I wrote this after a pretty interesting weekend.
I just dropped my son Sam off at college. Met a bunch of his buddies. Hung out with them. Talked about what they want to do with their lives. Internships. Jobs. What sounds interesting. What doesn't. All the 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 don't 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're using it in the enterprise. How companies are using it. What we're 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. To write. To learn. To figure things out. They know when it's useful and they know when it sucks. If they don't get what they want they change what they're asking and try again.
They aren't "adopting AI." They're simply using it (just like the rest of us).
That conversation happened at the same time I've been having some really interesting conversations with younger engineers here at Datamatics who are much closer to this stuff than I am. They're actually building with AI. Trying to operationalize it. Trying to figure out what works inside a real company and what doesn't.
And honestly I love their perspective.
The more I listen to them the more I think we’re may be having the wrong conversations, and it’s become the impetus behind this article.
I don’t think it’s about AI transformation anymore. We've been transforming over these past few years. And I don't think it's really about adoption anymore either. We're all adopting AI. Even the big legacy companies are using it now. Maybe some are doing it better than others, but it's happening! I can assure you!
So what's next?
Acceleration.
That's 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 doesn't work? And then how fast can we change it and try again?
I think that's where a lot of companies are going to struggle.
We don't like failing but we must anticipate failure and learn from it. Failing will be inevitable.
Especially big companies. We like plans. We like business cases. We like knowing what the ROI is going to be before we've actually done anything. We can spend six months figuring out whether we should run a six-week pilot.
I'm 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 doesn't work then try something else. If that doesn't work either then you probably learned something. Keep going.
Fail internally. Fail fast. Fail again.
Just don't fail your customer.
That's a really important part of this for me. Moving fast doesn't mean being reckless. Your customers shouldn't have to suffer through your AI learning curve. Protect the customer experience. Protect your brand. Protect the trust you've built.
But internally? Go nuts.
Experiment. Break things. Challenge processes. Build agents that don't 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's acceleration.
One of the ideas that came out of my conversations with these younger executives was this idea of looking at 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. Workflow tools. All of this technology is basically the machinery running the company.
We've spent years putting people in front of those machines.
Now AI shows up.
So maybe the question isn't where we can stick some AI into the existing process. Maybe we should be asking whether we'd 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's 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 into another?
And my favorite...
Why do we do it this way?
Usually, the answer is some version of, "because that's how we've always done it".
Which brings me to something else I've 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 don’t have a seat at the table you’re probably on the menu!”
To that spirit, let’s get these younger leaders to the table faster!
I mean that.
Experience absolutely matters. I'm not suggesting otherwise. But experience is also built around things we've already experienced.
We’re 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 doesn't automatically have the best answer.
Maybe that 25-year-old who has been experimenting with AI every day sees something we don't. Maybe they ask why a process exists and instead of spending twenty minutes explaining the history of the process we should actually think about the question.
Maybe it shouldn't exist.
That's what I find so exciting about this next generation of leaders. They don't have as much to unlearn.
And I don't mean just college kids. I'm seeing it with young executives already inside companies. They're curious. They're impatient. They want to build things. They don't seem particularly impressed by AI either. That's probably a good thing.
It's a tool to them.
Use it. Push it. See what happens.
There's another piece of this that I think we're just beginning to understand. As AI starts doing more of the actual work the role of the person 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 one decision about how a system operates can suddenly impact thousands of transactions.
That's going to create a different kind of leader.
And I want those people in the room now.
Don't make them wait twenty years until they've 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'm realizing I'm 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're curious. They want to work. They're already using the technology we're 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’s make an effort to get these kids to the table faster!