Toward an AI-Powered Computational Testbed for Workforce Policy

2026-05-18

AI Summary

This study addresses the critical gap in effective tools for predicting employees’ psychological and behavioral responses to AI integration in knowledge work, which hinders precise management of workforce transformation. It proposes a novel computational framework that, for the first time, integrates generative agents with organizational behavior theory to construct dynamic employee agents powered by large language models. These agents synthesize human resource records, psychometric assessments, and digital behavioral logs to simulate individual cognitive, emotional, and behavioral trajectories during organizational change, all within a multi-layered privacy-preserving architecture. The resulting infrastructure offers a scalable and ethically grounded platform for forward-looking simulations, thereby enabling responsible, AI-informed workforce policy design.

Abstract

Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge work currently affects a substantial share of the global workforce, yet this transition proceeds without tools to forecast how individual employees will respond psychologically and behaviorally. We combine recent advances in LLM-powered generative agents with foundational management science and organizational behavior research to propose dynamic employee agents. Among consenting populations, these agents can be seeded with HR records, validated psychometric measures, and digital activity data to simulate employees' cognitive, emotional, and behavioral trajectories across successive workdays during planned organizational changes. In this article, we detail the computational architecture required to construct this simulation platform and define the privacy, accuracy, and representativeness safeguards necessary for responsible deployment. We argue that establishing this prospective forecasting infrastructure is a critical technical requirement for managing the current global workforce realignment around AI.

Problem

• workforce policy
• AI integration
• employee response
• organizational change
• forecasting

Innovation

• generative agents
• LLM-powered simulation
• workforce policy
• organizational behavior modeling
• AI-driven forecasting