What Tennis Has Taught Me About Building Businesses in the AI Era

Tennis has shaped the way I think about competition, improvement, and what it takes to build something that lasts.

On a tennis court, your intentions don’t count toward the score. Neither does how impressive your practice session looked or how much you spent on your equipment. You have to read the situation, make a decision, execute, and deal with the result.

Building a business brings you back to those same demands.

Now that AI is part of the process, the pace feels different. We can move from an idea to a draft, a design, or an early prototype much faster. A small team can explore possibilities that once required considerably more time and resources.

But faster execution raises a harder question: Are we working on something worth executing?

That’s where I keep coming back to tennis. The tools change. The need for judgment, discipline, and feedback remains.

1. A Powerful Shot Needs a Purpose

A hard shot can feel great coming off the racket. Whether it was the right shot depends on where you were, where your opponent was, and what happened next.

Business has its own version of hitting hard without a plan.

You can generate dozens of articles, launch multiple websites, create elaborate automations, and fill a calendar with social posts. All of that produces activity. Its value depends on whether it helps someone and moves the business forward.

Before using AI to accelerate a project, I want clear answers to a few questions:

• Who is this for?
• What problem are they trying to solve?
• What am I offering that makes their next step easier?
• How will I know whether it worked?

For a tennis coaching service, the objective might be helping a player understand why their serve breaks down under pressure. For a local business website, it might be helping a visitor confidently book an appointment.

Those are concrete outcomes. They give the technology a purpose.

The clearer the target, the more useful the power.

2. Footwork Comes Before the Finish

People notice the winning shot. They’re less likely to notice the small adjustments that made it possible.

Good positioning gives you options. Poor positioning forces you to improvise under pressure.

The business equivalent is preparation: understanding your customer, defining your offer, organizing your information, and establishing a reliable way to deliver.

AI can help draft a sales page. But if the offer is vague, the page may simply express that vagueness more fluently.

It can help write customer emails. But someone still needs to decide what the business can promise, what the customer needs to know, and when a person should step in.

For my projects, that means doing the groundwork before adding complexity:

Define the customer → clarify the offer → establish delivery → improve the process.

Consider a tennis video analysis service. Before building a complicated platform, I need to establish what footage the player should submit, what feedback they will receive, how quickly it will arrive, and what they should do afterward.

Once those pieces are clear, automation can support a service that already makes sense.

3. Practice Must Transfer to Match Play

There’s a difference between hitting well in practice and competing well when the score matters.

The same gap exists between an impressive AI demonstration and a useful business.

A prototype can look excellent in a controlled setting. Then a customer asks an unexpected question, submits incomplete information, or uses the product differently from how you imagined.

That’s when you discover what you actually built.

I want to test ideas close to the conditions in which people will use them. That means putting an early version in front of a real customer and watching carefully.

Can they understand the offer without an explanation? Can they complete the next step? Does the result help them? What needs manual correction?

For a coaching offer, a small paid pilot can reveal more than weeks of debating features. For a website service, delivering one clearly defined project can expose the weaknesses in your onboarding and approval process.

Preparation matters. At some point, you have to play.

4. Feedback Works Best When It Is Specific

“Play better” is useless coaching.

“Recover your position after that wide forehand” gives a player something to work on.

Business feedback needs the same precision.

If a website isn’t producing inquiries, “the marketing isn’t working” is too broad to guide a decision. I need to understand where the problem sits.

Are the right people visiting? Do they recognize their problem in the headline? Is the offer understandable? Is there enough evidence to trust the business? Is the contact form difficult to use?

Each answer points toward a different adjustment.

AI can help organize feedback, compare drafts, and suggest explanations. I still need to check those explanations against what customers actually do.

My preferred approach is to choose one meaningful change, observe its effect, and learn from the result. Changing the headline, pricing, audience, and delivery model simultaneously makes it difficult to understand what helped.

A useful feedback loop creates a better next decision.

5. Consistency Makes Creativity Valuable

Tennis rewards the ability to repeat good decisions under changing conditions.

