How would you describe BoodleBox and the problem it sets out to solve? “BoodleBox is a collaborative AI platform built in partnership with higher education. We give every student, lecturer and employee secure access to more than 15 leading AI models – ChatGPT, Claude, Gemini, Mistral and others – in one environment designed for genuine collaboration between humans and AI. The problem we set out to solve was the chaos and inequity that arrived within institutions alongside generative AI. While some students could afford premium subscriptions, most could not. Faculty had no visibility into how AI was being used. Institutions were exposed on privacy, compliance and intellectual property. We built BoodleBox to bring order, equity and responsibility to that landscape. We also wanted to prepare students for a workplace that will not be one person working with one chatbot, but teams of humans and multiple AI systems collaborating on complex problems.” You have a background in law, public service and entrepreneurship. What drew you to build AI infrastructure for education? “Service has been the through-line of my career – to institutions, students and the educators shaping the next generation of leaders. Across more than two decades in law, public service, technology and BoodleBox founder and CEO France Hoang outlines the importance of “AI fluency” and explains why multi-model AI, faculty trust and equitable access can reshape how business schools prepare tomorrow’s leaders Producing AI-fluent graduates
Faculty resistance to generative AI remains one of the most-cited barriers to adoption. How can institutions address this? “Resistance is rarely about the technology itself. It is about pace, autonomy and trust. Faculty are being asked to redesign decades of pedagogy, often without adequate time, training or institutional clarity. The schools getting this right are doing three things simultaneously: building trust through professional development that respects faculty expertise; giving educators safe environments to experiment with AI in low-stakes ways; and providing clear, transparent policies so that no one is left guessing where the institution stands. Bottom-up enthusiasm and top-down clarity have to meet in the middle.” Privacy, data sovereignty and a growing patchwork of regulation are reshaping how institutions procure AI infrastructure. What should higher education leaders be looking for in an AI partner? “Contractual clarity should come first. Specifically, every model provider should be bound by terms that prohibit training on institutional data as many enterprise AI products are not as airtight as their marketing suggests. Second, architectural privacy: prompts should be anonymised before they reach any model and institutional knowledge should be
teaching, the common thread has been helping institutions navigate transformative change responsibly. When generative AI began arriving within institutions without guardrails, I recognised a familiar pattern: enormous promise paired with serious risk. I wanted to build infrastructure that lets institutions adopt AI in a way that elevates human judgement rather than displacing it and that prepares graduates rather than merely impressing them.” There is a temptation among institutions to use a single AI provider for simplicity. What is the case for the multi-model approach taken by BoodleBox? “No single AI model is best at everything and the leaders change every few months. Forcing every student into one provider is like handing every driver the same vehicle for every journey. By placing several models side by side, our approach also has a literacy dimension, as it enables students to see how outputs differ between providers. Comparing them builds critical thinking, reduces hallucinations and demystifies the technology rather than putting it on a pedestal. While a standardised AI environment may be convenient, it teaches students that AI is a single oracle when the reality will be far more complex in their subsequent working lives.”
30 Business Impact • ISSUE 3 • 2026
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