In this episode of The Lean AI Podcast, host Ben Hafele is joined by Siddarth Anand from Royal Bank of Canada. Together, they explore the critical gap between business leaders and technologists in AI initiatives, the importance of starting with business problems rather than technology, and how to build cross-functional teams that deliver real value through strategic prioritization and execution.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Philip Chew, a veteran technologist with experience building platforms at financial companies since the 1990s. Together, they explore Philip's unique approach to leading AI transformation in large corporations, including his portfolio management methodology, the importance of breaking down professional silos, and why this technological shift represents a fundamental paradigm change from deterministic to probabilistic programming.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Adam Newton, AI transformation leader with experience at EON, Danone, and Amazon. Together, they explore the critical difference between problem-focused and technology-focused AI initiatives, the importance of strategic speed over operational speed, and practical frameworks for securing funding and scaling AI projects.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Kunal Sawarkar, Chief Data Scientist and Distinguished Engineer at IBM. Together, they discuss practical strategies for embedding AI into products successfully, including Kunal's "burning star syndrome" approach to AI investment, the importance of customer-focused problem solving, and building the right talent composition for AI teams.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Supreet Kaur, AI professional at Microsoft. Together, they discuss strategic approaches for securing leadership buy-in for AI initiatives, the importance of aligning AI with business problems, and how to effectively communicate AI progress to maintain momentum and support from executives.

In the first episode in Season 2 of The Lean AI Podcast, host Ben Hafele is joined by Chris Reitz, a seasoned product management professional and AI educator. Together, they discuss the importance of cultivating AI use cases through problem-focused approaches, avoiding "magical thinking" in AI initiatives, and making strategic small bets rather than overinvesting in uncertain outcomes.

In the dynamic season #1 finale of The Lean AI Podcast, host Ben Hafele is joined by Eric Ries—bestselling author of The Lean Startup and The Startup Way, and creator of the transformative Lean Startup methodology. Known for redefining how companies innovate, Eric shares actionable strategies to develop AI-powered solutions that drive real impact. This conversation explores critical topics like testing key assumptions, implementing metered funding, and shifting away from traditional corporate funding models—all through the lens of Lean principles. If you’re looking to harness AI innovation with proven frameworks, this episode is a must-listen!

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Mike Hollinger, Distinguished Engineer at IBM and President of the Austin AI Alliance. Together, they explore the critical importance of understanding user intent, leveraging organizational data effectively, and avoiding common pitfalls in AI implementation.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Chris Withers, a seasoned leader known for his transformative roles in data and AI at BT Group, EY, and IBM. Together, they discuss deploying AI tools to enhance productivity and spearhead initiatives, the challenges of collecting "ground truth" data, and the value of testing assumptions early.

In this episode of The Lean AI Podcast, host Ben Hafele is joined by Christina Mongan, Global Director of AI Practice at Unisys. Together, they discuss driving successful AI transformation in large organizations. Christina shares insights on prioritizing business outcomes, creating a human-centered approach to AI, and avoiding common pitfalls in AI adoption.