Agentic AI in Strategic Management Research: Methods, Promises, and Pitfalls

Across presentations, speakers emphasized that agentic AI systems—collections of semi‑autonomous research agents—can dramatically expand what scholars are able to study, from accelerating theory development to building qualitative datasets at unprecedented scale and simulating counterfactual strategic scenarios. At the same time, the panel highlighted key risks, including over‑reliance on automation, degradation of research quality, and the unintended sidelining of PhD student learning. All presenters stressed that AI is most powerful when used as a collaborative tool that sharpens, rather than replaces, human judgment.

The discussion underscored that the central challenge for the field is not speed, but rigor, transparency, and training. As AI lowers the cost of prediction and analysis, strategy scholars’ distinctive contribution increasingly lies in framing problems, evaluating assumptions, and designing credible interventions. The panel concluded with a call for shared experimentation, clearer norms around disclosure and validation, and renewed attention to mentorship—ensuring that agentic AI raises the ceiling of strategic management research without lowering its standards.