AI Adoption Isn’t Enough. Time to Accelerate.

I wrote this after a pretty interesting weekend.

I just dropped my son Sam off at college. I met a bunch of his buddies, hung out with them, and talked about what they want to do with their lives. Internships? Jobs? What sounds interesting? What does not? In short, all that stuff you start thinking about when you realize college eventually ends, and someone is actually going to expect you to get a job.

And these kids want to work.

Seriously. They want internships. They want experience. They want to learn cool stuff and work with interesting people. They want to make an impact. They do not seem overly concerned about titles or climbing some corporate ladder yet! They just want an opportunity to get involved and learn.

At some point, we started talking about AI, and they became really curious about how we are using it in the enterprise. How companies are using it. What we are building. What it means for jobs.

Because... <shocker>.. They ALL use AI in college!! 

Of course, they do! They use it constantly! They use it to research, write, learn, and figure things out. They know when it is useful. They know when it sucks. If they do not get what they want, they change what they are asking and try again.

They are not  "adopting AI." They are simply using it (just like the rest of us).

That conversation happened at the same time I have been having some really interesting conversations with younger engineers, here at Datamatics, who are much closer to this stuff than I am. They are actually building with AI. They are trying to operationalize it; trying to figure out what works inside a real company and what does not.

And honestly, I love their perspective.

The more I listen to them, the more I think we may be having the wrong conversations, and it has become the impetus behind this article.

I do not think it is about AI transformation anymore. We have been transforming over the past few years. And I do not think it is really about adoption anymore, either. We are all adopting AI. Even the big legacy companies are using it now. Maybe some are doing it better than others, but it is happening! I can assure you!

So what's next?

Acceleration!

That is the conversation we need to be having today.

How fast can we try something? How fast can we figure out whether it works? How fast can we figure out that it does not work? And then how fast can we change it and try again?

I think that is where a lot of companies are going to struggle.

We do not like failing, but we must anticipate failure and learn from it. Here, failing will be inevitable. 

Especially big companies like plans and business cases. We like knowing what the ROI is going to be before we have  actually done anything. We can spend six months figuring out whether we should run a six-week pilot.

I am not sure we have that luxury anymore.

AI is moving too fast. More importantly, the people using it are learning too fast.

So try stuff.

If it does not work, then try something else. If that does not work either, then you probably learned something. Keep going.

Fail internally. Fail fast. Fail again.

Just do not fail your customer.

That is really important for me. Moving fast does not mean being reckless. Your customers should not have to suffer through your AI learning curve. Protect the customer experience. Protect your brand. Protect the trust you have built.

But internally? Go nuts.

Experiment. Break things. Challenge processes. Build agents that do not work. Try workflows that make absolutely no sense once you actually see them running. Then fix them or throw them away.

Eventually, you start figuring out what works.

That is acceleration.

One of the ideas that emerged from my conversations with these younger executives was to view the company as a Digital Factory. I love this!

Think about it. A traditional factory has machines. A modern services company has CRM, ERP,  HR systems, finance systems, knowledge bases, reporting platforms, and workflow tools. All of this technology is essentially the machinery running the company.

We have spent years putting people in front of those machines.

Now AI shows up.

So maybe the question is not where we can stick some AI into the existing process. Maybe we should be asking whether we should build the process this way at all if we were starting today. The original thinking that sparked this idea was exactly that: once AI becomes part of the workforce, the conversation moves beyond making an existing process a little more efficient. You start thinking about how the whole digital factory should operate differently.

That is a much more interesting conversation.

Why does this process take three days?

Why are seven people touching it?

Why are we creating a report manually when all the information already exists somewhere?

Why does somebody need to move information from one system to another?

And my favorite ...

Why do we do it this way?

Usually, the answer is some version of, "because that is how we have always done it".

Which brings me to something else I have been thinking about.

I spend a lot of time with executives. I get a seat at the table with some really smart people who have been running companies and industries for a very long time. People with incredible experience. People I learn from all the time.

But the more time I spend in those rooms, the more I think we need to reconsider who gets invited into them. As the great former governor of Texas, Ann Richards once said, “If you do not have a seat at the table, you are probably on the menu!”

To that spirit, let us get these younger leaders to the table faster!

I mean that.

Experience absolutely matters. I am not suggesting otherwise. But experience is also built around things we have already experienced.

We are in a new world.

None of us has 25 years of experience operating a company in an AI world. Nobody has done this before.

So maybe the person with the longest resume does not automatically have the best answer.

Maybe that 25-year-old who has been experimenting with AI every day sees something we do not. Maybe they ask why a process exists, and instead of spending 20 minutes explaining its history, we should actually think about the question.

Maybe it should not exist.

That is what I find so exciting about this next generation of leaders. They do not have as much to unlearn.

And I do not mean just college kids. I am already seeing it among young executives inside companies. They are curious. They are impatient. They want to build things. They do not seem particularly impressed by AI either. That is probably a good thing.

It's a tool to them.

Use it. Push it. See what happens.

There is another piece of this jigsaw puzzle that I think we are just beginning to understand. As AI starts doing more of the actual work, the person's role changes. People move from doing every step themselves to managing and improving the systems doing the work. That can actually make an individual more important because a single decision about how a system operates can suddenly affect thousands of transactions.

That is going to create a different kind of leader.

And I want those people in the room now.

Do not make them wait twenty years until they have earned enough tenure to have an opinion. Give them real problems. Put them in front of customers. Put them into strategy conversations. Let them challenge us. Let them build things. Let them fail.

Then let them try again.

I started thinking about all of this because of AI. But after this weekend, I realize that I am probably more excited about the people than the technology.

I dropped my son off at college, thinking about his future. I left thinking about ours. And honestly, I feel pretty damn good about it. These kids are smart. They are curious. They want to work. They are already using the technology. We are sitting in boardrooms trying to figure out how to adopt.

Maybe we should stop talking so much about adoption and start talking about acceleration. 

And most importantly, let us make an effort to get these kids to the table faster!