AI ambitions are outpacing the data foundations that are supposed to support them. For many enterprises, the result is a familiar pattern: fragmented data assets, rising infrastructure complexity, compliance exposure, and AI initiatives that struggle to move from pilot to production. The missing piece is rarely the model — it's the operating model.

This session makes the case for a unified, sovereignty-first approach to AI data infrastructure as a strategic enabler — not just an IT decision. Drawing on the capabilities of a new class of object-native lakehouse architecture, we will explore how Chief AI Officers and AI program leaders can build a data foundation that is regulatory-ready by design, vendor-agnostic, and directly tied to measurable business outcomes.

Attendees will leave with a clear framework for:

• Aligning AI data strategy with regulatory requirements, including GDPR, the EU AI Act, and emerging data residency mandates — without sacrificing analytical performance
• Reducing AI program risk by eliminating fragile, multi-vendor data pipelines that create audit gaps and slow down governance
• Accelerating time-to-value for AI use cases by consolidating structured and unstructured data into a single, high-performance layer that your AI tools can access immediately
• Making the business case for on-premises AI infrastructure — how full data sovereignty and hyperscaler independence translate into cost predictability, competitive advantage, and board-level confidence

Whether you are setting the strategy for your organisation's first enterprise AI program or looking to scale what you have already built, this session will give you a practical, outcome-oriented lens for evaluating your data infrastructure choices — and the language to bring your stakeholders with you.