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Large People ModelHuman Operating Architecture
Founder/framework analysisPublic record

Coordination debt in large organizations

How accumulated ambiguity across ownership, decisions, communication, information, platforms, and governance slows execution at enterprise scale.

Framework-based analysisMay 26, 2026By Chad Stewart7 min read

Research record

Read the claim with its evidence boundary.

Evidence basis
Founder/framework analysis
Method
Founder/framework synthesis of the mechanisms and sources described in the record.
Citations / endnotes
Formal citations or endnotes are not yet attached to this public record.
Version / date
Published May 26, 2026; a separate version identifier is not yet supplied.
Limitations
The evidence label and record sections define the current boundary. No customer outcome is inferred.
What would change the conclusion
Contradictory external research, benchmark data, or verified customer outcomes would require review.

Executive summary

Coordination debt accumulates when unresolved operating model ambiguity becomes normal work. The debt appears as extra meetings, stalled decisions, manual handoffs, duplicated tools, and governance workarounds.

Core thesis

Large organizations often lose speed because they keep paying interest on unclear ownership, decisions, information, platforms, and controls.

What LPM diagnoses

LPM treats coordination debt as a layered operating model pattern.

  • Where accountability is assumed but not assigned.
  • Where decisions age or get reopened.
  • Where communication volume replaces clarity.
  • Where information is duplicated or distrusted.
  • Where platforms fragment workflow and context.
  • Where governance creates delay or misses real risk.

Current evidence status

This is published framework analysis. Benchmarks and mini-data studies are planned to test the recurring patterns.

Keep the Evidence Clear

Move from research question to operating signal.

Use the related metrics, use cases, and knowledge objects to inspect the operating-model problem without overstating what has been proven.