We didn't predict agentic AI. We built the model that agentic AI now has to live inside — twenty-seven years ago.

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@procurement-insights
We didn't predict agentic AI. We built the model that agentic AI now has to live inside — twenty-seven years ago.

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In January 2025 I asked whether any AI could tell me what time of day orders come in. One did — right after I told it order timing was the variable. That one-second gap, between answering a question and knowing which question to ask, is why adding an AI agent to an undiagnosed field doesn't build an intelligent ecosystem. It builds a faster version of the existing misalignment. Gemba walks one floor. The agent field has no floor.
Why IT keeps getting blamed for failures that were decided before the platform was ever chosen.
The companies everyone cites as proof that AI works were winning long before AI arrived.
Four firms. Four acronyms. Forty years. One diagnosis — and the failure rate never moved. Why?

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"What really happens when your AI technology works — and nothing changes?"
From Ariba 2007 to Vertice 2026 — has the substrate misalignment that derails most initiatives finally been addressed?
When the CFO and the COO are the same person, what happens to the silos? I wrote about this in 2008. Brittany Bagley just embodied it in 2026.
"For twenty years we tried to eliminate maverick spend. The AI era is about to show us it was never the problem — it was the signal."
What do a Chief Data Officer and Chief AI Officer have in common?

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Phase 0 isn't a readiness methodology. It's the mechanism for escaping forty years of accumulated technology debt.
A frontline agent is not an agent-based model. The difference is why your AI will fail.
It's time we stopped buying AI capability — and started committing to the outcomes it's supposed to produce.
Gartner calls it an adoption gap. The evidence — including Gartner's own — says it's an absorption gap. Knowing the difference decides whether you fix it.
Why would AI produce any better results than previous technologies?

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We don't predict failure. We identify the conditions that produce it.
AI does not break broken clinical processes. It perfects them at machine speed.