One spectacular winner doesn’t compensate for repeatedly giving away points. You need patterns you can trust.

In business, consistency shows up in ordinary places:

Answering an inquiry. Delivering on time. Sending a clear proposal. Following up. Keeping information accurate. Resolving a problem without making the customer chase you.

These actions rarely attract attention, but they shape how people experience the business.

This is where I see practical value in AI and automation. They can support repeatable work: preparing a first draft, organizing intake information, summarizing notes, or prompting the next step.

The important part is designing the process around a clear standard. What does good work look like? Who reviews it? What happens when the situation falls outside the normal pattern?

A system becomes valuable when it helps you deliver reliably.

6. You Cannot Play Every Ball as a Winner

Shot selection requires restraint. Sometimes you attack. Sometimes you create a better opportunity. Sometimes the right decision is to keep the ball deep and recover.

Entrepreneurship requires that same willingness to choose.

When you see possibilities across tennis, AI, media, and digital services, generating ideas is rarely the hardest part. Giving the right ideas sustained attention is much harder.

AI makes this more challenging because starting becomes easier. A new concept can quickly turn into a name, a logo, a website, and a content plan.

But every project still creates obligations. Someone must maintain it, sell it, serve its customers, and decide what happens next.

I want to evaluate opportunities through a practical lens:

• Is there a clear customer need?
• Can I reach the people who have it?
• Can I deliver a worthwhile result?
• Does it support the direction I want to build?
• What will I have to postpone to give it enough attention?

That last question matters. A promising idea can still be the wrong commitment today.

7. The Scoreboard Should Inform Your Decisions

A tennis score tells you where you stand. It doesn’t explain every reason you got there.

Business metrics work the same way.

Traffic, inquiries, sales, repeat purchases, and delivery time can all be useful. Each answers a different question.

For an early service business, I care about whether people understand the offer, whether qualified prospects buy, whether delivery is sustainable, and whether customers value the outcome.

A post receiving attention is encouraging. A relevant prospect taking the next step tells me something more specific about the business.

The goal is to choose measurements that match the stage of the project. During an initial test, direct customer feedback may be especially useful. As the service develops, conversion, retention, and profitability deserve more attention.

Measurement should help me decide what to improve next.

8. Composure Is Part of the Skill

After a bad point, you have limited time to reset. Carrying frustration into the next rally makes the original mistake more expensive.

Building a business creates similar moments.

An offer gets little response. A launch underperforms. A tool produces an unreliable result. A competitor seems to move faster.

The temptation is to react immediately: change everything, add features, lower the price, or start something new.

I try to approach those moments as a coach would. What happened? What evidence do I have? Was the problem execution, strategy, or simply an insufficient test?

That creates room for a measured response.

Resilience includes staying with a useful process long enough to learn from it—and recognizing when the evidence calls for a change.

9. Technology Makes Human Judgment More Important

A coaching relationship involves more than identifying a technical flaw.

A player may understand an instruction and still struggle to apply it. They may need a simpler explanation, a different drill, or time to build confidence.

Business relationships have that same human complexity.

A customer may ask for a new website when their deeper problem is an unclear offer. They may request more content when they need a stronger reason for someone to contact them.

AI can help explore possibilities and prepare useful work. Responsibility for the recommendation remains with the person making it.

My experience, attention, and willingness to understand the customer are part of what I bring to the work. Technology should help me apply those qualities more effectively.

How I Want to Build From Here

The approach I want to carry into my businesses is straightforward:

Choose a clear problem. Build a focused offer. Test it with real people. Listen closely. Improve delivery. Use AI where it contributes to a better result.

Then repeat the process with discipline.

Tennis has taught me to respect fundamentals, prepare carefully, and stay engaged when conditions change. It has also taught me that progress often comes from a sequence of small, well-chosen adjustments.

That feels especially relevant in the AI era.

There is more we can create, more we can test, and more we can automate. My job is to decide what deserves that effort—and to stay accountable for the result.

The question I want to keep asking is simple:

What is the most useful next move I can make for the person I’m serving?