In 2008, I published a piece titled “Similarity Heuristics, Iterative Methodologies, and the Emergence of the Modern Supply Chain.”
Very few people engaged with it at the time.
That wasn’t because it was obscure or academic.
It was because it challenged an assumption the market wasn’t ready to let go of.
The dominant belief then was simple:
If you could capture the “right” reference model — the right process, the right system, the right best practice — you could standardize around it and scale.
The article argued the opposite.
That in complex supply chains, the baseline never holds.
That weights, conditions, and relationships shift continuously across multiple streams.
And that equation-based, static models don’t fail because they’re poorly designed — they fail because reality moves faster than they can adapt.
I used a simple analogy:
Most enterprise systems are built like a canvas — they capture a moment in time, then rely on change management to keep reality aligned to the picture.
What complex systems actually need is a camcorder — something that observes motion, replays context, and adapts as conditions change.
At the time, that framing landed as theoretical.
Today, it reads as obvious.
AI agents, semantic layers, orchestration fabrics — all of them expose the same constraint:
Static assumptions collapse in dynamic environments.
AI doesn’t politely wait for humans to reconcile inconsistencies.
It operationalizes them.
That’s why semantics is now being treated as infrastructure.
And why organizational readiness — ownership, decision rights, accountability — has become unavoidable.
The insight didn’t change.
The environment did.
What was hard to see in 2008 is now visible because failure can no longer hide behind abstraction, timelines, or “next phase” optimism.
The lesson remains the same:
When systems move, meaning must move with them.
And when meaning isn’t governed, technology doesn’t fix complexity — it accelerates it.
Sometimes ideas don’t arrive early.
They arrive before the cost of ignoring them becomes visible.
— Procurement Insights
-30-
Procurement Insights
Why This Made No Sense in 2008 — and Why It Suddenly Does
Posted on December 20, 2025
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In 2008, I published a piece titled “Similarity Heuristics, Iterative Methodologies, and the Emergence of the Modern Supply Chain.”
Very few people engaged with it at the time.
That wasn’t because it was obscure or academic.
It was because it challenged an assumption the market wasn’t ready to let go of.
The dominant belief then was simple:
If you could capture the “right” reference model — the right process, the right system, the right best practice — you could standardize around it and scale.
The article argued the opposite.
That in complex supply chains, the baseline never holds.
That weights, conditions, and relationships shift continuously across multiple streams.
And that equation-based, static models don’t fail because they’re poorly designed — they fail because reality moves faster than they can adapt.
I used a simple analogy:
Most enterprise systems are built like a canvas — they capture a moment in time, then rely on change management to keep reality aligned to the picture.
What complex systems actually need is a camcorder — something that observes motion, replays context, and adapts as conditions change.
At the time, that framing landed as theoretical.
Today, it reads as obvious.
AI agents, semantic layers, orchestration fabrics — all of them expose the same constraint:
Static assumptions collapse in dynamic environments.
AI doesn’t politely wait for humans to reconcile inconsistencies.
It operationalizes them.
That’s why semantics is now being treated as infrastructure.
And why organizational readiness — ownership, decision rights, accountability — has become unavoidable.
The insight didn’t change.
The environment did.
What was hard to see in 2008 is now visible because failure can no longer hide behind abstraction, timelines, or “next phase” optimism.
The lesson remains the same:
When systems move, meaning must move with them.
And when meaning isn’t governed, technology doesn’t fix complexity — it accelerates it.
Sometimes ideas don’t arrive early.
They arrive before the cost of ignoring them becomes visible.
— Procurement Insights
-30-
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