Understanding AI through the work it changes.

Many organisations appoint employees to promote generative AI, identify use cases and help colleagues build confidence. Yet the role is often underspecified and overloaded.

My research examines how champion programmes should be designed: which competencies champions need, how they connect strategy and local practice, and which resources, mandates and learning formats allow them to create sustained value.

• How should GenAI champion roles be designed?
• How do champions translate between management, technology and functions?
• How can organisations assess programme outcomes?

AI agents can search, compare and recommend products through conversation. This may reduce effort, but it can also reshape perceived autonomy, transparency and control.

Through controlled online-shopping experiments, I study how agentic interfaces change usability, cognitive decision effort, autonomy, decision quality and satisfaction compared with conventional interfaces.

• When do agents reduce effort without reducing autonomy?
• How do transparency and control shape user responses?
• What makes an AI recommendation useful and trustworthy?

Efficiency is only one outcome of generative AI use. AI also changes how people learn, coordinate, communicate, test results and distribute expertise within teams.

My work investigates both productive and adverse mechanisms of human–AI collaboration, taking a sociotechnical view of performance and the conditions under which benefits persist over time.

• How does GenAI change teamwork beyond task completion?
• Which practices preserve learning and trust?
• How do organisations manage new risks and maintenance work?

I combine qualitative organisational research—including interviews, observations and case studies—with controlled experiments and mixed-method evaluations.

Working closely with organisations helps surface the real tensions of AI adoption while creating insights that are useful to scholarship and practice.