If you've spent any time online lately, you've probably seen someone showing off something they built with AI.

It could be someone built a membership site over a weekend using AI.

Someone else has launched a little SaaS product with AI built into it.

Or perhaps you've seen an AI voice assistant answering calls and nurturing leads, an AI-powered CRM app, a marketing dashboard, or an AI chatbot sitting inside someone's funnel.

You might also be seeing people build smaller AI tools for things like lead generation, assessments, content creation, client onboarding, or other parts of their business.

And the interesting part is that some of these aren't huge software companies with teams of developers behind them.

They're business owners, coaches, consultants and creators showing what they've managed to build themselves.

So if you're running an online business, it's pretty easy to start wondering:

"Should I be doing this too?"

Maybe you're just starting out and thinking about what technology to build around your business.

Or you already have clients, but you're juggling a website, forms, email, CRM, calendar and other tools that don't quite talk to each other.

Or perhaps your business is already established and you're wondering whether building your own AI app or tool could give you something that existing software can't.

And when AI makes building software look easier than ever, the temptation is understandable.

BUT there's an important question we should ask before jumping in:

Just because you can build it, does that mean you should?

Quick Answer: You don't need to build your own AI app just because you can. Build when you have a valuable problem that existing tools can't solve well enough. Buy when an existing tool already does the job. And consider connecting and customising existing tools when you only need something in the middle.

The question isn't whether you can build it anymore

Not that long ago, building software usually meant hiring developers, learning to code, or spending a significant amount of money getting someone else to build it for you.

That's changing.

AI coding assistants and no-code and low-code tools have made it much easier for people without a traditional software development background to create working applications.

You can describe what you want.

AI can help generate the code.

You can connect APIs and existing services to make apps talk together.

You can build an interface.

You can test it.

And sometimes, you can get something surprisingly useful working in a very short amount of time.

That's genuinely exciting.

But there's a subtle problem with this new capability.

The easier something is to build, the easier it is to start building things you don't actually need.

It's a little like having a power tool for the first time.

Just because you can build a shelf doesn't mean you need another shelf.

The same applies to AI apps.

The interesting question isn't:

"Can I build this?"

It's:

"What problem would building this solve that my current tools can't solve well enough?"

That's a much better place to start.

Start with the problem, not the AI

Let's say you're a coach.

You want to create an AI assessment that prospects can complete on your website.

They answer a series of questions.

The AI analyses their responses.

It produces a personalised result.

Then the prospect receives recommendations and is invited to book a call.

That's potentially useful.

But before building an AI application, you could ask:

Do I actually need to build the whole thing myself?

There may already be tools that can handle the form, scoring, AI processing, email delivery and booking.

• Maybe you can connect a few existing tools together.

• Maybe you only need a small custom component.

• Or maybe there really is something unique about your assessment that existing tools can't handle.

Those are three very different situations.

And that's why I wouldn't start with:

"How can I build an AI app?"

I'd start with:

"What am I trying to accomplish?"

Then work backwards.

When an existing tool is probably the better choice

There is nothing wrong with using software that somebody else has already built.

In fact, that's usually the point of software.

• You don't build your own email platform every time you want to send an email.

• You don't build your own payment processor when you want to accept a payment.

• You don't build your own video conferencing software because you need to run a coaching call.

You use something that already does the job.

The same thinking applies to AI.

If an existing tool already solves the problem reasonably well, using it can save you from taking on a lot of unnecessary work.

For example, if you're building an online coaching business, you might need:

• a website or landing pages

• forms

• a CRM

• email marketing

• appointment booking

• workflows and automations

• conversations with leads

• client follow-up

You could theoretically build your own software to handle all of this.

But you probably don't need to.

Platforms such as GoHighLevel already bring many of these functions together, so you can spend your time configuring the system around your business rather than building the underlying software yourself.

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And that's an important distinction.

You're not necessarily paying for the most technically sophisticated solution.

