Artificial intelligence as a teammate: How to achieve a successful collaboration

How does artificial intelligence (AI) change work systems, tasks, roles, and collaboration practices in real healthcare teams? The research team chose healthcare, in particular intensive care units (ICUs), as research context because both the opportunities and risks of collaborating with AI are particularly high. In six studies using quantitative and qualitative methods and involving over 1,101 nurses and physicians, data scientists, AI developers, and medical students, the research team found that a careful evaluation of human versus AI strengths and weaknesses when designing future work systems can improve the job satisfaction and well-being of workers. This may help alleviate the skills shortage through reduced administrative tasks or enhanced problem-solving opportunities. Furthermore, the team found that the use of AI impacts not only human-machine collaboration but also fundamental human team processes such as knowledge sharing, speaking up, and problem-solving. The results have important implications for practice, including developing new approaches to team collaboration and leadership, as well as guidelines for managerial decisions regarding AI implementation and policymaking.

The increasing integration of AI into work systems is reshaping the nature of work. AI-driven automation and augmentation have the potential to enhance productivity, improve decision-making, and address the skills shortage. However, we cannot take for granted that AI will inevitably augment human performance, as poorly designed systems risk imposing new burdens on workers and complicating implementation efforts. An in-depth understanding of how AI can effectively enhance rather than impair work conditions and how humans collaborate effectively with AI is therefore needed.

This project addressed these gaps to increase our understanding of successful human-AI teaming in complex, high-risk systems. Through six studies applying both quantitative and qualitative methods and including multiple stakeholder perspectives (N~1,101), the research team explored how AI can foster motivating and health-promoting work and how human-AI teams can create better outcomes than either humans or AI could achieve alone.

Key insights that address new demands in work organisation, collaboration, job tasks, competence profiles, and worker training have been summarised in the form of guidelines, decision frameworks, and recommendations for policymaking and practice. This will help leverage the opportunities and respond adequately to the risks of implementing AI in healthcare and other high-risk work settings. Future research should investigate how AI can be imbued with more advanced teaming capabilities, for example, through the combination of AI and robotics. These results are relevant not only to healthcare and other high-stakes industries (e.g. finance, aviation, or emergency response) but to any work system where humans must “team up” with AI to produce safe and reliable outcomes.

Three main messages

1. With good work design, AI can improve jobs: AI has the potential to increase job satisfaction, motivation, and well-being among healthcare professionals and alleviate skills shortages. However, this is only possible if new tasks, roles, and collaboration practices are designed based on the complementary strengths and weaknesses of humans and AI. Failure to consider optimal forms of human-AI teaming poses significant safety and performance risks. These include overreliance, complacency, loss of situational knowledge, and inability to control AI outputs.
2. AI changes the way we communicate and solve problems in teams: Human-AI team collaboration can influence not only how team members interact with the AI but also with each other. The project’s results show that while accessing information from human team members was negatively associated with both developing new hypotheses and speaking up, integrating AI into the team’s knowledge base helped generate new ideas, critically evaluate information, and foster speaking up. The presence of an AI system can thus be used to disrupt negative team dynamics such as confirmation bias and groupthink, encouraging team members to consider alternative perspectives and voice their concerns more freely.
3. Effective human-AI teaming requires new skills that go beyond technological aspects. Medical and nursing education programmes, as well as professional training, should focus on how to successfully “team up” with AI, i.e., how to build trust, communicate, and make joint decisions effectively. Leaders also need to learn how to manage these new forms of human-AI collaborations. Furthermore, preliminary findings indicate that using for example ChatGPT as a “learning coach” may have the potential to support medical students in the diagnostic decision-making process and improve the quality of clinical decision-making.

From Tools to Teammates: Human-AI Teaming Success Factors in High-risk Industries