Financial services today are no longer evolving in phases; they are being reshaped in real time. As AI matures, regulations intensify, and technology cycles compress, the industry is moving beyond traditional transformation agendas toward a more fundamental shift – rethinking how platforms are designed to anticipate, absorb, and adapt to constant change.
The focus is no longer on replacing legacy systems, but on orchestrating them alongside next-generation capabilities to unlock data, embed AI, and enable continuous innovation. Ranjeetha Raja, Head of Product – Enterprise Platforms, Broadridge India, shares her insights on designing for disruption, approaching modernisation, integrating AI meaningfully, and building platform-led enterprises for the future in an exclusive interaction with Vishwas Sinha of Elets News Network (ENN). Edited excerpts:
In an era of rapid innovation and evolving paradigms such as agentic workflows, how should organisations design platforms that remain resilient, scalable, and adaptable to continuous disruption?
In today’s environment of rapid innovation and emerging agentic workflows, organisations must design platforms not for stability, but for continuous change. This starts with modular, API-driven architectures that enable composability and allow capabilities to evolve independently without disrupting the whole system. Data must be treated as the core asset, governed, real-time, and unified, so both humans and AI agents can operate on trusted insights. Equally important is designing for human–agent collaboration, with built-in orchestration, guardrails, and auditability to ensure control and transparency. Resilience also comes from abstraction and optionality, avoiding vendor lock-in and enabling flexibility across cloud and technology choices. Finally, adaptability must be embedded in the operating model through product-centric teams, continuous delivery, and a culture of experimentation. Ultimately, the most successful platforms will not be the most complex, but the most adaptive, capable of evolving alongside shifting business needs, regulatory demands, and technological disruption.
As enterprises rethink legacy systems, what is the right approach to modernisation while balancing mainframes, cloud adoption, real-time processing, and the need for scalability, security, and incremental innovation?
Organisations are often faced with questions around whether modernisation means moving completely away from mainframes, fully adopting the cloud, or replacing batch processing entirely with real-time systems. The answer is not an absolute shift in any one direction – it requires a thoughtful and balanced approach.
As platforms are designed, scale remains a fundamental consideration, especially given the high transaction volumes involved. At the same time, security is critical due to the sensitive nature of operations, including money markets, investor relationships, communications, and other confidential business processes.
The approach to modernisation, therefore, lies in bringing together the strengths of both legacy and modern systems. Existing core engines continue to provide robustness and handle large-scale volumes effectively, while newer capabilities are introduced to unlock data from legacy environments and make it accessible for more contemporary use cases such as data, AI, and tokenisation.
Finally, modernisation should be incremental and outcome-driven, aligned to business priorities. This enables organisations to innovate continuously, reduce risk, and maximise return on existing investments while steadily transitioning toward a more flexible, future-ready architecture.
With AI now widely adopted, how should organisations transition from early experimentation to achieving measurable ROI, robust governance, and seamless integration with existing systems and workforce models?
As AI adoption matures, organisations must shift from experimentation to disciplined, value-driven execution. This starts by prioritising use cases with clear business impact and embedding AI directly into core workflows rather than treating it as a standalone capability. With the rise of agentic AI, the focus should be on orchestrating end-to-end processes where intelligent agents can act, adapt, and collaborate with humans, not just generate insights. This makes human–machine collaboration central – designing systems where humans provide oversight, handle exceptions, and continuously train and refine AI outputs. Achieving measurable ROI requires robust data foundations, clear KPIs, and tight alignment between technology and business outcomes. At the same time, governance must be built in from the ground up, including model transparency, auditability, bias controls, and regulatory compliance, especially in highly regulated industries. Integration is equally critical—AI must seamlessly connect with existing enterprise systems, data platforms, and operating models to avoid fragmentation. Ultimately, success comes from treating AI not as a tool, but as a scalable capability embedded across the enterprise, enabling continuous learning, operational efficiency, and smarter decision-making at scale.
What is the core value of a platform-driven strategy in today’s complex financial ecosystem, and how does it enable organisations to deliver agility, scalability, and innovation at speed?
The core value of a platform-driven strategy in today’s complex financial ecosystem is its ability to unify fragmented capabilities into a cohesive, scalable, and innovation-ready foundation. Rather than operating across siloed systems, organisations can leverage a platform with a unified data model that ensures consistency, transparency, and real-time access to trusted information across the enterprise. Standardised APIs enable seamless integration with internal systems and external partners, accelerating ecosystem connectivity and reducing time to market. This approach also enables our capabilities to be AI-native by design, embedding intelligence into workflows to enable automation, insights, and emerging agentic capabilities. A unified user experience further simplifies how clients and employees interact with the platform, improving usability and productivity. Critically, this architecture also streamlines client onboarding-standard interfaces, pre-integrated services, and consistent data models, reducing implementation complexity, enabling clients to realise value faster with lower operational friction. This combination allows firms to rapidly scale capabilities, respond to change, and continuously innovate without rearchitecting core systems. Ultimately, a platform-driven strategy transforms technology into a strategic enabler – delivering agility, scalability, and speed while simplifying how clients access and benefit from the platform.
How will technology enable organisations and individuals to think bigger and achieve more in the future?
Technology is increasingly shifting from a tool that supports work to a force that expands what organisations and individuals believe is possible. With advances in AI, particularly agentic systems, technology can autonomously execute complex tasks, augment decision-making, and continuously learn – freeing humans to focus on higher-value, strategic thinking. This evolution enables true human–machine collaboration, where individuals are not just more productive, but more creative and impactful. For organisations, platforms that are data-driven, AI-native, and seamlessly integrated allow them to test innovations rapidly and enter new markets with lower friction. Access to real-time insights and intelligent automation reduces barriers that once limited growth and innovation. At the individual level, technology democratises expertise – giving more people access to capabilities that were once specialised or scarce. At the same time, it fosters connectivity across ecosystems, enabling collaboration beyond traditional boundaries. Ultimately, technology will empower both enterprises and individuals to think bigger by removing constraints and achieve more by turning ideas into outcomes faster, more intelligently, and at scale.
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