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AI Adoption Isn't AI Enablement

A personal perspective on moving beyond AI access toward meaningful business capability.
The more hands-on I become with AI, the more uncomfortable I am with one phrase I hear constantly:
“We need to adopt AI.”
I understand why.
AI is moving incredibly fast, and as business leaders, there is a very real pressure that comes with it.
  • Are we moving fast enough?
  • Are our competitors ahead of us?
  • What are we missing?
I feel that pressure too.

But after spending considerable time working directly with AI, I have started questioning whether “AI adoption” is even the right goal. 

I don’t think it is.

I think the goal should be AI enablement. 

And to me, those are two very different things. 

Adoption is giving people access to AI. Enablement is changing what people and the business are capable of because of AI.

Adoption Is the Easy Part

We have been adopting technology in businesses for decades.
  • We buy the software.
  • We provision the licenses.
  • We configure it.
  • We train people.
  • We encourage them to use it.
Eventually, we measure adoption through usage, logins, training completion and other metrics. Those aren’t bad things. They are often necessary.
But with AI, I think they can give us a false sense of progress.
Giving 100 employees access to an AI tool means that 100 employees now have access to an AI tool. It doesn’t necessarily mean the company has become any better at what it does.
And that distinction matters to me.
I don’t want AI simply present in the organization. I want it to make the organization better.

This Became Personal for Me

In my last article, AI Fatigue, I wrote about what happened when I went deeply hands-on with AI and suddenly found myself accomplishing things at a pace I hadn’t experienced before.
What I didn’t explore there was another realization that came from that experience.
Using AI and being enabled by AI are not the same thing.
My thinking around this changed when I stopped looking at AI primarily from the CEO’s chair and started working with it myself.
I went hands-on.
Very hands-on.
I started using AI in development, architecture, analysis, problem solving and increasingly in the everyday process of turning an idea into something tangible.
That’s when I began understanding the difference.
The biggest gains weren’t coming simply because I had access to AI.

They came when I understood what I was trying to accomplish, had enough knowledge of the problem to challenge the output, gave AI the right context, and incorporated it into the way I was already thinking and working.

  • Sometimes AI gave me exactly what I needed.
  • Sometimes it confidently took me in completely the wrong direction.
  • Sometimes the best thing I could do was stop, rethink the problem and start again.
  • And sometimes I realized that AI wasn’t actually the answer to the problem at all.
That experience changed the way I think about bringing AI into a business.

AI Is Not the Strategy

This may be the most important lesson I have learned so far:
AI itself is not a business strategy.
A business still needs to know what it is trying to accomplish.
  • Respond to customers faster.
  • Help employees spend less time searching for information.
  • Reduce manual steps in a process.
  • Shorten repetitive sales preparation work.
  • Bring together information spread across multiple systems.
  • Reduce a backlog that exceeds our human capacity to execute.
Those are business problems.
AI may be part of the solution.

But I believe we should start with the problem, not with the technology.

That sounds obvious.
Yet businesses can easily find themselves doing the opposite: seeing what AI can do and then searching for somewhere to put it.

What Enablement Means to Me

When I use the term AI Enablement, I mean something much broader than deploying an AI product.

I mean creating an environment where people, processes, data, systems and AI work together to produce a better outcome.

  • AI.
  • Automation.
  • Integration between systems.
  • Cleaning up data that nobody has trusted for years.
  • Changing a business process that should have been changed long before AI arrived.
  • Training people differently.
  • Deciding that a particular problem doesn’t need AI at all.
That’s why I believe we need to stop beginning the conversation with:
“Where can we use AI?”
Start instead with:
“What are we trying to make better?”
Then work backward.

The Human Being Doesn’t Disappear

There is another part of AI enablement that I feel strongly about.
The conversation around AI often gravitates toward what AI can replace. I find myself much more interested in what AI can amplify.
My own experience has reinforced this for me.
  • When I knew the architecture.
  • When I understood the business process.
  • When something didn’t look right and I knew enough to question it.
  • When I could explain not only what I wanted, but why.
AI could move extraordinarily fast.
But speed wasn’t the most valuable part.

The valuable part was what happened when human judgment and machine capability started working together.

That is what excites me.
I don’t want people simply using AI. I want people becoming more capable because of AI.
There is a difference.

Knowing the Problem Does Not Mean Knowing the AI Solution

There is an important gap here that I think we sometimes overlook.
A business leader may understand their problem extremely well and still have no idea how AI could solve it.
And that’s okay.
A CFO doesn’t need to understand how to architect an AI solution to know that month-end reporting consumes too much of the team’s time.
A sales leader doesn’t need to understand large language models to know that preparing proposals is repetitive and slow.
An operations leader doesn’t need to know whether something requires AI, automation or integration to recognize that employees are manually moving information between systems all day.
Business leaders should be experts in the problem. They shouldn’t have to become AI engineers to solve it.

The next step is translation.

Someone has to bring the business understanding and the technical understanding together and determine what the solution should actually look like.

  • Sometimes the answer will be AI.
  • Sometimes it will be traditional automation.
  • Sometimes it will be integration or process redesign.
  • Increasingly, the most powerful solutions will combine several of them.
That translation – from a business problem to the right combination of capabilities – is a critical part of what I mean by AI enablement.

If You’re Starting, Start Here

If another business leader asked me today how they should begin their AI journey, I wouldn’t start by recommending a platform.

I would ask them to pick one real business problem.

Choose something that takes too long, creates frustration, produces errors, consumes valuable human time or simply doesn’t work as well as you know it could.
Understand how it works today.
  • Document the people involved.
  • Identify the information required.
  • Map the handoffs and time being consumed.
  • Note where mistakes occur.
  • Identify where human judgment matters.
Then define what better looks like.

You don’t need to know how AI will solve it.

That’s the next part of enablement: bringing the right business and technical expertise together to determine whether the answer is AI, automation, integration, process redesign – or some combination of them.

Then start small.

  • Build it.
  • Learn from it.
  • Measure what actually changed.
  • Take what you learned to the next problem.
To me, that is a much healthier starting point for AI enablement than buying technology and searching for somewhere to use it.

From Adoption to Enablement

I’m applying this same thinking in my own business.

I’m experimenting aggressively with AI, but I’m also becoming much more deliberate about why we’re using it and what I expect to change because of it.
I don’t have every answer.
I don’t think any of us do.
This technology is moving too quickly for that.
My thinking will continue to evolve as I experiment, build, succeed, fail and learn.
But one belief is becoming stronger for me.
  • I don’t want to measure our AI journey by how many tools we deploy.
  • I don’t want to measure it by how many prompts we write.
  • I don’t want to measure it simply by how many employees are ‘using AI.’
I want to know whether we made something meaningfully better.
  • Did we remove work that didn’t need to exist?
  • Did we help someone make a better decision?
  • Did we give an employee time back?
  • Did we improve the experience of a customer?
  • Did we unlock something we previously didn’t have the capacity to do?
  • Did we make our people more capable?
That is AI enablement to me.
So the question I believe we should ultimately be able to answer is:
“What has our organization become capable of because of AI?”
And if we can’t answer that yet, perhaps we haven’t enabled AI.
Perhaps we’ve only adopted it.
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