The rapid integration of artificial intelligence (AI) tools into project work alters how collaboration unfolds and how e-leadership is exercised, with implications for performance. This study explores how e-leadership practices shape the relationship between human-AI collaboration and perceived team effectiveness in project-based settings.
A qualitative design was adopted, drawing on 34 semi-structured interviews with project managers across five UK industries. Sampling targeted managers as boundary spanners across diverse project types from site-based construction to innovation-driven squads, to capture the socio-technical alignment process. Data were analyzed thematically using a Gioia-informed approach to identify how e-leadership practices interact with varying orientations of AI integration.
The analysis identifies a curvilinear pattern of bounded augmentation, where effectiveness peaks in a zone of balanced use but declines under under-use and over-reliance. This trajectory is governed by e-leadership practices. Proactive engagement combined with creation-oriented use generated the highest effectiveness, while reactive approaches paired with automation or creation produced breakdowns. These dynamics are synthesized in a e-leadership-AI orientation matrix mapping how social (leadership engagement, trust, ownership, mediation and alignment) and technical (automation, creation, reliability, distraction and integration) subsystems combine to enable or erode team effectiveness.
To achieve balanced augmentation, leaders must proactively frame AI's role, embedding validation checkpoints and human authorship clauses to maintain accountability. Organizations should cultivate a culture of critical engagement with AI outputs, while e-leadership development must focus on building competencies in mediating, filtering and legitimizing AI contributions within digital workflows.
The study integrates e-leadership and human-AI collaboration within a socio-technical systems lens. It refines team effectiveness theory by showing how mediators such as trust, cohesion and accountability are reshaped when AI-generated contributions enter collaboration, and by demonstrating that augmentation is bounded rather than linear.
