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industry reportChapter 6Eigenvector Research · 2025

Patternomics: A Framework for Enterprise AI Pattern Recognition and Reuse

Marco van Hurne (Eigenvector Research)

Abstract

We present Patternomics, a framework for identifying, cataloguing, and reusing successful patterns in enterprise AI deployments. Patternomics provides a systematic approach to building institutional knowledge about what works in Zone III deployments.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Patternomics addresses the institutional learning problem for Zone III deployments. Rather than reinventing the wheel for each new deployment, Patternomics provides a systematic approach to capturing and reusing successful patterns.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Patternomics framework
  • →Enterprise AI pattern catalogue
  • →Institutional knowledge management

Topics

pattern recognitionenterprise AIknowledge reuseinstitutional learning