AI’s glittering promise—agentic assistants hinting at personalized budget tips or proactive fraud alerts—can distract us from the hard plumbing underneath. In this 20-minute session, we’ll dive into an end-to-end example: from value creation in the customer interface all the way through our lean, privacy-first pipeline and into a tiny data-mesh node. You’ll see how we stand up a domain-scoped data product, stream real-time features, and enforce consent—all without “boiling the ocean.” Along the way, we’ll touch on why banking has lagged (legacy systems, privacy constraints, fragmented data) and how our current “grow & scale” phase keeps us grounded in small, use-case builds while still thinking big. By the end, you’ll have a clear picture of how to lay the foundation now—so that when AI glitter arrives, it truly shines for customers. What You’ll Learn: 1.) How to translate a simple customer question (“How much can I spend tonight?”) into a domain-scoped data feed and real-time feature pipeline. 2.) The essential governance guardrails (consent, pseudonymization, policy-as-code) required to power an AI interface in Swiss banking. 3.) A 3-step, lean approach to evolve a single data-mesh node into a broader mesh—delivering early wins without drowning in complexity.