Dr Ece Kaya & Professor Bronwen Dalton
The UTS 2030 Strategic Plan positions UTS as a globally impactful public university of technology. Under its “resilient and agile” pillar, we strive to leverage advanced technologies to prepare students for the future of work and to streamline our operations.
As part of the School of Management’s commitment to UTS’ digital-first, student-centred approach, we have expanded our teaching not only to develop students’ ethical AI literacy, but to build a deep understanding of what AI means for future workplace demands. One of our approaches has been to create online teaching resources that take a deep dive into the implications of AI for work in industries across our teaching portfolio: not-for-profits, events, sport, human resources and supply chain management.
Why now?
Business schools globally have shifted decisively from debating whether to allow AI to working out how to embed it. A recent collaborative report from AACSB, GMAC, the Graduate Business Curriculum Roundtable and Inspire Higher Ed, drawing on input from nearly 50 member schools, finds that institutions are no longer treating generative AI defensively but are making it a foundational part of the curriculum. The same article points to a persistent gap on the employer side: AI tool skills grew in importance faster than any other capability in GMAC’s 2025 Corporate Recruiters Survey, yet many employers, particularly small and medium-sized businesses, remain unsure exactly what AI skills they need. Business schools, the report argues, are well placed to become the bridge between industry demand and AI capability.
Students, meanwhile, are already there. Research with recent UK management graduates found that tools like ChatGPT have simply become part of the ordinary study toolkit used to summarise, brainstorm, and get “unstuck” but that students also carry real anxieties about integrity, equitable access, and how employers will judge their AI use. The authors argue that if educators engage with these lived experiences rather than policing from the extremes, GenAI becomes an opportunity to rethink what meaningful learning looks like.
There is also a design lesson in how not to teach AI. Kitsing (2026) cautions against isolating AI in stand-alone technical courses citing CarringtonCrisp research in which only three per cent of respondents found university AI courses genuinely helpful and argues instead for integrating AI horizontally, embedding it within strategy, operations, and the disciplinary conversations students are already having.
To address all 3 signals, foundational integration, student-centred relevance, and horizontal design over silos, we created the AI in Business: Expert Interview Series.
The project: 5 industries, 1 host, real conversations
The series comprises short educational videos recorded in the studio by the UTS Learning Design and Technology Unit. Industry AI expert Sha-mayne Chan, co-founder of Friyay, interviewed UTS Management academics on what AI means for the industries they teach. Sha-mayne brings over three decades of experience across graphic design, strategic branding, and AI innovation. She now leads the development of AI-powered solutions that help small and medium-sized businesses automate processes using generative AI. That SMB perspective matters: it is precisely the segment where the AACSB report identifies the greatest uncertainty about AI adoption, and where many of our graduates will work.
Each video is anchored in a sector where our students will build their careers:
- AI in the Event Industry, featuring Dr Anja Hergesell, explores how AI is reshaping event operations from planning and logistics automation to personalised attendee experiences, predictive analytics for engagement, and real-time adaptive event management.
- AI in Not-for-Profit Organisations, featuring Dr Mark Riboldi, examines the unique opportunities and constraints of the social sector: donor relationship management, grant writing support, volunteer coordination, and impact measurement alongside the ethical imperative to balance automation with mission-driven human connection.
- AI in Sports Organisations, featuring Dr Lloyd Rothwell, looks at fan engagement and personalised experiences, performance analytics, venue and crowd management, and the competitive advantages (and integrity considerations) of data-driven decision making in sport.
- AI in HR and People Management, featuring Dr Anna Stephens and Dr Rebecca Dong, tackles one of the most consequential frontiers: talent acquisition automation, people analytics, employee sentiment analysis and the ethical challenges of bias, privacy, and human oversight that come with them.
- AI in Supply Chain Management, featuring Associate Professor Sanjoy Paul, covers demand forecasting, logistics optimisation, risk and disruption prediction, and sustainability tracking, along with the practical implementation challenges organisations face when integrating AI across supply chain partners.
Why this format works
Rather than presenting AI as an abstract technology, each conversation grounds it in the operational realities of a specific industry consistent with Kitsing’s (2026) argument that AI teaching lands when it is framed as a transformative capability inside the subjects students already care about. Sha-mayne brought sharp, practice-informed questions shaped by her experience implementing generative AI with real businesses; our academics brought deep sector knowledge and curriculum-aligned insight.
Critically, ethics is not a bolt-on. Questions of bias, privacy, human oversight, and the appropriate limits of automation surface throughout the series. This responds directly to what students themselves say they need: Ciachir’s (2026) research shows business students are enthusiastic AI users who are nonetheless uneasy about integrity and fairness and who benefit when their institutions engage those tensions openly rather than leaving them unspoken. It also reflects our commitment to developing graduates who can implement AI responsibly, not just enthusiastically (the “this tool is to help you, not replace you”) mindset that educators in the AACSB report describe cultivating.
Designed for reuse and access
The videos were produced with accessibility and flexibility in mind. Each is being embedded directly into Canvas course materials across the Management portfolio. Because the content is modular, teaching teams can drop individual episodes into the subjects where they fit best, whether that’s a sports management tutorial or a supply chain case study.
This series is one step in a broader program of work embedding AI literacy across the School of Management’s teaching. If, as the AACSB report suggests, business schools are to become the link between employers and AI capability, academic-industry conversations produced efficiently, targeted at the sectors we teach are how that link gets built.
References
Ciachir, C. (2026, March 26). Generative AI in business schools: Friend or foe? The Conversation. https://theconversation.com/generative-ai-in-business-schools-friend-or-foe-278249
Harland, N. (2026, February 11). How AI is evolving in business schools. AACSB Insights. https://www.aacsb.edu/insights/articles/2026/02/how-ai-is-evolving-in-business-schools
Kitsing, M. (2026, April 6). How business schools can turn AI from ‘threat’ to ‘sustainability enabler’. THE Campus, Times Higher Education. https://www.timeshighereducation.com/campus/how-business-schools-can-turn-ai-threat-sustainability-enabler