DBS rewires operating models for AI reasoning era

At the start of this year, DBS Bank, Southeast Asia’s largest lender, claimed to have unlocked just under S$1bn in economic value from its artificial intelligence (AI) and analytics initiatives in 2025, a feat it achieved not by merely layering AI tools over existing business workflows.

Instead, it has been re-engineering its corporate structure to better harness the capabilities of AI in so-called operating model transformations (OMTs).

These enterprise-wide initiatives, according to DBS CEO Tan Su Shan in the bank’s 2025 annual report, involve rebuilding workflows for human and AI collaboration, introducing simplified organisational structures, and reskilling staff. DBS completed nine such OMT initiatives in 2025, surpassing its initial target of six.

The moves come as AI continues to advance rapidly, moving beyond simple chatbots into what Tan termed the “reasoning era”. Breakthroughs such as the introduction of OpenAI’s GPT-5 and Google’s Gemini 3 models have brought reliable deep thinking capabilities at speed as well as reduced inference costs, she noted.

“With agentic AI gaining prominence, we foresee a transition from AI as a copilot to AI operating on autopilot as we integrate agents with autonomous capabilities into workflows to unlock new possibilities for our people,” she said.

Tan pointed to DBS’s agentic applications such as CodeBuddy, a tool that combines generative AI (GenAI) and agentic AI to help DBS data professionals save up to 20% of their time on certain coding tasks. For the bank’s broader technology team, the use of GenAI has also reduced the time required for end-to-end process automation, cutting tasks like test case generation and user story documentation from months to weeks.

Meanwhile, the bank is increasingly automating its customer-facing operations. DBS Joy, the bank’s direct-to-customer corporate banking chatbot launched in July 2025, has already served over 20,000 unique corporate and SME (small and medium-sized enterprise) customers. By providing 24/7 support and accurate query resolution, the chatbot has driven a 23% increase in customer satisfaction scores.

Despite having deployed over 2,000 models and more than 430 AI use cases in 2025, DBS is not writing blank cheques to fund its AI initiatives. Tan said the bank’s approach remains grounded in financial discipline and delivering tangible business impact.

“We have consistently leveraged our data capabilities to measure returns with precision, from instituting Digital Value Capture in 2016 to embedding a performance management architecture within our horizontal customer journeys,” she said.

DBS’s digital value capture framework was co-developed with management consultancy McKinsey to measure the progress and financial impact of its digitalisation efforts.

“Our commitment to tracking business outcomes ensures that capital is allocated only to initiatives with proven traction, while allowing for course correction for those that fall short,” Tan said. “By applying the same level of rigour to our AI approach, we are well-positioned to capture the upside of AI without compromising on return on investment.”

As DBS pushes the boundaries of AI, Tan stressed the importance of AI governance, noting that the bank’s responsible AI framework provides a “firm foundation in understanding how best to deploy AI agents safely”. Recognising the potential challenges that AI may pose for its employees, DBS has also launched upskilling and reskilling initiatives to help them adapt and grow.

“We fully anticipate that the pace of AI development will continue to be breathtaking,” Tan concluded. “However, rather than being swayed by hype, we will remain grounded in our approach to be an AI-enabled bank with a heart, where our people and AI work synergistically to better serve our customers.”