AI Is Changing Everything. But That's Not the Interesting Part.

Yesterday I went to an expo where, unsurprisingly, AI came up a lot. How it's changing industries, how quickly the technology is evolving, what jobs it might replace, what businesses should be doing with it, and what happens to the companies that don't move fast enough. I agree with most of it. AI is going to change how we work. It already has. But I left thinking about something a little different: the technology is moving incredibly fast, and most businesses aren't.

That isn't a criticism. Businesses are complicated. There are people, processes, old systems, new systems, spreadsheets & macros someone built seven years ago that apparently control half the company, and workflows that only make sense because "that's how we've always done it." Then AI gets dropped on top, and everyone wonders why transformation isn't happening overnight.

AI doesn't fix a bad process

This is probably the biggest thing I think companies are going to have to figure out. If a process is confusing today, adding AI to it doesn't automatically make it better. Sometimes it just makes the confusion happen faster.

Before asking, "Where can we use AI?" I think there are better questions. Where are we wasting people's time? What work are we doing over and over again? Where are decisions taking too long? Where does information get stuck? What are our teams manually assembling that a system should already know? What depends entirely on one person remembering to do something?

Once you understand that, the technology conversation becomes much more useful. Maybe the answer is AI. Maybe it's automation. Maybe it's a CRM workflow. Maybe it's better reporting. Maybe the process itself just needs to be simplified. The tool should come after the problem.

The best AI use cases are usually not very glamorous

A lot of AI demonstrations are designed to make you say, "Wow." And they do. But I'm much more interested in the things that make someone say, "Wait. I don't have to do that anymore?"

That's where the value starts getting real. Taking information scattered across multiple places and making it usable. Summarizing something that normally takes someone an hour to review. Preparing a first draft. Prioritizing a list. Finding an exception before it becomes a bigger problem. Turning unstructured information into something a team can actually act on. Removing three steps from a process nobody particularly enjoyed doing in the first place.

None of that sounds as exciting as "AI is changing the world." But multiply 20 minutes saved by a process that happens hundreds of times a month across an organization, and suddenly it gets pretty interesting.

Adoption is an operations problem too

Another thing I kept thinking about yesterday is how often we treat technology adoption like a technology issue. Someone selects the platform. The company announces it. Everyone gets training. There is a kickoff meeting. Then six months later, leadership discovers that half the team barely uses it.

I've seen versions of this with technology long before AI entered the conversation. The tool usually isn't the entire problem. People need to understand where it fits into their actual work. Not the perfect example someone built for a presentation.

If using the new tool takes more effort than the thing you're asking someone to stop doing, they're probably going to keep doing it the old way. That's not always resistance to change. Sometimes it's a completely rational response to friction.

Leaders have to use it too

I also think companies are going to struggle if AI becomes something leadership tells employees to adopt without changing how leadership works. If it's important, use it. Experiment with it. Talk about where it helped and where it didn't. Let people see you figuring it out too.

There is going to be a learning curve. Some prompts will be terrible. Some outputs will be wrong. Some ideas that sound brilliant in a meeting will turn out to save absolutely no time. That's part of learning where the technology actually belongs. The companies that get good at this aren't going to be the ones that picked the perfect AI strategy on day one. They're going to be the ones that built the ability to test, learn, adjust, and keep going.

This is the part of AI that interests me most

I'm fascinated by the technology, and I use it constantly. But I'm much more interested in what happens when you connect it to the way a business actually operates, because that's when AI stops being a cool tool and starts becoming an operating advantage.

The opportunity isn't simply to replace work. It's to redesign it. To give people better information. To remove things that shouldn't require human effort anymore. To help teams make decisions faster. To build systems where technology handles more of the repetitive work so people can spend more time on the parts that actually require judgment, relationships, creativity, and experience.

That's a very different conversation than asking which AI platform a company should buy.

Start smaller

If I were running an AI initiative inside a business today, I wouldn't start with a company-wide mandate to "use more AI." I'd start with one team, one workflow, and one annoying problem.

Find something people do repeatedly that consumes time and creates very little value. Understand exactly how it works today. Then ask: Can we make this better? Build it, test it with the people who actually do the work, see what breaks, change it, and measure whether it actually helped. Then find the next thing.

That's not as exciting as announcing an AI transformation, but it is usually how transformation actually happens: one process at a time, one team at a time, one useful improvement at a time.

AI may be changing the world incredibly quickly. The harder, and much more interesting, work is figuring out how to change the business with it.