You’ve probably seen them all over your social media feeds.

AI experts. AI consultants. AI strategists. AI thought leaders.

LinkedIn, in particular, has become a hotspot for AI content — and, yes, its fair share of AI slop which the platform is trying to solve. Every day, there’s another AI hack, a new list of prompts you need to know, or another post explaining how to use AI to work faster, smarter, and better.

That doesn’t mean everyone posting about AI is contributing to the slop. Far from it. There are plenty of people sharing genuinely useful ideas, experiments, and lessons about a technology that’s evolving incredibly quickly.

But there’s also a difference between talking about AI and having expertise in AI.

And that distinction matters.

AI expert vs. AI expertise

It’s easy to call yourself an AI expert, it becomes much harder when that expertise is tested.

Knowing how to write a clever prompt, keeping up with the latest models, or experimenting with a handful of AI tools can make someone a knowledgeable AI user. But expertise goes further than knowing how to use the technology.

It means understanding what happens when you try to apply it to a real problem.

What happens when the output isn't reliable? When the model confidently gives you the wrong answer? When a promising prototype has to become something people can actually use and depend on?

That's where the difference between familiarity and expertise starts to show.

And we’ve had to learn that ourselves.

We didn’t start by calling ourselves AI experts

At Fluff Software, our experience with AI has been a learning process too.

We experimented, we built, we tested what worked and, just as importantly, discovered what didn’t.

And that experience is part of why we became comfortable offering AI automation and integration services.

Not because we suddenly decided to put “AI experts” on a website, but because we had gone through the process of developing the experience behind that claim.

So, what does AI expertise actually mean?

In that sense, AI isn’t particularly different from any other field.

Think about software engineering, design, architecture, finance, or practically any profession. Expertise develops through learning, application, failure, iteration, and experience.

You learn how something works. Then you apply it. Your assumptions get tested. Things break. You figure out why. You improve them. And eventually, you develop something that’s much harder to pick up from a tutorial: judgment.

For us, that judgment is becoming an increasingly important part of building with AI.

Knowing how to integrate AI is one thing.

Knowing when it makes sense, what it should actually improve, where its limitations are, and when a simpler solution would be better is another.

We’re still learning, too. AI is moving far too quickly for anyone working seriously in this space to claim they’ve learned everything there is to know.

Perhaps being an expert doesn't mean you've finished learning.

Maybe it means you’ve built enough, tested enough, failed enough, and learned enough to know how much the details matter.

Expertise is earned

None of this is an argument against using AI.

Experiment with it. Ask questions. Build things. Break things. Try the latest tools. Share what you discover.

Everybody has the potential to develop expertise in AI.

But access to AI doesn’t automatically make someone an AI expert, just as having access to any other tool doesn’t automatically make you an expert at using it.

There’s a learning process and experience involved.

And if you’re going to call yourself an expert — or ask someone to trust you with their business because of that expertise, there should be something behind the word.

For us at Fluff Software, it’s a title we’d rather earn through what we build than simply claim.