As firms accelerate AI adoption, traditional data management architectures are struggling to support increasingly complex investment workflows. Many organisations still rely on fragmented processes and manual intervention, limiting scalability and slowing the delivery of AI-driven use cases.

• How to deploy AI agents to automate workflows across the data management lifecycle
• How to enhance data quality through autonomous monitoring and exception handling
• How to create scalable, self-service data platforms for investment and operations teams

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?