Why Indian banks need AI-native cores — not just stacked-up digital projects

Indian banks face a pressing need to move beyond piecemeal digital projects and focus on measurable outcomes as legacy systems, siloed customer experiences and talent shortages slow their transformation. While AI adoption remains largely in early-stage use cases such as chatbots, fraud detection and credit scoring, experts highlight the urgent need for banks to move toward “agentic” AI autonomous systems capable of decision-making and embed intelligence across end-to-end workflows.

In an interview with Firstpost, Nishant Singh, founder and global CEO of BUSINESSNEXT, a leading composable enterprise platform provider for banks, insurers and financial institutions outlined how public and private sector lenders are managing the shift from digital “projects” to outcome-focused strategies, why AI “war rooms” are emerging in boardrooms, and how the next five years will be defined by data monetisation, automation and building AI-native cores.

What are the primary operational and strategic challenges currently confronting Indian banks?

Mr. Singh: Indian banks face a complex set of challenges that are slowing their ability to innovate and adapt. Legacy technology systems continue to be a major hurdle, reducing agility and making it harder to deploy new products quickly. Customer experiences often remain fragmented due to a lack of true omnichannel integration, resulting in inconsistent service across physical and digital touchpoints. On top of this, regulatory compliance demands from data security requirements to lending norms are consuming a significant portion of technology teams’ time and resources. The shortage of skilled digital talent within banks is another barrier, hampering the speed of transformation. Finally, many institutions are struggling to shift from running isolated digital “projects” to achieving measurable digital “outcomes” that impact revenue, customer loyalty and efficiency.

With AI gaining traction globally, how mature is its adoption in Indian banking and do institutions have fully developed autonomous AI strategies?

Mr. Singh: Artificial intelligence is increasingly present in Indian banks, but adoption is still concentrated in early-stage use cases such as chatbots, fraud detection systems and credit scoring models. More advanced applications — particularly “agentic” AI, where systems can act autonomously and make decisions, are mostly limited to pilot projects and proof-of-concept trials. Very few banks have yet developed a comprehensive AI strategy that is directly tied to revenue growth or operational efficiency goals. However, CIOs and CXOs are now recognising the need to embed AI into end-to-end workflows, rather than treating it as a set of disconnected features.

Given that AI adoption in banking is still in early stages, how can institutions innovate without losing customer trust?

Mr. Singh: While AI holds transformative potential, it’s crucial for banks to approach its integration with caution. The Reserve Bank of India has recommended a “tolerant supervisory stance” towards first-time AI errors, provided institutions have robust safety measures in place. This approach encourages innovation without compromising oversight. Also, the RBI’s comprehensive framework for AI in the financial sector emphasises risk management and the establishment of a multi-stakeholder committee to monitor AI’s adoption, ensuring that trust and reliability are maintained as banks innovate.

Can AI assist elderly or disabled customers in banking, especially when they cannot provide physical signatures?

Mr. Singh: Absolutely. AI can play a pivotal role in enhancing accessibility for elderly and disabled customers. For instance, AI-powered systems can identify specific disabilities and customize banking services accordingly, guiding users through product selections and explaining terms in an understandable manner. Moreover, AI can facilitate secure digital authentication methods such as biometric verification or voice recognition, enabling these individuals to manage their banking needs remotely and securely even when traditional signatures are not feasible.

How should boardrooms interpret and approach ‘AI war strategies’ in the context of banking competition and market disruption?

Mr Singh: In banking boardrooms, “AI war strategies” refer to preparing for an era where AI disruption could reshape customer relationships, pricing models and margins. Boards are actively debating whether to build in-house AI teams or partner with established technology platforms to accelerate adoption. There is also growing concern over “AI-native” fintech players that could bypass traditional banks entirely and capture significant parts of the value chain. Ultimately, these strategies are about improving “time-to-intelligence”, enabling banks to make smarter, faster and more scalable decisions than their competitors.

In terms of digital transformation, how do public sector banks compare with private banks in India?

Mr Singh: Public sector banks (PSUs) benefit from their vast scale and nationwide reach, but they often face slower progress due to lengthy procurement processes, a cautious approach to risk and entrenched internal silos. Private banks, on the other hand, tend to be more agile, they experiment earlier, invest faster and adapt quickly to new trends. However, they may lack the extensive rural and semi-urban penetration that PSUs offer. Interestingly, some PSU banks are now leapfrogging traditional digital transformation paths by directly implementing AI-driven platforms instead of gradually upgrading legacy systems.

To what extent is technology driving measurable business outcomes in the banking sector today?

Mr Singh: Technology has moved far beyond its traditional role as a back-office utility — it is now a direct enabler of revenue and customer satisfaction. In practical terms, banks that have successfully deployed intelligent automation have seen up to a 40% improvement in lead conversion and 20–25% cost reductions in operational processes. However, these benefits only materialise when technology is implemented with an “outcome-first” mindset. For instance, banks are seeing 25–35% improvements in frontline conversion rates with real-time AI assistance for field teams, which now enables them to cross-sell multiple products during every customer interaction rather than being limited to just one offering. AI supports relationship managers (RMs) with daily planners that identify high-priority customers and suggest timely, personalised conversations based on customer events or behaviours. As a result, an RM who used to handle a hundred customers can now effectively manage a portfolio of a thousand, enhancing both

On the operations side, banks are rapidly automating loan processing, credit approvals and compliance, progressing from 50-60% to as much as 80-90% of cases receiving instant, automated decisions.

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How does India’s regulatory framework for digital banking and fintech innovation compare with international standards?

Mr Singh: India’s banking regulation is often considered progressive in its approach to technology. Initiatives like the Reserve Bank of India’s regulatory sandbox, digital lending guidelines, and the account aggregator framework have provided clear pathways for innovation. In some respects, the pace of fintech enablement here is faster than in many Western markets. That said, there is still room for improvement in areas such as uniformity in digital KYC norms and clarity in execution across different financial institutions.

Looking ahead, what should be the top strategic priorities for banks over the next five years?

Mr Singh: Over the next half-decade, banks are expected to focus on three major priorities. First, building an “AI-native core” — a digital foundation centred around data-driven decision-making rather than transactional processing. Second, radically reducing the cost-to-serve through automation, allowing banks to serve more customers at a lower operational expense. Third, unlocking new value from data monetisation by turning transactional insights into targeted cross-selling opportunities, innovative credit offerings, and ecosystem partnerships.

From your perspective, how do India’s major banks strike a balance between digital innovation and traditional branch operations?

Mr Singh: Large institutions such as HDFC Bank which we’ve worked with for more than 15 years, have focused on offering a consistent customer experience across digital channels, physical branches and contact centres. They’ve managed to scale while keeping both technology-driven and in-branch services relevant. Similarly, our five-year engagement with SBI shows how the bank has been transforming customer service and sales operations—adapting more quickly to change while continuing to serve a very large customer base. What stands out across these players is that technology is treated as a long-term investment rather than just an operational cost.

Smaller banks, such as AU Small Finance Bank are taking a different approach. Their emphasis has been on extending modern banking services into Tier-2 and Tier-3 cities, showing how innovation can be tailored to reach newer customer segments outside the big urban centres.