The Documentation Paradox in GTM Engineering

There's a chasm in every business. On one side, you have unstructured data - the wild, untamed conversations, the scattered insights, the tribal knowledge that lives in people's heads. On the other side, you have structured data - the neat rows and columns that machines can understand and act upon.

For years, we've tried to bridge this gap with OCR, NLP, and RPA. Like trying to teach a fish to climb a tree - possible, but not exactly elegant.

Then came AI. Not just any AI, but language models that actually understand context, nuance, and intent.

Here's what everyone gets wrong about AI in GTM:

But winners know: It's not about the tools. It's about the data.

Quality in = quality out. It's that simple.

Stop thinking about documentation as a chore. Start thinking about it as your competitive moat.

The companies that win won't be the ones with the fanciest AI tools. They'll be the ones who turned their messy, human knowledge into structured, machine-readable intelligence.

That's not just documentation. That's engineering.

And that's how you win.