You're paying to avoid having to build and maintain something that has already been solved.

An established product may have limitations, of course. It may not work exactly the way you want. You may have to change your process to fit the software.

But sometimes that's a perfectly reasonable trade-off.

You don't always need the perfect system.

You need a system that's good enough to do the job without creating another job for you.

The same applies to AI.

Suppose you want an AI chatbot on your website.

You could potentially build one yourself.

But then you're not just building a chatbot.

You're also potentially dealing with:

• connecting an AI model

• managing API access

• storing and retrieving information

• handling user conversations

• managing authentication

• monitoring usage

• handling errors

• keeping integrations working

• updating the system when connected services change

• figuring out what happens when something goes wrong

An established product may already handle most of that.

You might pay a monthly fee for it.

But that fee is buying more than software.

You're also paying someone else to take responsibility for part of the infrastructure.

That's an important distinction.

When building your own AI app might make sense

This doesn't mean you should never build your own.

There are situations where building makes perfect sense.

For example, you might have a process that's very specific to your business.

Perhaps you've developed a unique assessment methodology that doesn't fit neatly into existing software.

Or perhaps your internal workflow is unusual enough that you're constantly forcing generic tools to do things they weren't designed to do.

Or maybe you're building something that is actually becoming part of your product.

In those cases, owning the technology may create a meaningful advantage.

You might want to build when:

1. Existing tools can't do what you need

You have a genuine gap that isn't easily solved by configuration or integration.

2. The problem happens frequently

If a custom solution saves you or your team significant time every week, the investment may become worthwhile.

3. The workflow is important to your business

If the system sits at the centre of something valuable you do, having more control over it may matter.

4. You need something highly specific

Generic software has to serve many customers.

Your own software can be designed around your exact process.

5. You're building a product, not just an internal tool

If customers will actually pay to use the application, that's a very different situation from building something simply because it's interesting.

But notice something.

NONE of these reasons are:

"Because AI is hot right now."

There's a third option people often overlook

When people talk about technology, the conversation often becomes:

Build vs Buy.

But there's another option.

Connect and customise.

You don't necessarily need to build an entire application from scratch.

You can use existing technology for the parts that are already solved and build only the part that's unique to you.

For example:

• Use an existing CRM.

• Use an existing payment system.

• Use an existing calendar.

• Use an existing AI model.

• Use existing email infrastructure.

• Then build a small custom layer that connects everything together around your particular workflow.

This can be a much more practical approach.

You're not reinventing things that don't need reinventing.

You're only building the piece that gives you something different.

And this is something I think business owners should consider more often.

You don't have to own the whole technology stack to own the part that makes your business different.

Building it is only the beginning

This is probably the part that gets overlooked when someone shows you an impressive AI app they built over a weekend.

You see the finished product.

You don't necessarily see everything that comes after it.

Because building software and running software are two different things.

Imagine you've built an AI tool that connects to your CRM.

It works perfectly.

Great.

But now ask:

• What happens when the CRM changes its API?

• What happens when the AI provider changes its pricing?

• What happens if your API key is exposed?

• What happens when the AI produces an unexpected response?

• What happens when a third-party service goes down?

• What happens when you need to change something six months from now and you've forgotten how the application works?

• What happens when customers start using it in ways you didn't anticipate?

These aren't reasons not to build.

They're simply part of the decision.

AI has made it much easier to create software.

It hasn't removed the responsibility of running what you build.

The hidden cost isn't always money

When deciding whether to build something, it's easy to think about development cost.

But there are other costs.

There's maintenance.

There's security.

There's reliability.

There's support.

There's documentation.

There's updates.

There's technical debt.

There's also something less obvious:

Your attention.

If you build a tool yourself, you're creating another thing in your business that you need to think about.

And if you're a small business owner, that matters.

You probably already have a website.

An email system.

A CRM.

A calendar.

Payments.

