Despite growing research on human–AI collaboration, practical methods for building effective human–AI partnerships that maximize organizational performance remain limited. The study addresses this gap by developing an applied model grounded in economic theory to optimize task allocation between humans and AI within organizations and to guide human capital investments that strategically leverage and amplify unique human strengths.
The study uses theory-based conceptual analysis combined with mathematical modeling to operationalize Ricardo's comparative advantage theory for optimizing human–AI collaboration outcomes. Mathematical models were developed to optimize task allocation and identify human capital investment priorities. Validation was conducted using simulated data of semiconductor processing technicians, comparing the comparative advantage model's performance against random task assignment baselines.
The comparative advantage model demonstrates 63% higher productivity compared to random task assignments when validated with semiconductor processing technician data. Results confirm that strategic alignment of human and AI capabilities through comparative advantage principles significantly enhances collective performance, validating economic theory application in AI-era human resource management (HRM).
The study equips HRM practitioners with systematic methodologies for optimizing human–AI collaboration through strategic task allocation and targeted human capital investments. The mathematical models provide actionable tools for workforce planning, training needs assessment and performance optimization in AI-integrated work environments.
This study demonstrates the first systematic application of comparative advantage theory to human–AI collaboration, introducing a fresh analytical lens for allocating tasks between humans and AI to maximize organizational effectiveness. By integrating economic theory into HRM, an area where scholarly work remains limited, this interdisciplinary study makes a novel contribution that advances theory, informs future empirical research and provides HR professionals with actionable models to prepare the future workforce in the AI era.
