Aubrey Blanche

Aubrey Blanche, The Mathpath (Math Nerd + Empath), is a ethical business and technology executive with global experience specialising in developing and scaling teams and processes equitably to achieve responsible growth objectives. An expert at systems-level analysis and design and a believer in data-informed methods, with extensive experience in communications (internal, external, and crisis), HR, ESG, AI governance, and go-to-market strategy. Experienced non-profit board member, startup investor, and advisor. She is currently the Director of Ethical Advisory & Strategic Partnerships at The Ethics Centre and Founder of her eponymous equitable design consultancy. She is a master’s student in AI Ethics and Society at the Leverhulme Center for the Future of Intelligence at Cambridge University.

Organisations everywhere are racing to automate cognitive work with AI, and most of the conversation sounds like either breathless optimism or dystopian panic. Neither is particularly useful — and neither helps teams actually harness AI for meaningful transformation. The harder questions are the ones we’re not yet asking loudly enough. What happens to creativity and professional judgment when expertise gets encoded into a model? How do we build the conditions where AI genuinely amplifies human capability rather than quietly eroding it?:

Because here’s the thing: the organisations that will get the most from AI aren’t the ones moving fastest. They’re the ones thinking clearly about which cognitive work to automate, how to preserve the human judgment that makes teams exceptional, and who gets a say in shaping that future. This session gives you a more honest map of the ethical terrain — one that takes human dignity and creativity as seriously, and directly questions whether efficiency is a justifiable goal (or just one method of many to achieve more worthy outcomes). Because building powerful technology and building it well aren’t in conflict. But that only happens if we’re willing to ask the uncomfortable questions before the decisions are already made.