Backfiring AI? AI Deployment in Workplace

Seeking value from artificial intelligence (AI) technologies, firms are rapidly deploying them to augment employees and improve business performance. The diffusion of AI into a firm’s business processes affords the tracking of task actions performed by high-performing employees and the codification of best practices into recommendation systems and training programs. The rising trend in AI deployment reveals managers’ expectations that AI-facilitated knowledge transfer would elevate overall firm performance. However, deploying AI in a workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity. In this paper, we study this problem of employee incentive issues when deploying AI in a competitive workplace, using a game-theoretic model. Our results show that when employees compete using both tangible (“hard”) and intangible (“soft”) skills, firm policies that favor AI-facilitated knowledge transfer and task outcome-based compensation may lower firm performance. We illustrate that payoffs from AI deployments depend on workforce heterogeneity, reliance on tangible skills, the skill disparity between employees, and AI efficacy. Using our model, we develop policy recommendations for maximizing the return on organizational AI deployments. Our results suggest that some ostensibly simple solutions, like guaranteeing or increasing the wages of adversely affected employees, may not solve the problem effectively, and firms would have to judiciously choose optimal AI efficacy levels for achieving better outcomes.

This paper was accepted by D. J. Wu, information systems.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.03108.