Data foundations, domain-based AI, and the coming banking divide with McKinsey's Stephen Reddin

In this episode of Banking Reinvented, recorded live at ENGAGE Americas in Nashville, host Tim Rutten sits down with Stephen Reddin, Partner at McKinsey & Company and leader in McKinsey Digital's data and AI practice for the US and Canada.

Stephen brings ten years of experience at the intersection of data transformation and AI strategy, and a builder's instinct from founding a telecom startup before consulting.

The conversation covers why banks are trapped between two urgent mandates: fix your data foundations and show AI results now. Stephen makes the case that the banks getting real value from AI are picking a domain, reimagining the process end to end, and deliberately capturing the efficiency rather than redistributing it.

Stephen also gets into the LLM dependency question: Are banks ceding competitive ground by building on top of models they do not own? He argues that the competitive advantage comes from the proprietary data and business context that sits on top of it - not the models.

The episode closes with a question about what comes next: AI-native bank startups, the divide between tier-one institutions and regional banks, and why Stephen is more optimistic about smaller institutions than most expect.

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