Active Industrial Learning: What We've Learned—and What We'd Like to Learn From You

This month’s episode of the Harvard Data Science Review Podcast takes listeners behind the scenes of Active Industrial Learning, HDSR’s column exploring how data science and AI are applied in real organizations. We speak with column co-editors Hamit Hamutcu and Miguel Paredes about the challenges of translating data science theory into practical business impact.

Drawing on their experiences working with industry leaders, they discuss data and AI literacy, responsible AI, organizational transformation, and the critical role of leadership in successful AI initiatives. The conversation also explores the future of enterprise AI, the value of cross-disciplinary collaboration, and how Active Industrial Learning is evolving to showcase lessons from business, education, the arts, and beyond.

Whether you're leading AI initiatives, building data-driven organizations, or simply interested in how AI succeeds in practice, this episode offers valuable insights into the people, processes, and perspectives shaping the future of applied data science.

Our guests:

• Hamit Hamutcu is the founder of AIxEd, an AI in education event and ecosystem initiative; a co-founder of Elements, a data skills assessment platform; and a former senior advisor at the Institute for Experiential AI at Northeastern University.  He is also co-editor of HDSR’s Active Industrial Learning column.
• Miguel Paredes is a senior AI executive, adviser, and consultant, a venture partner at Silicon Foundry, an AI Executive Fellow at Harvard Business School, and a fellow/adviser for the AI Fund and Milemark Capital, two AI-focused venture capitals. He is also co-editor of HDSR’s Active Industrial Learning column.