As financial institutions across Hong Kong accelerate their adoption of AI, the conversation is rapidly shifting – from experimentation to execution at scale. While many organizations have already defined their AI strategies and governance frameworks, a critical question remains: is the underlying data truly ready to support AI in a controlled, scalable, and trusted way?

Regulators including the HKMA, SFC, and PCPD are placing increasing emphasis on data excellence, governance, and accountability – making data readiness not just a technical priority, but a strategy and regulatory imperative. At the same time, institutions are discovering that the biggest barriers to scaling AI are not the models themselves, but the challenges of ensuring data quality, accessibility, lineage, and governance across increasingly complex environments.

This executive roundtable brought together senior leaders from across banking and financial services to explore what “data readiness for AI” means in practice.

This discussion focused on how organizations are addressing the gap between policy and execution – embedding governance into day-to-day data and AI workflows, managing both structured and unstructured data, and enabling collaboration across business, data, and risk functions.

Participants exchanged perspectives on key challenges, shared lessons from early AI initiatives, and discussed how to prioritise high-value use cases while maintaining control and trust. The session also explored what it takes to operationalise data readiness at scale—moving from fragmented, manual processes to repeatable, governed workflows that can support enterprise-wide AI adoption.

The goal of this roundtable was to surface practical insights, common challenges, and emerging best practices—while identifying clear next steps for organisations looking to scale AI with confidence in a rapidly evolving regulatory and competitive landscape.