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industry reportChapter 6Eigenvector Research · 2025

Tokenomics of Enterprise AI: Economic Models for Agentic Workflows

Marco van Hurne (Eigenvector Research)

Abstract

We present a framework for understanding and optimizing the economic model of enterprise AI deployments, covering token costs, compute allocation, value attribution, and ROI measurement for agentic workflows.

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)

Tokenomics is the business case layer for Zone III. Without a clear economic model — understanding the cost per workflow, the value generated, and the ROI — Zone III deployments remain experiments rather than enterprise programs.

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

  • →Enterprise AI economic framework
  • →Token cost optimization
  • →Value attribution methodology

Topics

tokenomicseconomic modelscost optimizationROI measurement
Relevance Scores
Long-Horizon Score82
Enterprise Score97
Completeness86
Paper Info
Year2025
VenueEigenvector Research
Typeindustry report
ChapterCh. 6
Authors1
Zone III Analysis
Frameworks