Dr Yirui Jiang

Dr. Yirui Jiang is an interdisciplinary research scientist specializing in AI enterprise applications and AI ecosystems. Dr. Jiang is currently working in the Department of Computer Science and Technology at the University of Cambridge, a previously Research Fellow and Examiner Board Member at Saïd Business School, University of Oxford, and an Adjunct Professor at the London School of Innovation. Co-director of CAM AI LAB. She is the member and fellow of Academy of Management (AOM), the Institute for Operations Research and the Management Sciences (INFORMS), IEEE, Royal Society, British Academy, and  Alan Turing Institute.Dr. Jiang is an FHEA-certified supervisor and fellow scientist, guiding PhD and Master's students in the following research areas: AI Ecosystem, AI for Business Transformation, and AI for Industry Application.

Dr. Jiang’s research focuses on the commercialization of AI technologies, the design of cross-industry AI ecosystems, and the strategic pathways of digital transformation. She is dedicated to bridging the gap between frontier technological research and real-world business practice, fostering synergy between academia and industry.

Dr. Jiang holds a PhD in Computer Science, Artificial Intelligence, and Digital Transformation, and has earned certificates in Digital Transformation and Data Analytics from Stanford University and Financial Analysis from the University of Oxford. Her research methodology integrates design science, empirical research, and action research. Through deep collaborations with global leading enterprises such as Microsoft, Boeing, and McKinsey, she translates theoretical innovations into verifiable business practices.

Dr. Jiang’s research spans multiple strategic industries, including enterprise digital transformation, smart aviation, net-zero construction, intelligent manufacturing, and medical AI. Her research network encompasses AI ecosystems across the United States, Europe, the United Kingdom, South Africa, India, Japan, Singapore, and China, advancing technology readiness levels (TRL) from 3 to 6. In the aviation sector, her collaboration with Boeing on AI-driven next-generation aviation transformation has provided a benchmark case for industry digitalization, garnering extensive citations and recognition from field leaders. In medical AI, her team’s development of a knowledge graph-enhanced multi-agent diagnostic framework and medical hallucination detection benchmark, presented at AAAI 2025, has made significant contributions to the reliable application of large language models in healthcare.

Dr. Jiang’s academic contributions manifest in three dimensions: theoretical construction, methodological innovation, and policy impact. At the theoretical level, her proposed “Technology-Market Fit Framework” and “AI Innovation Ecosystem Mapping Method” provide systematic analytical tools for understanding and designing AI commercialization pathways. In methodological innovation, she combines design thinking, data-driven decision-making, and systems engineering to develop AI application assessment and implementation frameworks applicable across industries and markets. In policy impact, she actively contributes to global AI governance, collaborating with the British Standards Institution (BSI), UK Parliament, European Commission, UNESCO, and the International Organization for Standardization (ISO) to co-develop and advise on AI policy documents and international standards.

Her research has been published in international journals including the Journal of Air Transport Management, Knowledge-Based Systems, and Sustainability, and she has delivered influential papers and presentations at major conferences such as the Academy of Management (AOM), the Institute for Operations Research and the Management Sciences (INFORMS), and IEEE. As the Professional Development Workshop Chair for both the Asia Academy of Management (AAM) and the Africa Academy of Management (AFAM), she is committed to advancing AI research capacity building and academic network development in emerging markets. She has also served as a delegate to the United Nations Climate Change Conferences (COP26–29), integrating AI innovation research with global sustainable development goals.

Dr. Jiang possesses unique strengths in industry–academia–research collaborative innovation. She has served as a Digital Innovation Research Scientist at Microsoft, as a Digital Consultant at McKinsey & Company, and as Co-founder of AI Business Lab, while providing consulting services to companies across diverse industries. This multifaceted identity—spanning academia, corporate R&D, strategic consulting, and entrepreneurial practice—enables her to rapidly transform cutting-edge AI research into business value and to feed practical challenges back into new research agendas, forming a virtuous cycle of “research–application–reflection–innovation.”

In teaching, Dr. Jiang has been lecturing on AI, digital transformation, computer science, and innovation and entrepreneurship at multiple universities since 2015, supervising undergraduate, master’s, and doctoral students, as well as group projects. She holds the Fellow of the Higher Education Academy (FHEA) qualification and remotely teaching for online course at Stanford University and MIT, demonstrating her professional standing in higher education. Her teaching philosophy emphasizes the integration of theory and practice, encouraging students to solve real-world problems through authentic projects while cultivating interdisciplinary thinking and innovation capabilities. Her students greatly appreciate Dr. Jiang’s courses.

Dr. Jiang’s research vision is to build an “Inclusive AI Ecosystem” that ensures AI technological development benefits different regions, industries, and enterprises of all sizes. She believes that the true value of AI lies not in the complexity of the technology itself, but in whether it can solve genuine business and social problems. Her research provides global enterprises with transformation pathways from AI technology to market value, offers policymakers empirical evidence for AI governance, and contributes methodological innovations in interdisciplinary research to academia.

Research Interests

• AI Technology Commercialization Mechanisms – Pathways, barriers, and enabling factors from laboratory to market
• AI Ecosystem Design and Optimization – Multi-stakeholder collaboration, value co-creation, network effects
• Cross-Industry Digital Transformation – Industry-specific AI application patterns, organizational change, capability building
• Data-Driven Decision Making and Strategy – Data asset valorization, data governance, privacy and compliance