What a stalled platform deal taught me about the 1% of context that matters
2026-05-27
A few years back, I had a $4.5M analytics platform deal going sideways.
Too many stakeholders, poorly qualified, everyone "interested," and no one actually deciding. The kind of deal that doesn't blow up. It just dies slowly.
So we reset it. Ran real discovery this time. Underneath the noise there were only two things that mattered to them: inventory visibility and operational risk. We quantified what those were costing, found the person who could actually say yes (the COO, who had been in the room the whole time), and stopped selling the technology. We started selling the decision.
Closed in about five months.
I keep thinking about that deal because of all the AI talk.
It didn't turn on information. We were drowning in information. It turned on judgment: figuring out which 1% of context actually changed the answer, and having the discipline to throw the rest out.
That is the exact skill everyone now assumes LLMs are about to make worthless.
I think that's backward.
Yes, LLMs are displacing knowledge workers. But here's the part nobody says out loud: the only way to get an LLM to beat a good knowledge worker is to do exceptional knowledge work first. We all have the same models now. The model was never the differentiator. The framing is. The context is. Knowing what decision you are even trying to make is.
A weak operator writes a vague prompt and gets a confident, mediocre answer. A strong one frames the actual problem and gets something worth using. The model didn't close that gap. If anything, it widened it.
Off-the-shelf prediction is getting cheap and abundant. Judgment, optimization, and knowing what not to do hold their value.
So I've stopped believing LLMs replace knowledge work. They raise the return on it. Generic prediction is becoming a commodity. Applied judgment is the moat.
First published on LinkedIn, 27 May 2026. Lightly revised. Original post.