What coaching five-year-olds
taught me about AI.

At this point in my life, pretty much everything revolves around two things: my family and my work. I’m not complaining. I love both. But it does mean that when I sit down to write something, most of my inspiration comes from one of those two places.

This week, it’s youth soccer.

I’ve been helping coach my son’s U6 soccer team, which has been an incredibly fun experience. It’s also been a surprisingly good reminder of something I think we’re getting wrong in the AI industry.

“Just because you understand something doesn’t mean you’re communicating it effectively. And just because you can teach someone something doesn’t mean you’ve made them want to learn it.”

The art of explaining soccer to five-year-olds

Every week, we put together drills for practice. Some are fantastic. The kids understand them immediately, everyone gets involved, and you can actually see them learning.

Others? Not so much.

I’ll gather the kids together, explain what we’re going to do, demonstrate the drill, and then send them off. Within about 15 seconds, it’s complete chaos.

Half the kids are doing something different, a few are looking at me like I’m speaking another language, and at least one has discovered something much more interesting happening on the other side of the field.

And here’s the interesting part: sometimes the drill works perfectly for a few kids and makes absolutely no sense to the others. The drill itself might be perfectly reasonable. I might even think I’ve explained it well. But none of that matters if the kids don’t understand what they’re supposed to do.

When that happens, I have two choices. I can keep repeating the same instructions and hope something changes, or I can recognize that I need to approach it differently. Simplify the drill. Demonstrate it another way. Break it into smaller pieces. Or sometimes just scrap the whole thing and try something else.

The goal isn’t to prove I know how to coach soccer. The goal is to help the kids learn how to play.

But actually, that’s not even my biggest goal.

I don’t really care if we win

Don’t get me wrong, I love seeing the kids score goals. And when they start passing to each other or doing something we’ve practiced, it’s incredibly rewarding.

But we’re talking about five-year-olds. My goal isn’t to develop the next professional soccer player or put together an undefeated season.

My goal is pretty simple. I want these kids to have fun. I want them to feel comfortable trying something new, making mistakes, and learning alongside their teammates.

Most importantly, I want them to finish the season thinking, I really like soccer. I want to do this again.

If a kid leaves practice frustrated, intimidated, or feeling like they’re not good at something, I haven’t accomplished much. Even if they technically learned the drill.

At this age, having a positive experience matters as much as the instruction.

And while the stakes are obviously very different, I think there’s an important lesson here for how we introduce AI into organizations.

Here’s the other thing: I’m not exactly a soccer expert

I played soccer in grade school. That’s about the extent of my playing career. No high school soccer. No college soccer. Certainly no professional aspirations that didn’t work out.

I’m learning a lot of this right alongside the kids. And strangely enough, I think that sometimes helps.

I’m not coming into practice with 20 years of technical knowledge or a complicated philosophy about how soccer should be played. I’m thinking about what these kids need to learn, what they’re capable of understanding, and how to make it fun enough that they want to keep trying.

Does that mean expertise doesn’t matter? Of course not. An experienced coach could teach me an enormous amount. But knowing more about soccer wouldn’t automatically make me better at communicating with a group of five-year-olds.

And that’s where I think the connection to AI gets interesting.

We’ve made AI unnecessarily complicated

Spend 30 minutes in a conversation about enterprise AI and you’ll probably hear some combination of:

To be clear, these concepts matter. They’re real technologies and approaches that solve real problems. I spend a significant amount of my working life trying to understand them myself.

But I’ve started wondering how often we’re explaining the technology because it helps the person across the table, and how often we’re explaining it because we want them to know how much we understand.

Imagine someone tells you they’re spending three hours every Monday pulling information from different systems to prepare a report. They don’t necessarily need a lesson on agent orchestration or retrieval-augmented generation. They need to understand whether there’s a better way to get their Monday mornings back.

We can explain the technology when it becomes relevant. But we shouldn’t make understanding our vocabulary a prerequisite for solving their problem.

In fact, I’d argue the opposite. The more sophisticated the technology becomes, the simpler the experience should feel to the person using it.

That’s not about building less sophisticated solutions. It’s about doing the difficult work behind the scenes so the customer doesn’t have to.

Making AI approachable matters

There’s an enormous amount of excitement around AI, and understandably so. The possibilities are incredible, and we’re only beginning to understand what this technology can do.

But there’s also uncertainty.

For many people, AI represents a significant change in how they work, what skills they’ll need, and potentially even what their roles will look like in the future. Those are legitimate concerns, and we shouldn’t minimize them.

What we can control, however, is how we introduce the technology and how we bring people into the process.

If someone is already uncertain about AI, overwhelming them with technical terminology, unfamiliar tools, and expectations they don’t fully understand isn’t going to help. It can make an already intimidating transition feel even more inaccessible.

I think we sometimes underestimate how important the first experience is.

• Does someone feel comfortable asking a question?
• Do they understand why a new tool is being introduced?
• Can they see how it might help them in their day-to-day work?
• Do they feel like they have a voice in how the technology is being implemented?

