The Three Questions That Change Everything About Your AI Approach

There's a moment that happens in almost every AI conversation I have.

We start the meeting, and the first question that comes is: "So, how can we automate this?" And I know we're starting in the wrong place.

We live in a world obsessed with quick solutions. Show me the tool. Give me the template. Tell me which AI to buy.

It's not laziness, at this point it’s exhaustion. People are drowning in options, overwhelmed by possibilities, and desperate for someone to just tell them what to do.

And most of us don’t live to follow AI. Artificial intelligence is just another complication in our already complex daily lives.

So, they buy subscriptions. Try ChatGPT for a week, or they sign up for the latest AI platform everyone's talking about. Hope that something clicks for them. That their life will be better.

It rarely does.

Not because the technology couldn’t work. But because they're trying to solve a problem they haven't defined, with a solution they don't understand.

After years of building AI solutions with purpose-driven entrepreneurs and organizations, I've learned that success comes down to three questions (asked in order, and with intention):

Sounds simple, right?

But here's what makes it powerful: most people skip the first two questions entirely and jump straight to HOW.

So let’s start from the beginning.

"Why are we doing this?" feels too basic. Too obvious. Everyone knows why, right?

Except most people don't.

I've sat in rooms where clients want AI "because everyone else is doing it." Where teams are implementing chatbots not because customers are confused, but because chatbots are trendy. Where organizations chase automation without ever asking what value they're trying to create.

The WHY question forces you to get uncomfortable. It asks you to articulate the actual problem. Not the symptom, not the buzzword, but the real friction point that's costing you time, money, or impact.

A good WHY is specific. It's measurable. It names who it affects.

"We need AI" is not a WHY.

"Our team spends five hours every week searching through old documents during client calls, and it's making us look unprepared" is a WHY.

The difference? One leads to random tool adoption. The other leads to a solution people will actually use.

Once you know WHY, the WHAT question reveals something most people miss entirely: the solution isn't about the technology at all.

It's about what you know.

Generic AI gives generic answers. Anyone can type a question into ChatGPT. But what makes AI transformative for your product and organization is when it reflects your way of solving problems. Your methodology. Your years of experience in what you do. Your intuitive judgment calls.

This is where most people realize they can't articulate what makes their service valuable. The expertise lives in implicit knowledge. It lives in things they "just know" from experience. Patterns they can easily recognize without thinking. Judgment calls that feel obvious but aren't.

This is why co-creation matters. You can't outsource this step to a consultant who doesn't understand your work. And it’s hard to figure it out alone without guidance on how to extract what's in your head.

Most of the time we take our greatest value for granted.

The WHAT phase is where your competitive advantage gets embedded into the solution.

Here's the truth nobody tells you about AI implementation: the technology is the easy part. The hard part is adoption.

I've seen beautifully designed AI solutions that sit unused because nobody understands them. Expensive platforms that create dependency instead of capability. Tools built by consultants who leave your team with no idea how to maintain what they've paid for.

AI is not a static solution that you build once, and leave be. It’s a living being that requires constant attention.

The HOW question isn't just about building, it's about building in a way that ensures people use it and that over time it becomes better, not worse.

This is why the traditional consulting model fails. When someone builds FOR you, you don't understand it. When you don't understand it, you can't fix it when it breaks. You can't improve it when needs change. You can't explain it to your team.

The co-creation approach flips this: you build WITH guidance. You understand what you've created because you created it. When your team participates in building, they're invested in using it.

HOW becomes less about technical execution and more about change management through inclusion.

If you jump to WHAT without WHY, you'll build something elegant that solves the wrong problem.

If you jump to HOW without WHAT, you'll chase tools without knowing what inputs your solution needs.

And if you skip all three and just buy a subscription? You'll join the majority who try AI, get frustrated, and abandon it.

Let me show you how this plays out.

Old approach: "Everyone's using AI for customer service, so let's get a chatbot."

The purpose-driven approach:

WHY: Our customers get stuck on three specific issues after purchase, and our support team is overwhelmed answering the same questions repeatedly. My company relies on me being there, if I leave, it’ll crash.

WHAT: We need our AI to understand our product deeply. Not just FAQs, but the nuanced troubleshooting process our best support person uses. Input: product docs, past support tickets and examples, and our internal troubleshooting guide. Process: match the question to our methodology, and train on the edge cases for optimal output. Output: step-by-step guidance in our approach and voice.

HOW: We'll build this together with our support lead so she can maintain and improve it. We'll test with real customers. We'll create a launch plan that includes training our team on when to use it versus when human support is better.

See the difference?

One is technology for technology's sake. The other is purpose-driven implementation.

The framework isn't complicated. Three questions. Five steps.

But the work is in being honest enough to ask WHY before you know the answer. Patient enough to articulate WHAT when your expertise feels too obvious to explain. Committed enough to participate in HOW instead of delegating it entirely.

This is what separates AI that works from AI that gets abandoned.

Not the sophistication of the model. Not the size of the budget. Not the hype around the platform.

The questions you ask before you start building.

Want to work through these questions for your specific situation?

That's exactly what the Co-Build Sprint does. We start with WHY and end up with a working solution you understand and own.