By Kumaravel Ramakrishnan, Director of Technology, ManageEngine
India’s BFSI sector is entering one of its most significant technology transitions. Regulatory expectations are expanding beyond cybersecurity into continuous governance, AI accountability, and operational resilience, even as institutions accelerate AI adoption while operating under increasing cost pressures.
For IT leaders, three priorities now sit at the top of the agenda. Regulatory compliance has shifted from an annual exercise to continuous evidence generation. AI has moved from pilot projects to production faster than governance frameworks have evolved. Simultaneously, IT leaders are expected to optimise costs amid growing tool sprawl, foreign exchange exposure on software subscriptions, and flat budgets.
These priorities are often managed as separate programs, each with its own roadmap, budget, and stakeholders. However, treating them independently creates unnecessary complexity. The real opportunity lies in addressing them through a single operating model built on integration, governance, and operational resilience.
Compliance as a continuous discipline
The pace of regulatory change has only reinforced the need for continuous governance and compliance. Recent directives from the Reserve Bank of India around IT governance, cybersecurity, and oversight, coupled with evolving expectations for AI governance and scale-based regulation for non-banking financial companies, are raising the bar for accountability. Compliance is no longer about producing documentation during audits. Institutions increasingly need to demonstrate continuous control, traceability, and governance as part of day-to-day operations.
Alongside this, a new operational challenge is emerging. As employees adopt generative AI tools in their daily work, organisations must manage risks around sensitive customer information, data residency, and model governance. Shadow AI is quickly becoming as important a governance issue as shadow IT once was. Without clear policies, visibility, and controls, innovation can inadvertently introduce new compliance and security risks.
From AI-ready to AI-driven
The next phase of transformation will see institutions evolve from being AI-ready to becoming AI-driven. Many organisations have already deployed AI across fraud detection, customer service, credit assessment, and risk management. The next step is enabling AI to support autonomous IT operations while remaining transparent, auditable, and governed. That shift will only be possible if institutions establish a reliable operational foundation capable of supporting AI at scale.
Three strategic plays for IT leaders
The first priority is to simplify and integrate the IT operating environment. Fragmented security, identity, endpoint management, observability, and service management platforms often create operational silos that increase costs and complicate compliance. A converged operating layer enables teams to apply common controls, automate evidence collection, and improve visibility across the technology estate. Instead of assembling compliance information retrospectively, organisations can generate it continuously as part of normal operations.
The second priority is to establish a governed AI strategy. As AI becomes embedded in enterprise workflows, institutions need greater control over where data resides, how models are used, and how AI-driven actions are monitored. Whether organisations choose proprietary or frontier LLMs, governance, auditability, and clearly defined operational boundaries will be critical for maintaining trust while enabling innovation.
The third priority is to build for local operational realities. Data residency, regulatory expectations, commercial predictability, and timely support all influence long-term technology decisions. Infrastructure choices, commercial models, and support capabilities should align with local compliance requirements while giving institutions the flexibility to scale securely and cost-effectively.
A single foundation for the future
Ultimately, compliance, AI adoption, and cost optimisation are not separate technology initiatives. They are outcomes of the same architectural decisions. Organisations with integrated operating environments are better positioned to automate governance, reduce operational overheads, and deploy AI responsibly without adding unnecessary complexity.
As AI capabilities continue to mature, success will depend less on adopting the latest technology and more on building the operational discipline to govern it. The institutions that lead the next phase of digital transformation will be those that treat compliance, resilience, and intelligent automation as interconnected capabilities rather than competing priorities.
For IT leaders, the question is no longer whether AI-driven operations are possible. It is whether their operating model is ready to support them securely, responsibly, and at scale.