An invitation-only discussion for CIOs and Chief Architects to exchange perspectives on how to operationalise AI at scale—covering platform strategy, architecture decisions, and the integration of data, AI, and legacy systems.

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?

Many organisations struggle with inconsistent data definitions across systems, teams and use cases, creating friction between data, analytics and AI. Without a shared understanding of core business concepts, scaling trusted data usage becomes increasingly difficult. This session will explore how organisations are using semantic models to bridge the gap between data and business meaning. 

• How to align data definitions across domains using semantic models to enable consistent consumption 
• How to bridge business meaning and data to support reporting, analytics and AI use cases 
• How to scale a shared semantic layer across complex, federated environments