The Liability of
Digital Fluency

You may be strategically ready for AI, but what about practically? Digital fluency is often the hidden barrier nobody thinks to check.

Last week I was on-site with a Visitor Attraction client. Every time I tell people we do AI work in hospitality and tourism, I get the same look. "AI feels like it should be more... 'techy'?"

That reaction tells me something important. Most people still think AI is a software developer problem. But developers are generally fine. They have tooling, budgets, and directives from leadership.

The teams that are struggling? They're still figuring out how to let AI use their computer. How to organize their files. How to not get distracted by new model drops.

Before we could even begin the actual AI work with this client, we had to go through and change some settings on their computer. I walked users through finding the app. We talked about where local files live and how to organize them in a way that makes sense.

This is often what AI adoption first looks like on the ground. Not choosing the best model or prompt engineering workshops. Just: where did that file go? Why can't AI see it? What next?

The assumption that breaks things

There's an assumption baked into most AI rollouts that people already know how to use their computer fluently. It's assumed because "everyone uses computers." But using a computer and understanding how it works are two different things.

Most people have figured out enough to get their job done. Email, web browser, a few key applications. That's not the same as knowing how your files are organized, why they're organized that way, or how to adjust app permissions when something isn't connecting properly.

AI exposes whether your team is digitally fluent or not. And in many of the teams we work with, at least 30-40% have trouble just using their computer.

When someone tries to get AI to reference the right files and it can't find them, that's not an AI problem. That's a digital fluency problem. When AI isn't "connecting right," that's often a permissions issue they've never had to think about before.

What happens when this goes unaddressed

I've seen this play out with clients who've already tried some kind of AI work before we arrived. Maybe a supervisor put them onto a platform or they experimented on their own.

Confidence starts to erode when they hit roadblocks in simply using the technology. They can't get AI to reference the right files. They struggle to connect it to their tools. They reuse the same prompt, telling AI to try again, without making meaningful progress.

Eventually they start thinking: "I wish I had just done this the way I used to do it."

That's the moment when someone privately decides AI is too complicated for them. Sometimes it stays private and just becomes part of how they work. Other times they say it out loud, and that rubs off on others.

Either way, the organization absorbs the cost of that decision. And often, leadership never knows it happened. It just becomes "we tried AI and it didn't really stick."

What actually matters

The barrier usually isn't AI itself. It's baseline digital skills that nobody thought to address because they seemed too basic to mention.

This isn't about becoming a developer or a computer whiz. The skills we're talking about are simple. But simple doesn't mean obvious, especially if nobody ever walked you through it.

Three things matter most:

Know how your files are organized and why. Have a system, even a simple one. Know where things live so you can point AI to the right place.

Be comfortable with app settings and permissions. When something isn't connecting, you need to be able to poke around in settings without freezing up.

Know how to screenshot and copy/paste. It sounds almost too basic to say, but these are the building blocks of working with AI.

If those pieces are solid, AI becomes dramatically easier to work with.