CEO of Southeast Asia’s Top Bank DBS: AI Adoption Already Paying Off

SINGAPORE – In an era punctuated by skepticism surrounding the tangible returns on artificial intelligence investments, DBS Group, Southeast Asia’s largest bank, is bucking the trend. Amidst widespread concerns regarding an AI bubble and reports questioning the profitability of these investments, DBS CEO Tan Su Shan asserts that her firm is already witnessing substantial benefits from its AI initiatives, with further gains on the horizon.

“It’s not hope. It’s now. It’s already happening. And it will get even better,” Tan Su Shan told CNBC on the sidelines of Singapore Fintech Week, addressing the pervasive question of AI’s real-world impact.

DBS has strategically integrated AI into its core operations for over a decade, a foresight that has equipped the bank with the robust data analytics infrastructure necessary to capitalize on the recent surge in generative and agentic AI technologies. Agentic AI, characterized by its capacity for autonomous decision-making, proactive planning, and independent task execution, is playing a crucial role in this transformation.

Tan anticipates that AI adoption will contribute an additional revenue boost exceeding 1 billion Singapore dollars (approximately $768 million) to DBS’s bottom line this year, a significant increase from the SG$750 million recorded in 2024. This projection is underpinned by the deployment of over 1,500 AI models across approximately 370 use cases throughout the organization.

“The proliferation of generative AI has been transformative for us,” Tan stated, emphasizing the “snowballing effect” of advantages derived from machine learning implementations.

Notably, DBS has been leveraging AI extensively in its financial services offerings for institutional clients. The technology facilitates the gathering and analysis of data to provide clients with more nuanced and personalized solutions. The bank is using AI to enhance risk management, streamline compliance processes, and identify emerging market opportunities, leading to more informed investment decisions for its clientele.

According to Tan, this strategic deployment of AI has fostered teams that are “faster and more resilient.” She believes that these AI-driven enhancements have contributed to a recent growth in the bank’s deposit base, outpacing its competitors. Industry analysts point to DBS’s proactive embrace of AI-driven customer service and personalized offerings as key differentiators in a competitive market.

Furthermore, DBS recently unveiled an enhanced AI-powered assistant for corporate clients, “DBS Joy,” designed to provide seamless support for unique corporate banking inquiries around the clock, thereby improving client satisfaction and operational efficiency. This chatbot leverages natural language processing and machine learning to understand complex financial scenarios and offer tailored advice, showcasing the bank’s commitment to innovative customer service.

Despite the optimistic outlook from DBS, the prevailing narrative in the broader business landscape suggests that many companies are struggling to translate AI investments into tangible financial gains. A critical missing element for many organizations is often a well-defined AI strategy that aligns with specific business objectives.

A report released by MIT in July revealed that 95% of 300 publicly disclosed AI initiatives, representing $30–$40 billion in generative AI investments, failed to deliver substantial returns. The report highlighted a common issue: the lack of clear metrics and Key Performance Indicators (KPIs) to measure the success of AI projects.

However, emerging data indicates a potential shift in the banking sector. The successful integrations at DBS and JPMorgan Chase suggest that a strategic, data-driven approach, coupled with a long-term vision, is key to realizing AI’s potential.

While DBS does not delineate generative AI spending from its overall in-house investments, other major financial institutions have provided such comparisons. JPMorgan Chase CEO Jamie Dimon revealed in a recent Bloomberg TV interview that the bank is already breaking even on its approximate $2 billion annual investment in AI adoption, emphasizing that this is “just the tip of the iceberg.” This demonstrates a growing confidence in the ability of AI to generate significant value in the financial sector.

These optimistic expectations are mirrored by DBS, which is committed to accelerating its AI development to become a truly AI-powered bank. This ambitious vision involves not only technological enhancements but also a fundamental shift in the bank’s culture and operating model.

According to Tan, the ultimate goal is to evolve generative AI into a trusted financial advisor for clients, including retail users who will interact with personalized AI agents through the DBS banking app. This vision requires substantial advancements in areas such as AI ethics, data privacy, and the development of robust algorithms that can provide reliable and unbiased financial advice.

The bank currently employs over 100 AI algorithms that analyze user data to offer personalized “nudges,” such as alerts on potential shortfalls, product recommendations, and other relevant insights. These interventions aim to improve customers’ financial literacy and promote responsible financial decision-making.

While DBS is already reaping the benefits of AI adoption, Tan acknowledged that ongoing investments in both capital and employee reskilling are essential. The bank recognizes that human capital is crucial for maximizing the potential of AI and ensuring that employees can effectively collaborate with these technologies.

The company has launched several AI reskilling initiatives across departments this year and has even implemented a generative AI-powered coaching tool to bolster these efforts. These programs are tailored to meet the specific needs of different roles and departments, ensuring that employees develop the skills necessary to thrive in an increasingly AI-driven environment.

Tan emphasizes that these initiatives are designed to automate routine tasks and enable employees to focus on building and maintaining strong human-to-human relationships with customers, rather than reducing headcount. This approach reflects a belief that AI should augment, rather than replace, human capabilities.

“We’re not freezing hiring, but it does mean reskilling. And that’s a journey. It’s a never-ending journey … a constant evolution,” Tan concluded, highlighting the importance of continuous learning and adaptation in the age of AI.