The Economic Ledger: A Preliminary Framework for Measuring the Net Productive Capacity of Human–AI Collaboration

Abstract

The question of how much productive value is gained through human–AI collaboration is both theoretical and practical and remains under-researched. Existing approaches either measure theoretical task exposure (what AI could affect) without measuring realized enterprise value, or report gross productivity gains without subtracting the labor costs of review, rework, and oversight that those gains generate. This white paper proposes the Economic Ledger (EL) — the measurement of work in an environment where AI is introduced — as a candidate higher-order latent construct that bridges this gap: a net, task-grounded measure of productive capacity unlocked through human–AI collaboration, conditioned on task fit, adoption quality, and organizational readiness, and net of verification burden. We develop its theoretical and empirical foundations across three research streams: (1) AI Potential, Readiness, and Exposure, which establishes the determinant layer; (2) Work, Task Productivity, and Human/Agent Performance, which establishes the state and outcome layers; and (3) Entangled Workflows and Networked Productivity, which establishes the workflow- and team-level aggregation layer. Drawing on more than thirty primary sources spanning field experiments, randomized controlled trials, psychometric instrument validation, process mining, and enterprise survey research, we propose a preliminary measurement architecture suitable for empirical testing with enterprise data. Key contributions include: a three-layer determinant–state–outcome separation as the non-negotiable structural principle for defensible measurement; a systematic treatment of verification burden as first-class labor rather than residual friction; and the integration of agentic workflow provenance as an emerging infrastructure requirement for enterprise-grade traceability.

Keywords: Economic Ledger, human–AI collaboration, productivity measurement, verification burden, task exposure, organizational readiness, workflow coherence, agentic workflows, SEM, EFA/CFA

Inside the full paper

1. Executive Summary
2. 1. Introduction: Defining the Economic Ledger
3. 2. Structural Principles: Determinants, State, and Outcomes
4. 3–5. Three Research Streams: Exposure, Productivity, and Entangled Workflows
5. 6. Toward a Measurement Architecture
6. 7. Implications
7. References (44 sources)