The potential after effects of the war in the middle east may force companies to revive the operating model adopted during the Covid-19 outbreak.
COVID-era practices have not only become mainstream but are now being re-engineered through an AI-first approach. As a result, companies are prioritising legacy modernisation over incremental upgrades. FE FUTECH speaks with Pratik Shah, National Leader – Financial Services, EY India.
IT and digital played a major role during the COVID-19 outbreak. Any practices from the Covid times that you see coming back?
IT and digital practices introduced during the COVID period have transitioned from emergency measures to core operating capabilities across banks, insurers, and NBFCs, and are now being amplified by the rapid adoption of agentic AI.
The key shift is that institutions are re-architecting these COVID-era practices through an AI-first lens. Rather than pursuing incremental fixes, institutions are accelerating legacy modernisation to build nimble, AI-ready systems and strengthen data quality, ensuring enterprise data is clean, governed, and usable for AI at scale.
This is enabling a shift toward AI-infused, end-to-end processes, where AI supports exception management, improves servicing responsiveness, and strengthens fraud and risk controls.
In parallel, customer engagement models are evolving toward conversational and multimodal interfaces, alongside existing tile-based experiences, reflecting the shift in user expectations for more intuitive, real-time interactions.
Over time, the focus will be on materially reducing manual operations and building more automated workflows across the organisation. Together, these shifts reflect how COVID-era digital capabilities are forming the foundation for AI-led operating models in financial services.
What kind of CEO–Chief Information Officer conversations do you see already happening and foresee in the immediate future?
CEO–CIO conversations are being reshaped by the rapid rise of agentic AI, increasingly viewed as a COVID-scale inflection point. Leaders are prioritising the building blocks for enterprise-wide AI, especially data availability, quality, and governance, alongside operating design that ensures critical services continue during stress events.
Together, these forces are accelerating legacy modernisation into nimble, AI-ready systems and driving a shift toward an AI-first approach in reimagining end-to-end journeys across onboarding, underwriting, servicing, and cross-departmental operations, with resilience embedded from the outset.
In the financial services space, where does India stand in the artificial intelligence adoption cycle—based on the budgets financial services companies are allocating for AI?
India’s financial services sector is moving beyond AI pilots toward broader enterprise adoption, with budgets increasingly prioritising the scaling of proven use cases and the operating foundations required to deploy AI at scale.
The pivot is visible at the top: CEOs and boards are treating AI as a core business lever linked to measurable outcomes such as productivity uplift, and improved customer experience. As a result, the budgets are steadily shifting toward the practical enablers of scale—quality of data, clearer governance and operating model changes that make AI usable across functions, not just within digital teams.
The sector also benefits from strong structural tailwinds. Public digital rails and rich transaction data make AI applications commercially viable across areas such as onboarding, fraud detection, underwriting, customer servicing and collections.
The sector also benefits from strong structural tailwinds. Public digital infrastructure and rich transaction data make AI applications commercially viable across areas such as onboarding, fraud detection, underwriting, customer servicing, and collections.
The next phase will be shaped less by AI spend and more by the ability to deploy AI consistently and reliably at scale across the enterprise. Financial institutions will focus on embedding AI into day-to-day workflows rather than running isolated pilots. This will require stronger execution: improving data quality, implementing robust AI governance aligned with emerging RBI expectations, and ensuring the right mix of capabilities to deploy and manage AI safely in production.
Where do you think India stands in terms of the availability of AI talent? What are the strengths and weaknesses?
India is widely seen as having one of the largest and fastest-growing pools of AI talent globally. The country is structurally well positioned, with a strong pipeline emerging from AI startups, STEM graduates, and digital-native ecosystems, supported by government initiatives such as IndiaAI and SARVAM.
From a numbers standpoint, talent availability is unlikely to be the primary constraint. The bigger challenge is how quickly organisations can mainstream AI skills across the existing workforce.
For most banks, insurers, and NBFCs, success will depend less on hiring a specialist layer and more on reskilling and upskilling current employees, particularly those who are not yet functional users of AI in their day-to-day roles. This includes building practical AI capability within business teams and putting in place structured change management so that AI becomes part of daily decision-making and operations.
Large-scale reskilling and disciplined execution will determine how effectively India converts its AI talent advantage into tangible business impact.
How does the tension between legacy infrastructure and digital ambitions shape CIO/CDO priorities?
The coexistence of legacy infrastructure and digital ambition is increasingly being reshaped by an AI-first mandate. Core banking, policy administration, and loan platforms were designed for reliability, but enterprise-wide AI—especially agentic AI—demands greater speed and AI-ready data.
CIO priorities are shifting from incremental upgrades toward modernising legacy systems into nimble, AI-ready platforms—unlocking data, improving data quality, and adopting API-first integration, while maintaining resilience, security, and regulatory compliance.
CDOs, in parallel, are driving AI-infused, end-to-end journeys rather than just digital overlays—embedding AI into onboarding, underwriting, servicing, and collections, and redesigning experiences toward conversational and multimodal interfaces, alongside existing tile-based systems.
Institutions that move fastest will be those where CIO and CDO agendas converge on shared outcomes: enterprise-wide AI deployment, strong governance, clearly defined journeys, and operational resilience.
What differentiates operational IT leaders from strategic technology leaders?
Technology leaders focused on managing IT tend to concentrate on system stability, cost control, and operational efficiency.
In contrast, leaders driving strategic transformation are using technology—particularly through an AI-first approach—as a business lever to reshape operating models, customer experience, and productivity.
This shift is characterised by treating data as a strategic asset, with a stronger emphasis on data quality and governance, while prioritising the development of AI-ready systems that support continuous innovation and real-time decision-making.
AI is being embedded across the full customer journey, from onboarding through servicing, while experiences are redesigned to be more conversational, particularly for Gen Z users.
At the same time, strategic leaders are investing in reskilling to ensure that AI and other advanced technologies are effectively used across the organisation.
