Most small and mid-sized businesses don’t have an AI person.
There’s no Head of AI, no data science team, and nobody whose job is to figure out what all this means for the company. At this size, that role usually doesn’t exist. And honestly, trying to hire for it often goes badly. You’re paying a premium for someone who doesn’t know your business, your customers, your data, or where the actual friction lives.
But here’s the thing.
You probably don’t need to hire for it.
The people most likely to drive AI adoption inside your company already work there.
Every organization has a handful of people who are wired this way. They’re naturally curious. They don’t accept that a process has to stay slow just because it’s always been slow. When they run into repetitive work, their instinct isn’t to complain about it. It’s to figure out whether there’s a better way.
These people have always existed. Twenty years ago they were building spreadsheets. Ten years ago they were automating things with Zapier. Today they’re experimenting with AI.
What makes them valuable isn’t their technical background. Most of them aren’t developers. They’re in operations, finance, sales, customer service, project management, or somewhere else entirely.
What they have is curiosity.
Right now, most of these people are using AI the same way everyone else is. They’re drafting emails, summarizing documents, researching information, and cleaning up content. Useful tasks, but relatively small improvements.
The interesting shift happens when they stop asking how AI can help with a task and start asking whether the task should exist at all.
That’s when things start to get interesting.
Instead of asking how to speed up data entry, they start asking why someone is entering the data manually in the first place. Instead of asking how to summarize a report faster, they start asking whether the report could be generated automatically. The conversation moves from productivity to process design.
The problem is that most people get stuck at this stage.
Not because they lack motivation. Not because they lack intelligence. They get stuck because they don’t know what’s actually possible.
They have an idea, but they don’t know if it’s a one-hour project or a six-month project. They hit a technical roadblock and assume it’s impossible. They see an opportunity but can’t quite connect the dots between the systems involved.
Eventually the idea gets pushed aside and everyone goes back to doing things the way they’ve always been done.
This is where I think most companies miss the opportunity.
The goal isn’t to train every employee on AI. The goal is to identify the people who are already motivated and help them move faster.
Sometimes all they need is someone to think with.
Someone who understands both the business side and the technology side. Someone who can look at a process and quickly identify what’s realistic, what’s not, and where the biggest opportunities are. Someone who can help them avoid spending three weeks solving the wrong problem.
That kind of guidance is often more valuable than a generic AI course.
Training teaches people what the tools are. Guidance helps them understand what to build.
Once someone successfully solves a real business problem, something interesting happens. Other people notice.
A report that used to take four hours now takes five minutes. A manual workflow disappears. Information becomes easier to access. A frustrating process gets simplified.
Suddenly AI stops feeling like a trend and starts feeling useful.
Then other people start bringing forward their own ideas. They start asking different questions. Momentum builds naturally because people can see the value for themselves.
That’s usually how AI takes root inside an organization.
Not through a strategy deck.
Not through a company-wide initiative.
And usually not through a new hire.
It starts with a few curious people solving real problems that matter to the business.
The deeper work comes later. Once people start building, the conversation naturally shifts toward systems and data. Information that lives in spreadsheets, PDFs, emails, ERPs, CRMs, and line-of-business applications needs to become more accessible. Processes become easier to automate when the underlying data is organized and connected.
But that isn’t where most companies should start.
They should start with people.
The champion is probably already on your payroll. They’re already curious. They’re already experimenting. They’re already looking for better ways to work.
The challenge isn’t finding an AI expert.
The challenge is identifying the people who are already trying to improve the business and giving them the support to turn ideas into results.