Human-AI Decision-Making: A phenomenon-driven study in personalized medicine

This thesis explores human-AI decision-making as an organizational process occurring in complex contexts. To do so, it employs a phenomenon-driven strategy, considering human-AI decision-making in organizations as a continuously emerging phenomenon in its center. The empirical evidence is gathered in the field of personalized medicine and data-driven healthcare context in Sweden, including cases of breast cancer diagnostics and personalized medicine development.

Based on the embedded case study performed, this thesis contributes to a multi-level understanding of human-AI decision making, moving beyond perspectives that view AI mainly as a decision support tool. At the organizational level, AI influences attention structures, search processes, decision premises, routines, and governance arrangements. As a result, decision-making is shown to be distributed between human and AI in ways that are neither fixed at design nor evenly spread, but allocated according to specific contextual conditions.

At the interactional level, AI becomes meaningful through ongoing sensemaking processes in which actors interpret what the technology does, what its outputs mean, and how those outputs should be incorporated into organizational action. Sensemaking for AI anticipation is done by relating AI’s output to something already familiar and already legitimate like an established professional norms, and transparent decision environment. Additionally, situated sensemaking is grounded on temporality, which means that for each decision in a point in time humans and AI can take different roles depending on the context and situatedness.

At the individual level, AI alters the conditions under which professional judgment is formed, exercised, challenged, and legitimized. At the same time, AI outputs are themselves interpreted through existing heuristics, professional frames, and organizational expectations.

Finally, practical implications focus on AI integration that is grounded on distributed effort and fluid roles within humans and AI configurations, as well as organizations periodically reflecting on which aspects of reality AI solutions are making more visible and which may be receiving less attention as a result in decision-making processes. In this way, organizations can develop forms of human–AI collaboration that support informed decisions in complex environments.