Or does it feel like something happening around them, with little opportunity to participate?

“AI transformation shouldn’t be something that happens to employees. It should be something they’re invited to help shape.”

That doesn’t eliminate every concern, but it creates a much better foundation for understanding, trust, and adoption.

Training can’t be an afterthought

This is where I think organizations have an enormous opportunity to improve. AI is moving incredibly quickly. Companies are understandably eager to implement new tools, automate processes, and start seeing results.

But in that rush, training and employee engagement can become secondary priorities. Something we’ll get to once the technology is deployed.

I think that’s a mistake.

Training shouldn’t be a single presentation or a one-hour introduction to a new tool. And it certainly shouldn’t be treated as a nice-to-have if there’s time left in the project. It needs to be part of the transformation itself. That means:

• Regular opportunities for people to learn, ask questions, and share what they’re experiencing.
• Checkpoints throughout implementation, not just at the beginning and end.
• A safe environment for ideation, which may matter most of all.

Some of the best ideas about how AI can improve a business are going to come from the people doing the work every day. They’re the ones who understand the exceptions, the workarounds, the repetitive tasks, and the little frustrations that don’t always show up in a process diagram.

But those ideas don’t surface if people feel uncomfortable asking questions, challenging assumptions, or admitting they don’t understand something. Organizations need to make room for those conversations. And they need to keep making room for them as the technology evolves.

The goal isn’t just to train people on a solution. It’s to give them a meaningful role in shaping how that solution works.

Not everyone is starting at the same place

This is another thing coaching has reinforced for me. Even with kids who are roughly the same age, the same explanation doesn’t work for everyone. Some understand immediately. Others need to see it. Others need to try it a few times. And some aren’t quite ready for that particular drill yet.

Businesses are no different. One person might be experimenting with AI every day. Another might have never opened ChatGPT. A third might understand the technology but have no idea how it fits into their organization’s operations. And they might all be sitting in the same meeting.

If we design our conversations around the person with the most technical knowledge, we risk leaving everyone else behind. If we make every conversation overly basic, we risk frustrating the people who are ready to go deeper. The answer isn’t to make everything elementary or strip away the important details.

It’s to make information accessible at the right level, for the right audience, at the right time.

It’s to meet people where they are and give them enough understanding and confidence to take the next step. That’s a much harder skill than memorizing the latest terminology.

Expertise should make things more accessible, not less

At Waifinder, we talk a lot about making AI actually work inside businesses. A big part of that is technical. The systems have to function, the data has to make sense, and the solutions have to deliver something valuable.

But there’s another part that’s just as important. The people using those systems have to understand what they’re doing, why they’re doing it, and how it affects their work. Otherwise, we haven’t really solved the problem. We’ve just introduced another layer of complexity that people are expected to navigate.

That’s one of the reasons we emphasize collaboration and transparency throughout our delivery process. We believe there should be regular touchpoints between the people building the technology, the people making decisions, and the people who will ultimately use it.

Not just a discovery meeting at the beginning and a demonstration at the end. Ongoing conversations. Opportunities to validate assumptions. Checkpoints to make sure everyone is still aligned. And room to change direction when we learn something new.

We want customers to understand what’s being built, participate in the decisions, and feel comfortable contributing ideas throughout the process. Because the reality is that the people building an AI solution don’t automatically understand every nuance of the business they’re building it for. That knowledge has to come from collaboration. And the learning has to go both ways.

“The sophistication belongs in the solution. It doesn’t need to be imposed on the person using it.”

If we’ve done our jobs well, the technology can be incredibly complex while the experience feels surprisingly simple.

And ideally, people leave the experience thinking, That was actually pretty useful. I’d like to do more of that.

That’s how you build adoption that lasts.

Back to the soccer field

The funny thing is, when a drill doesn’t work, the kids don’t care how much preparation went into it. They don’t care whether I borrowed it from an experienced coach, watched a video about it, or came up with it myself. They just want to play soccer.

And when I finally find a way to explain something that clicks, it’s pretty rewarding. You can actually see the progress. But the best part isn’t necessarily when they execute a drill correctly.

It’s when they’re laughing, working together, trying something they couldn’t do the week before, and asking when the next practice is. That’s what I want them to take away from the season.

Of course, introducing AI into a workplace comes with responsibilities and concerns that go far beyond anything happening on a youth soccer field. But I do think there’s a shared lesson about the importance of how we teach, communicate, and involve people in something new.

We can spend all day talking about how powerful this technology is. We can explain every technical detail, build increasingly sophisticated systems, and demonstrate capabilities that would have seemed impossible a few years ago. But if the people we’re trying to help walk away confused, intimidated, or disconnected from the process, what have we really accomplished?

I may not be the most qualified soccer coach on the field, but I’m learning alongside the kids, adjusting when something doesn’t work, and hopefully helping them enjoy the game enough to keep playing.

Maybe that’s something the AI industry could learn from a bunch of five-year-olds.