The companies that create the most value from AI will not be the ones that adopt it fastest. 

They will be the ones that use it to build organizational capability they’ve never had before.  

Until next time,

Kayla

P.S. The underlying operating model question is one I work on directly. If that is what you are navigating, email me directly and we can talk about whether it makes sense to look at it together.