As organisations push to scale AI, many are finding that legacy data foundations are not fit for purpose. Siloed architectures, inconsistent standards and poor data quality are limiting the ability to operationalise AI and deliver measurable outcomes. The challenge is no longer building data platforms—but ensuring data is trusted, accessible and aligned to real business use cases. This panel will explore how leading institutions are evolving their data strategies to enable AI at scale. 

• How can you transform fragmented, siloed data into standardised, interoperable and trusted assets?
• How can you leverage metadata, lineage and governance to make data discoverable and usable at scale?
• How can you align data initiatives with clear business use cases to ensure AI delivers measurable value?

As AI becomes embedded across the enterprise, traditional operating models are being stretched to breaking point. Organisations are rethinking how teams are structured, how skills are developed, and how leadership drives transformation in an increasingly AI-driven environment. This panel will explore how leading institutions are evolving their operating models for the AI era. 

• How can you redesign roles, teams and leadership to support enterprise-wide AI adoption?
• How can you build the skills and capabilities needed to compete as an AI-native organisation?
• How can you align operating models to scale AI while maintaining control and accountability?