LEADERSHIP ROLES IN AI IMPLEMENTATION: A QUALITATIVE STUDY OF SOCIO-TECHNICAL ALIGNMENT
TL;DR
It is argued that AI implementation reconfigures established leadership functions rather than replacing them and positions leadership as a practical mechanism of socio-technical alignment in AI-enabled organizational change.
Abstract
Artificial intelligence (AI) is increasingly embedded in organizational work, yet technological availability alone does not ensure meaningful implementation. Organizations must align AI tools with workflows, employee capabilities, business priorities, trust, and accountability structures. This study examines the leadership roles that become particularly salient during AI implementation and explores how these roles are enacted at the team and managerial level. The study employs an exploratory qualitative design based on nine semi-structured interviews with leaders and managers directly involved in AI-related organizational change across organizations with different levels of AI maturity. The data were analyzed using theory-informed thematic analysis and cross-case comparison. The findings identify five interconnected leadership roles: socio-technical translator and mediator, selective sponsor and filter, AI capability builder, trust calibrator and boundary-setter, and holder of human accountability. Leaders were found to simultaneously enable and constrain AI use, translate technological possibilities into organizational practice, develop employee capabilities, establish conditions for appropriate reliance on AI, and preserve human responsibility for consequential outcomes. The study argues that AI implementation reconfigures established leadership functions rather than replacing them and positions leadership as a practical mechanism of socio-technical alignment in AI-enabled organizational change.