As India’s banking sector shifts from AI pilots to enterprise-scale transformation, the focus is now on measurable impact, intelligent automation, and AI-first operating models. Hyper-personalisation, predictive risk analytics, and sovereign AI are redefining how banks innovate, scale, and build trust. Rishi Aurora, Managing Partner, IBM Consulting India & South Asia, shares his insights on AI-led transformation, structural challenges, and the rise of intelligent, future-ready banking ecosystems in an exclusive interaction with Vishwas Sinha of Elets News Network (ENN). Edited excerpts:
India is witnessing rapid AI adoption in banking. What key industry trends do you believe will define the next phase of AI-led transformation in the sector?
India’s BFSI sector is moving decisively from AI pilots to enterprise-scale impact, aligning with a broader shift revealed in an IBM Institute of Business Value study, where 64% of organisations are redirecting AI investment toward core business functions to drive measurable growth. For Indian banks, this means accelerating AI-led transformation of credit, fraud, customer service, and risk systems to meet the needs of one of the world’s most digital, high-volume financial markets.
The next wave of disruptions in BFSI will be defined by AI-first business models – echoing IBM’s Enterprise in 2030 report, which finds that 79% of executives expect AI to significantly contribute to revenue by 2030. Hyper-personalised financial journeys, predictive risk analytics, and embedded finance are poised to become the backbone of India’s new digital banking architecture.
As adoption scales, sovereign AI is becoming increasingly critical – enabling India-aligned, high-governance models that strengthen trust, compliance, and data residency.
As banks move from pilot projects to enterprise-wide AI implementation, what structural or operational challenges should they be prepared to address?
India’s banks are now entering a strategic phase of AI-at-scale — where the real challenge isn’t building models, but removing the four structural debts that slow down returns.
First is process debt arising from old workflows that weren’t designed for AI. IBM’s APAC AI Outlook 2026 shows banks across the region redesigning these processes so AI can truly power better customer experiences and new growth models.
Second is technical debt, which shows up when core systems can’t keep up. Many banks are now modernizing their architectures and rebuilding tech stacks to embed AI across risk, service, and operations.
Third is data debt, caused by fragmented, incomplete, and inconsistent datasets— even more critical under India’s DPDP Act, which demands strict consent, purpose limitation, and responsible data processing. This challenge grows as 57% of leaders expect competitive advantage to come from model sophistication by 2030, making high-quality, compliant data a strategic necessity.
Finally and most importantly, the skills debt must be addressed. Banks need to invest in creating AI-ready, cross-functional teams capable of designing and operationalizing enterprise-grade AI.
With the rise of AI agents and autonomous systems, how do you see the operating model of banks evolving over the next few years?
India’s banks are set to move from AI-enabled to truly AI-first operating models. IBM’s Enterprise in 2030 study shows that by 2030, AI will not just support the business—it will become the business model. Competitive advantage will be shaped by sophisticated, enterprise-tuned AI agents embedded across every workflow.
Banks are already adopting agentic mesh architectures that automate complex decisions, orchestrate end-to-end workflows, and modernize legacy systems without disrupting mission-critical operations. Instead of isolated improvements, this shift is helping institutions operate with far greater intelligence and agility—strengthening risk management, accelerating product innovation, and enabling faster, more responsive customer interactions.
A defining force in this evolution will be sovereign AI. As data-residency, cultural alignment, and trust become non-negotiable, banks will increasingly rely on sovereign AI models and architectures that protect sensitive financial data while meeting emerging regulatory expectations across the region.
Through it all, people remain central. The AI-first bank will run on an augmented workforce, where humans oversee and guide thousands of specialized AI agents, focusing on judgment, empathy, and value creation while agents handle analysis, orchestration, and execution.
Can you highlight some emerging AI-driven use cases that are creating measurable impact across customer experience, risk management, or operational efficiency?
The Indian banking industry is at a tipping point – AI is moving from experimental pilots to real impact, especially across customer experience, risk, and operations. Banks are using AI to make interactions far more intuitive—leveraging virtual assistants that understand context, anticipate needs, and solve problems instantly, instead of routing customers through long, manual processes.
On the risk management side, AI is identifying fraud patterns in real time, scanning huge datasets for anomalies and streamlining KYC/Anti-Money Laundering (AML) checks that used to take days together. Plus, behind the scenes, gen AI is speeding up software development, interpreting legacy systems and automating repetitive workflows, helping teams modernize without disruption.
This is where IBM Enterprise Advantage becomes essential. It gives banks a unified, enterprise-grade AI platform built around their own data and processes – so they can deploy governed AI agents, automate end-to-end workflows confidently, and scale AI as strategic IP. With this foundation, emerging use cases don’t just work—they compound.
How are the expectations of banks from consulting and technology partners changing in the age of AI-driven transformation?
As AI becomes central to how banks operate, banks want partners who can co-innovate with them—helping rethink processes, modernize core platforms, and design AI-first workflows that align with their business logic. They also expect partners to co-implement, translating ideas into secure, enterprise-ready systems that work reliably within complex, regulated environments. Essentially, banks are now looking for a blend of strategic alignment and hands-on technical execution to transform at scale.
They also want trusted enterprise AI—AI that is governed, transparent, and aligned to their risk and compliance frameworks. More importantly, they’re asking for support in building an augmented workforce: people equipped to work with AI agents, supported by reskilling and new operating models.
A great example is Unity Small Finance Bank’s collaboration with IBM to establish a centralized API hub—cutting time-to-market for new APIs by 50% and improving issue resolution by 30%, enabling faster innovation and smarter customer experiences.
Looking ahead to 2035 and beyond, what will an AI-first bank in India truly look like in terms of customer experience, operating models, and competitive differentiation?
Imagine stepping into a world where your bank doesn’t just respond to you—it anticipates you. I believe that by 2035, an AI-first Indian bank will feel more like an intelligent financial partner than a financial institution. Thousands of decentralized AI agents—precisely the kind of operating model future enterprises are expected to rely on—will work continuously in the background, tuning products, managing risks and making real-time decisions.
Customer interactions will become fluid, intuitive and hyper-personalized. Banks will predict cash-flow pressures before they emerge, automatically optimize savings and investments, and guide major financial choices with context-aware insight—reflecting expectations that AI will significantly drive revenue and productivity by 2030.
A defining feature of this future will be sovereign AI. Models will be trained and governed within India’s data, regulatory, and cultural boundaries, ensuring trust, privacy and compliance as AI becomes more autonomous in core decision-making.
Operationally, the bank becomes a living, adaptive system—continuously learning, evolving and improving. Its competitive differentiation comes not from products, but from its proprietary intelligence: a unique blend of data, models, and agent ecosystems tuned to its purpose and its customers.
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