Making Sense of Teaming with AI: Exploring Human-AI Team Development Through Interpretative Phenomenological Analysis

As artificial intelligence (AI) becomes increasingly embedded in collaborative work environments, Human-AI Teaming (HAIT) is reshaping how employees experience team development and view AI as an active teammate, versus passive tool. This in-progress dissertation study uses Interpretative Phenomenological Analysis (IPA) to explore how employees make sense of team development within HAIT context. Grounded in Business Psychology and designed for I-O psychology application, the study explores how employees interpret milestones of human-AI team development and navigate emerging socio-technical dynamics. The primary research question guiding this inquiry is: How do employees make sense of their experiences of team development while working on Human-AI teams? Supporting questions examine perceptions of team collaboration, trust calibration, and shared cognition with AI across group developmental stages. The study introduces a novel conceptual scaffold integrating Tuckman’s Group Development Model and the Machines as Teammates framework that is anchored by Media Synchronicity Theory, Trust in Automation, and Shared Mental Models. This qualitative study will analyze semi-structured interviews with 8–10 U.S.-based professionals actively adopting HAIT practices (e.g., healthcare, professional services, or technology). Participants must have either 12+ months of HAIT experience or have completed a full HAIT project cycle. Interviews will be conducted via Zoom, transcribed, and analyzed using IPA’s six-step process, supported by layered coding strategies to ensure idiographic depth and analytic rigor. Expected findings will illuminate how employees may perceive AI as a teammate, navigate evolving socio- technical dynamics, and form shared understandings in hybrid human-AI environments. These insights will contribute to both theory and practice by (a) extending traditional team development models into socio-technical domains, and (b) informing I-O professionals with evidence-based guidance for designing, facilitating, and sustaining human-AI teams in ways that preserve human-centric work environments, while leveraging AI capabilities responsibly. Further implications for research include advancing existing team development theory by offering a conceptual framework designed for HAIT dynamics, emphasizing the importance of interdisciplinary theory and qualitative nuance in understanding emerging team phenomena.