When I Fell for Precision. Again. (And what I finally understood about why we keep doing it)

Years ago, at Grainger, we built a pricing model that looked brilliant but stalled in the field. Too smart to sell. That lesson hurt. What can't be explained can't be executed. Full story here: https://lnkd.in/gv9-A4WZ

So you'd think I wouldn't fall into the trap again.

But I did.

A few years later, we built a freight model for a $5B enterprise client. It was precise. Modeled for a 2-5% lift. The working teams loved it.

Then came the cascade:

• •More scenarios
• •More precision
• •More slides

We delivered everything and more.

Eight weeks of analysis. Twelve weeks of technical conversations. While the market is moving.

When it finally hit the decision table, the verdict was short: "Too complex to execute."

The window had closed. The opportunity had passed. And we had built something too smart to survive.

Eventually, we simplified it into a matrix model. Clear. Explainable. Executable. Not perfect. But usable. And it worked.

So why did I fall for it again?

Because complexity feels like progress. Because smart people like showing their work. Because more detail feels safer than saying, "This is enough to act on."

But we weren't solving the problem. We were solving our fear of being wrong.

The pattern was clear:

• •The Planning Fallacy: "If we plan precisely enough, we can control uncertainty." Reality doesn't care.
• •The Sunk Cost Fallacy: "We've already built 80 percent. Might as well finish it." Even if it no longer solves the problem.

Together, they form the precision trap.

And here's what I finally saw clearly:

• •Perfection becomes a proxy for progress
• •Complexity masquerades as rigor
• •Momentum doesn't die from bad ideas. It dies from overbuilt ones.
• •The model isn't the deliverable. Execution is.

What finally stuck:

• •If it can't be explained in 2 minutes, it won't be executed in 2 weeks
• •If the field can't use it, the math doesn't matter
• •If the market is moving fast, your model needs to move faster

What I do differently now:

Before going further, I run a Precision Threshold Checkpoint:

• •Can the average rep explain this in 60 seconds?
• •If the market shifts in 30 days, will this still hold?
• •Are we solving the problem or trying to feel more certain?

If we can't say yes, we pause.

Because complexity isn't protecting us. It's just slowing us down.

This isn't just a freight or pricing story. This shows up in:

• •Product launches that never launch
• •AI pilots stuck in data wrangling
• •Strategy decks no one uses

Clarity isn't a tradeoff. It's the unlock.

Final thought: "Progress is not what you add to the model. It's what the model unlocks in motion."

What's the smartest model you ever had to abandon? And what finally made you let it go?