Principal Workers: Orchestrating AI Agents Improves Productivity

Abstract

For the artificial intelligence (AI) economy, we propose a principal worker model where each knowledge worker is both a task architect and an AI agent orchestrator. As task architect, the worker divides each task into subtasks and delegates some to AI agents. As agent orchestrator, the worker optimizes outputs from AI agents and synthesizes all outputs into a coherent deliverable. Workers who augment themselves with AI agents produce more and earn more. To reap productivity gains, organizations must implement policy changes on how they develop workers and incentivize them to use AI effectively.