What I Got Wrong About AI Before I Got It Right

There is a version of me from two years ago that I cringe thinking about.

I had just started going deep on AI tools. I had read every thread, watched every demo, and convinced myself I understood the play. The play was simple: find the bottleneck, find the tool, automate it out of existence.

I was wrong about almost everything. And it took me longer than I would like to admit to figure out where I had gone off course.

Here is what I got wrong, and what I learned when I finally got it right.

Mistake 1: I Thought the Goal Was Removal

My first instinct with every AI tool was the same question: what can this eliminate? Who can this replace? Where can I cut the headcount and keep the output?

That frame made sense on paper. Labor is expensive. Humans make mistakes. Humans get sick, get distracted, get emotional. If a machine could do the job, why not use the machine?

So I built automations. A lot of them. I wired up workflows that bypassed people entirely. I got proud of the leaner org chart.

And then the cracks showed up. Not in the automations. In the outcomes.

The emails went out but they felt like they were written by nobody. The responses came back but they landed wrong. The process was technically correct but it felt hollow. And the people on the receiving end could feel it, even if they could not name it.

The problem was not the tools. The problem was my premise. I had been trying to optimize out the human, and the human was the point.

Mistake 2: I Confused Speed With Quality

AI gave me speed I had never had before. I could produce in an hour what used to take a week. I took that as validation. More output, less time, lower cost. The spreadsheet looked great.

What I did not account for: speed amplifies whatever you put in. If your strategy is sharp, speed makes you sharp faster. If your strategy is off, speed makes you wrong at scale.

I had entire campaigns running on flawed positioning before I caught the problem. Because I had moved so fast, the damage was already wider than it needed to be.

The lesson: AI does not make you smarter. It makes you faster. Those are not the same thing. You still have to think. You still have to question the frame. You still have to do the work of knowing what you are actually trying to accomplish before you let the machine run.

Mistake 3: I Underestimated What Humans Bring to High-Stakes Moments

This is the one that changed everything for me. And it came from watching intake at law firms.

I was deep in building eNZeTi and spending a lot of time talking to attorneys about their intake process. What I kept hearing was the same thing in different words: the calls are getting dropped. Prospects hesitate, and the coordinator does not know what to say. The case walks.

And the solutions the market kept offering were all the same: replace the coordinator. AI receptionist. Virtual answering service. Outsourced intake team.

I had been guilty of that same thinking in my own business. Just remove the human who is struggling. Put a system in their place.

But when I actually listened to what was happening on those calls, I understood why that answer was wrong. The people calling a law firm after an accident or an arrest or a family emergency are not filling out a form. They are scared. They are confused. They need a human voice that knows them, hears them, and responds with something real.

No automation handles that. No script covers every variable a real person brings into that conversation.

What the coordinator needed was not to be replaced. They needed support. The right words on their screen the moment a prospect hesitated. A system that amplified their judgment instead of bypassing it entirely.

That is what eNZeTi became. Not an AI receptionist. Not an automation. A real-time coaching layer that makes the human on the phone better at the exact moment it matters most.

The shift in my thinking was fundamental: the goal was never removal. The goal was amplification.

Mistake 4: I Did Not Trust My People With the Tools

For a stretch of time, I treated AI as my competitive edge. Something I used that they did not. I was protective of the workflows, slow to share, hesitant to train anyone else on what I was building.

That was a mistake rooted in the wrong kind of ego. The goal is not to be the only person in your organization who can use AI well. The goal is to build a team where everyone operates at a higher level because the tools exist.

The moment I started treating AI as infrastructure instead of secret sauce, the whole operation changed. People who had been doing okay started doing great. The throughput went up. The quality went up. Because the tools were in everyone’s hands, not just mine.

The best version of an AI-powered business is not one person with a machine. It is a whole team of humans, each one amplified by the right tool at the right moment.

Mistake 5: I Thought the Implementation Was the Hard Part

It is not. The hard part is the clarity you need before implementation. What problem are you actually solving? What does success look like? Where does the human judgment live in this process, and where does the machine take over?

I burned real time and real money building automations that technically worked but solved the wrong problem. Not because the tools were bad. Because I had not done the thinking upstream.

Now my process is the opposite of what it used to be. I spend more time on the problem definition than the solution. I ask: what is the human doing here that a machine cannot? Where does the judgment live? What is the moment that matters most?

Build around that moment. Let the machine handle everything before and after. Keep the human exactly where the human needs to be.

What Getting It Right Looks Like

I do not think about AI as replacement anymore. I think about it as augmentation. The word matters because the frame matters.

Augmentation means the human stays. It means you are investing in your people, not working around them. It means the coordinator is still on the phone, still bringing warmth and judgment and presence to a call, but now they have the right words on their screen the moment they need them.

It means my team is not fighting the machine. They are working with it. And the work is better because of that relationship, not in spite of it.

I got here slowly. More slowly than I would have liked. But the mistakes taught me what mattered, and what I built on the other side of those mistakes is something I actually believe in.

If you are in the early stages of building with AI, here is the one thing I would tell you: stop asking what you can eliminate. Start asking what you can amplify. The business you build around that question is a fundamentally better one.