Reihaneh Bidar

Reihaneh is a Lecturer in Business Information Systems at the UQ Business School, The University of Queensland. She earned her PhD from the Queensland University of Technology, School of Information Systems. Her research centres on how organisations manage the complexities of AI, automation, and digital integration. Reihaneh's work delves into the impact of emerging technologies like AI on the redesign and organisation of work, while also addressing the challenges of managing their potential negative consequences. Her research provides actionable insights for non-governmental organisations and public agencies, helping them to deliver value through the effective implementation of human-AI hybrid systems. Reihaneh teaches managing business data and information retrieval to undergraduate and postgraduate Information Systems program. She has previously developed, coordinated, and taught courses in Business Analytics, Enterprise Architecture, Design of Enterprise IoT Systems, Mobile and Pervasive Systems, and Mobile App Development for both undergraduate and master's students. Redesigning Work in Organisations with AI: This research investigates how organisations navigate the complexities of AI and create value by redesigning work through strategically aligned and effectively governed AI implementation.

[2026] Information systems strategy in the age of machine learning. Bidar, Reihaneh, Desouza, Kevin C., Watson, Richard T. and Gurrie, Brad (2026). Information systems strategy in the age of machine learning. Business Horizons. doi: 10.1016/j.bushor.2026.07.001 [2026] The best way to teach students to think critically about AI? Make them argue with it. Bidar, Reihaneh (2026, 06 24). The best way to teach students to think critically about AI? Make them argue with it Times Higher Education [2025] Closing the gaps on inscrutability: tackling challenges with knowledge integration during AI development. Rinta-Kahila, Tapani, Someh, Ida, Darvishi, Ali, Bidar, Reihaneh and Indulska, Marta (2025). Closing the gaps on inscrutability: tackling challenges with knowledge integration during AI develop