The AI Teammate Paradox: Can Design Features Mitigate AI Limitations and Shape Job Crafting?

In 2025, the Hong Kong government designated artificial intelligence (AI) as a core driver of the city’s future development and committed to positioning Hong Kong as a global hub for AI innovation. Today, approximately 73% of organizations in Hong Kong have integrated AI into their workplaces, transforming AI from a mere functional tool into an indispensable “teammate” in daily operations. However, as AI teammates become increasingly integrated into work environments, an AI teammate paradox has emerged. This paradox highlights the tension between AI’s ability to enhance efficiency and its lack of human-like social qualities that are vital for sustaining employees’ motivation and engagement in team contexts. One such example is job crafting, which is a self-initiated process through which employees actively redefine and adapt their work roles. This paradox may inadvertently undermine employee engagement and constrain the full benefits of AI integration. To address this challenge, this project aims to explore how AI teammates influence job crafting behaviors and how AI systems can be designed to foster promotion-focused (beneficial) job crafting while mitigating prevention-focused (detrimental) forms. This project comprises four interrelated objectives. First, it identifies two defining characteristics that distinguish AI from human teammates—specifically, lack of self-interest and voice capability—and suggests that these features are critical to understanding employees’ job crafting responses. Second, it examines how the absence of these characteristics influences job crafting through two psychological mechanisms: felt obligation for contribution and perceived personal control. Third, building on these insights, it develops two design features—dynamic contribution tracking and constructive dissent expression—that aim to compensate for AI’s limited social characteristics. These features are designed to enhance employees’ felt obligation for contribution and foster promotion-focused job crafting while reducing prevention-focused behaviors. Fourth, it theorizes and empirically tests the effectiveness of these design features in shaping job crafting through the identified psychological mechanisms. Collectively, by achieving these four objectives, this project will establish a theoretical framework for understanding how AI teammates shape employee perceptions and behaviors. For organizations, it offers practical guidance for deploying AI teammates to improve employee performance and increase team adaptability. For AI system developers, it informs the creation of AI teammates that overcome inherent social limitations and better facilitate collaborative work. For policymakers, it offers support for designing AI governance and workforce development strategies that enhance productivity.