Industry leaders in India’s BFSI sector warn that outdated technology platforms risk obsolescence as AI-driven innovations accelerate, prompting a strategic rethink focused on agility and customer-centric AI deployment.
Indian banks and financial services firms are being pushed to rethink a familiar pattern: spend years building a major technology platform, only to find that the market has moved on before it is fully in place. At the ETBFSI CXO Conclave 2026 in Mumbai, industry leaders warned that long implementation cycles could leave institutions exposed to faster, AI-native rivals with more adaptable systems and lower operating drag.
Deepak Bhatia, head of BFSI business at Thoughtworks India, said the pace of change in artificial intelligence is forcing firms to question whether current investment models still make sense. The concern is not simply whether a new application uses AI, but whether the architecture, data design and operating model behind it can support a business built around machine-led decision-making. Research on India’s BFSI sector suggests the shift is already under way: a study of 128 institutions found 69% had implemented AI or machine-learning tools, with reported reductions in operating costs and faster processing times, even as privacy, skills and compliance challenges remained.
That warning is increasingly tied to culture as much as technology. Anand Bhatia, chief data and analytics officer at HDB Financial Services, said AI adoption requires organisations to change how people work, how authority is shared and how accountability is maintained when humans and machines operate together. His comments reflect a broader pattern seen across the sector, where AI is moving beyond pilots into enterprise deployment, but where data fragmentation, regulatory limits and staff resistance still slow wider adoption. A Deloitte analysis of financial services in 2026 similarly argues that older systems were not built for continuous data flows or real-time execution, making it harder for incumbents to keep pace with AI-native products.
The panel also drew a clear line between tasks that suit automation and those that do not. Ravi Kethana, chief platform officer at CAMS, argued that AI should be used where it adds value, not treated as a universal solution. He favoured a two-track approach in which teams can experiment while production systems stay tightly controlled. That caution is echoed in industry commentary on AI transformation, which says many programmes fail not because the technology is weak, but because ownership is unclear, operating models are rigid and organisations do not manage the transition well enough.
Boards are also demanding evidence of returns before AI programmes are scaled. Anand Bhatia said firms are increasingly expected to define metrics, measurement methods and expected business impact in advance, with payback periods varying depending on the use case. Kethana said AI spending should support revenue growth, better customer experience and wider reach, while staying within risk and regulatory limits. The challenge, the panellists said, is that data architectures built for dashboards and reports may not work for autonomous software agents, which need cleaner structure and a semantic layer that allows machines to interpret information consistently.
For the sector, the strategic stakes go well beyond efficiency. Voice-based interfaces and other AI-driven tools could make financial services easier to use for customers who struggle with text-heavy digital journeys, including those in less connected parts of India. But the bigger risk is competitive: if a new entrant can redesign a process or customer journey far more quickly than an incumbent can adjust its legacy stack, the old advantage of scale may no longer be enough.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
