According to CNBC, DBS CEO Tan Su Shan says her bank is already generating massive returns from artificial intelligence investments, declaring “It’s not hope. It’s now.” The bank expects AI adoption to bring in over SG$1 billion (about $768 million) in revenue this year, up from SG$750 million in 2024. This assessment comes from about 370 AI use cases powered by more than 1,500 models throughout DBS’s operations. Tan credits the bank’s decade-long AI preparation and data analytics foundation for enabling rapid adoption of generative and agentic AI. She described experiencing a “snowballing effect” of benefits, with AI already contributing to faster teams and improved deposit growth compared to competitors.
The AI reality check we needed
Here’s the thing: while everyone’s been talking about AI potentially being overhyped, DBS is actually showing us what real implementation looks like. They’ve been quietly building this foundation for over ten years. That’s the key difference between companies just throwing money at AI versus those actually getting returns. You can’t just flip a switch and expect magic to happen – you need the data infrastructure, the models, and the use cases all working together.
And the numbers don’t lie. SG$1 billion in additional revenue isn’t pocket change, even for a bank of DBS’s size. That’s real money flowing from real applications. It makes you wonder how many other companies claiming AI success are actually seeing this level of measurable impact versus just hoping for future returns.
Where the AI money actually comes from
DBS is focusing AI on areas that directly drive business value. Their institutional client services use AI to collect and leverage client data for personalized offerings. This isn’t about flashy chatbots – it’s about understanding clients better and serving them more effectively. The result? Faster teams and better deposit growth than competitors.
They’ve also rolled out DBS Joy, an AI-powered assistant for corporate clients that handles unique banking queries 24/7. This is where the snowball effect Tan mentioned really kicks in – once you have the infrastructure, adding new applications becomes progressively easier and cheaper.
What this means for everyone else
Basically, DBS is showing that AI success requires both patience and strategic focus. They didn’t jump on the generative AI bandwagon last year – they’ve been building toward this moment. For other enterprises watching this, the lesson is clear: stop treating AI as a magic bullet and start treating it as a long-term capability investment.
The companies winning with AI right now are the ones who started years ago. They had their data houses in order before the generative AI explosion. Everyone else is playing catch-up, and it shows in the results – or lack thereof. DBS’s experience suggests that when done right, AI isn’t just cost savings anymore – it’s becoming a genuine revenue driver.
