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DBS built AI into the bank through leadership ownership, digital and data foundations, and governance built into the workflow.
In this ADAPT Insider conversation, Sanjoy Sen, MD – Group Head of Consumer Bank at DBS Bank (Singapore), shares how that shift helped move AI from isolated use cases into broader operating change across the bank.
Key takeaways:
- Leadership ownership and organisation-wide experimentation help AI move beyond isolated use cases.
- Digital, data, and workflow foundations make AI more usable across the enterprise.
- Human accountability and embedded governance help AI earn trust as it spreads.
Leadership ownership helps AI spread across the organisation
Sanjoy’s view is that enterprise AI starts with leadership mandate and organisation-wide adoption.
A central team can support the work, but adoption grows when leaders across the business understand AI as part of their role and teams are encouraged to experiment with it directly.
At DBS, that meant creating a culture where employees could test ideas using data, see what worked, and scale successful ideas quickly.
That approach gave AI a place inside the operating rhythm of the bank rather than leaving it in a separate innovation stream.
Strong foundations make enterprise AI usable
Sanjoy is equally clear on the role of foundations.AI needs digital readiness, usable workflows, and a real-time data environment to support it.
DBS had already invested in cloud-based architecture, straight-through processing, and a stronger data platform before pushing AI more broadly across the organisation.
Those foundations made it easier to apply AI across underwriting, customer journeys, and service operations in a way that could support real decisions and real execution.
Without that groundwork, AI stays fragmented and localised.
With it, AI can move across the business with much more consistency.
Governance and accountability shape trust at scale
DBS also keeps human judgement and governance inside the design.
Sanjoy describes three principles that guide this: human in the loop for key processes, human-centred design in the workflow, and human accountability for the final decision.
That sits alongside the bank’s PURE framework, where AI use has to be purposeful, unsurprising, respectful, and explainable.
Together, those principles help keep trust close to the work itself.
AI can support the analysis, improve speed, and surface better insights, while people remain accountable for judgement, customer impact, and regulatory confidence.
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