Shane Butler

<start of post>

I spent three years building a "Strategic Insights Engine" for a company that didn't actually want insights. They wanted a mirror.

Every Monday, I’d pull the same numbers, format them into the same blue-and-grey slides, and present them to a room of people who had already decided what the story was. If the data matched their gut, I was a genius. If it didn't, the data was "noisy" and we needed to "drill down further" (which was code for: keep looking until you find the number that makes us look good).

It was the ultimate corporate theater. We were burning forty hours a week to maintain a narrative that everyone knew was hollow.

Then I started using open models to automate the entire pipeline.

What used to take me two days now takes about four seconds. I pointed a local model at our raw warehouse, gave it a set of validation rules, and let it rip. The first time it ran, it found a $200k discrepancy in our attribution model that we’d been ignoring for eighteen months.

I brought it to the meeting. I didn't bring the blue slides. I just brought the truth.

The room went silent. Not because they were impressed, but because the theater had finally been shut down. You can't argue with a machine that shows its work and doesn't care about your promotion cycle.

That’s the thing about AI in analytics. It doesn't just make us faster. It makes it impossible to keep playing pretend.

A lot of people are scared that AI will take their jobs. And if your job is just performing the theater, it probably will. But if your job is finding the truth? You just got a superpower.

We’re teaching how to build these "truth machines" in our free workshop this Friday. No theater. Just code and context.

Are you building something real, or just greasing the cogs?

Join us here: https://lnkd.in/example-link

<end of post>