As banks move AI from experimentation into enterprise deployment, the data foundation becomes an execution issue. In the first installment of a three-part CDO Magazine interview series, Shebani Baweja, Group Chief Data Officer (CDO) at Standard Chartered, speaks with Bryan Lee, Group Head of Data, Innovation Group, UOB, about the conditions required to scale AI responsibly.

Baweja frames data governance as a business enabler because AI amplifies both the strengths and weaknesses of the data underneath each use case. The discussion moves from governance as risk mitigation toward a model built around clear accountability, simpler policies and standards, and governance embedded into design and development.

The conversation also identifies three data enablers for enterprise AI. Data access must overcome fragmented and legacy data estates. Data quality requires greater discipline around unstructured data. Data risk management must account for privacy and data sovereignty across multiple regulatory jurisdictions.

Across the discussion, Baweja emphasizes risk proportionality so low-risk AI use cases can move without unnecessary friction while higher-risk use cases receive oversight. That approach connects governance with responsible experimentation and gives teams a clearer operating environment for deployment.

Key themes include:

• Governance by design: Accountability and standards can be embedded into pipelines and development processes.
  • Data foundations for scale: Access, quality and risk management shape how consistently AI can be deployed.
  • Risk-proportional execution: Different AI use cases require different levels of oversight and control.
  

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