Your actual product or service.

Customers.

Content.

Marketing.

And everything else that comes with running a business.

Adding another system isn't automatically progress.

Sometimes it's just another thing to maintain.

Easy to build doesn't mean easy to own

This is probably the biggest distinction I'd keep in mind.

AI can make the creation of software much easier.

But the ownership of software hasn't become equally easy.

You can build something in a weekend.

That doesn't necessarily mean you should be responsible for it for the next five years.

And that's where I think a lot of the excitement around AI can become misleading.

Someone can show you:

"Look what I built in two days!"

That's interesting.

But the more useful question might be:

"What will it take to keep this working for the next two years?"

That's a very different question.

A simple way to decide

Before building your own AI app or tool, I'd run through these questions.

1. What problem am I actually solving?

Try to describe it without mentioning AI.

For example:

Bad starting point:

"I want to build an AI lead qualification app."

Better:

"I want to automatically identify which leads are a good fit before I spend time speaking to them."

Now you can explore different solutions.

AI might be one.

But it isn't automatically the answer.

2. Can an existing tool already solve this?

Search properly.

Look at established software.

Look at integrations.

Look at APIs.

Look at what's already available.

You may discover that the thing you were about to spend weeks building already exists.

3. Can I connect existing tools instead?

Before building from scratch, ask whether you can connect what you already have.

Sometimes the missing piece isn't a new application.

It's simply a better workflow.

4. Is the problem valuable enough to justify owning the technology?

If the tool saves you 15 minutes a month, building it yourself probably isn't the same decision as building something that saves your team 20 hours every week.

The value of the problem matters.

5. Am I prepared to maintain it?

Not just build it.

Maintain it.

Secure it.

Fix it.

Update it.

Document it.

And eventually, perhaps replace it.

6. Is this becoming part of my actual product?

If customers will pay for it, or if it creates a meaningful part of your competitive advantage, building may make more sense.

If you're just building it because it looks cool, that's worth questioning.

So, should you build your own AI app?

I'd turn the question around.

Don't build an AI app because AI is trending.

Build one when you've found a real problem that is worth solving and existing solutions aren't good enough for what you need.

And if an existing tool solves the problem well?

Use it.

If several tools solve different parts of the problem?

Connect them.

If there's one important piece that's unique to your business?

Build that piece.

You don't need to choose between becoming a software company and never building anything yourself.

There's a lot of useful territory in between.

And there's nothing wrong with "boring" software

I think this is worth saying because the internet tends to reward novelty.

A custom AI app sounds exciting.

A well-configured CRM doesn't.

An AI agent sounds exciting.

A reliable automation that sends the right email at the right time doesn't.

A dashboard you built yourself sounds exciting.

A spreadsheet that already does the job doesn't.

But your business doesn't get extra points for having the most interesting technology stack.

It gets value from technology that actually helps you run the business.

Sometimes the smartest technology decision is the least exciting one.

Use the thing that already works.

My rule of thumb

Here's the simplest way I think about it:

Build when the problem is unique and valuable enough to justify owning the solution.

Buy when someone else already solves the problem well.

Connect and customise when you only need something in the middle.

And before doing any of those things, ask one more question:

What am I going to be responsible for once this exists?

Because you're not only deciding what to build.

You're deciding what you want to own.

Final thought

AI has changed something important.

It has lowered the barrier to building software.

That's a good thing.

But easier building doesn't automatically mean more building is better.

If you're starting an online business, you probably don't need to build everything yourself.

If you already have clients and your tools aren't talking to each other, you may not need a new AI app. You may simply need to connect the systems you already have.

And if you're an established business with a genuine problem that existing software can't solve, then building something custom might be exactly the right move.

The point isn't to avoid AI.

It's to avoid adding technology simply because you can.

Because the best system isn't necessarily the one with the most AI.

It's the one that works hard for you — quietly in the background.

That's the kind of technology worth building.