The New Moat: Knowledge Compounding, Not Data Hoarding
Thesis: Data is not a moat. Compounding knowledge is. The winners will build systems where every case improves the next case—like a self-improving operation.
Data hoarding is the old strategy
Most orgs can tell you:
- how many cases they submitted
- what their premium volume was
- what their close rate is
But they cannot tell you:
- which intake errors cause 60% of rework
- which carrier requirement patterns predict a decline
- which advisor behaviors correlate with NIGO
- which “fixes” actually reduce cycle time long-term
They have data. They don’t have learning.
Compounding knowledge requires a loop
AI is not a feature. It’s a loop:
Detect → Validate → Route → Explain → Learn
That last word is the moat.
If your system doesn’t learn, you’re building a treadmill:
more cases → more people → more chaos → more cost.
“Experiential knowledge” is just unlabeled training data
Your case team already knows:
- what will get kicked back
- what wording works with which carrier
- what order of operations prevents stall
The problem is it lives in:
- inboxes
- Slack
- brains
- “call me” moments
You need a system that captures it at the point of work.
What to build: Micro-Lessons in the workflow
Every exception should trigger:
- “What happened?” (pre-filled)
- “Why?” (2 clicks: carrier requirement, missing data, mismatch, compliance, etc.)
- “What fixed it?” (choose from system suggestions or write)
- “Should this become a rule?” (yes/no)
That’s how expertise becomes infrastructure.
