The Part of AI Work No One Posts About

At least once a week, I’m floored by the fact I’ve been working in some form of digital enablement for 17 years.

I still remember hard-coding event tracking into a WordPress site because Google Tag Manager was new and documentation was scarce. I had to track button clicks on a key page, and that kicked off a rabbit trail of writing custom code, connecting it to Google Analytics, and feeling amazed that I could watch those actions fire in real time.

Fast-forward a decade and a half. The technology is unrecognizable, but the feeling isn’t. In just a few months at Zinus, we’ve automated workflows, deployed agents that save real time, improved data analysis, and built momentum for even bigger AI projects in 2026.

But here’s the part that rarely makes it into LinkedIn posts: a lot of this work is frustrating.

Highlight reels are everywhere, but the day-to-day reality is not glamorous. Things break. Things don’t work. You test, fix, and test again. And sometimes, test again just to watch it fail exactly the same way.

Right now, I’m working on an AI agent within a platform—not GPT, Claude, or Copilot. When you’re inside a platform, you’re bound by whatever architecture the platform team designed. Sometimes that architecture doesn’t fit your business. And then what?

This week, every test I have run hits the same failure mode. I’ll eat dinner, clean up, get an idea, jump back in, configure it… and immediately hit another failure. I’ll go to bed thinking fresh eyes will help, and the next morning, same result.

So what’s the point of sharing this?

A few things:

1. Failure is actually data.

It sounds cliché, but it’s true. Every failed path teaches you how the system works—and how it doesn’t. A mentor of mine used to say:

“Figure out what doesn’t work. Then don’t do it again.”

In technical work, eliminating wrong paths is progress. You’re narrowing the solution space. Breakthroughs often rely on knowing which 90% of approaches are dead ends.

2. “Failure” often leads to something else.

I call these ancillary discoveries. Sometimes a technique won’t solve the problem you’re working on, but it reveals a method that becomes valuable elsewhere.

I’ve had analytics problems where the attempted solution failed, but it changed how I viewed the dataset—and that insight solved a different project entirely.

NASA has a long list of inventions created by accident during the space race. That’s the same pattern: solve one thing, discover five more.

3. Don’t buy into the perfection myth.

Work—especially technical work—is not a steady flow of wins. I’ve seen too many people fall into the “production trap,” where the job becomes endless clicking, meetings, and inbox management.

But solving real problems is different. It’s hard, creative, and unpredictable. My first marketing role taught me that nine out of ten ideas won’t work. Today, many companies behave like every idea must work or it’s wasted time. That mindset kills innovation.

Innovation is what you get after the failures, not before.

The takeaway

Be encouraged. Building solutions—especially AI solutions—is hard and often frustrating. That’s normal.

Don’t let other people’s highlight reels distort your view of real work. Most days, the people doing technical work are battling constraints, rethinking approaches, and hitting dead ends.

The key is simple: show up again tomorrow. Test again. Learn again. Adjust again. Eventually the solution breaks through—and along the way, you’ll learn techniques and patterns others never see because they only chase easy wins.

That’s the real side of AI work. And it’s the part that makes the results worth it.