Leadership adaptation and business process management in the age of generative AI: evidence from UK pharmaceuticals

Purpose Generative AI is increasingly influencing business processes through hybrid human–AI workflows. However, the success of this transformation depends not solely on technological capability but on how leadership interprets, governs, and integrates AI-enabled work. This study explores leadership adaptation in the UK pharmaceutical sector and advances the Recursive Evolutionary Leadership Activation Loop (RELAL) as an interpretive synthesis for understanding this dynamic. Design/methodology/approach A qualitative interpretive design using semi-structured interviews and a focus group with UK research-based pharmaceutical leaders and middle managers generated insights into how generative AI is influencing process governance, workflow execution, and leadership practice within a highly regulated context. Findings The findings indicate that leadership adaptation shapes how AI-enabled process change is governed and stabilised. Participants described shifts from control-oriented oversight toward orchestration, guardrail-setting, and the enabling of bounded experimentation. RELAL conceptualises these dynamics as recursive interactions between strategic intent, middle-manager mediation, governance refinement, and process evolution under regulatory constraint. The organisation may appear stable, yet underlying coordination mechanisms shift incrementally as hybrid human-AI routines become embedded. Practical implications The study offers guidance for leaders in regulated industries seeking to integrate conversational and agentic AI into business processes while maintaining human-in-the-loop accountability and adaptive governance structures. Originality/value By examining generative AI adaptation within a tightly regulated pharmaceutical context, this paper contributes to leadership and business process management scholarship and advances RELAL as a heuristic model for understanding recursive AI-enabled organisational change.