Toward an AI-Powered Computational Testbed for Workforce Policy

TL;DR

This paper proposes a novel AI-powered simulation platform using generative agents to forecast employee responses to organizational changes, aiding workforce management during AI integration.

Contribution

It introduces a computational architecture for dynamic employee agents that incorporate HR data, psychometrics, and digital activity to simulate workforce responses.

Findings

Proposes a simulation platform for workforce response forecasting.

Details the architecture and safeguards for responsible deployment.

Highlights the importance of this tool for managing AI-driven workforce changes.

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.