India’s AI opportunity massive, but legacy infrastructure holds back scale: Confluent's Rubal Sahni

India is at a crucial juncture in its artificial intelligence (AI) evolution, with its vast digital ecosystem presenting a unique chance to influence the global data economy. However, realizing this potential hinges on the ability of businesses to transition from outdated infrastructure to modern, real-time data systems. Rubal Sahni, Area Vice President and Country Manager for India at Confluent, emphasizes a significant obstacle in this journey—the 'modernisation paradox.' This term encapsulates the issue of critical enterprise data being stuck in legacy systems. He points out that many essential operations in sectors such as banking, insurance, telecommunications, and large enterprises still rely on mainframes and traditional databases. The modernization of these systems is crucial for unlocking valuable data that can support real-time analytics and AI applications, including instant payments, personalized services, fraud detection, and predictive maintenance. While numerous organizations have begun to adopt cloud services, APIs, and microservices to experiment with AI, Sahni highlights that genuine transformation requires a fundamental rethinking of data flow across various systems. Since AI inherently needs real-time context, relying on static or outdated datasets is not an option for achieving meaningful outcomes. As AI increasingly becomes a priority for executive teams, Chief Information Officers (CIOs) find themselves balancing the need for rapid innovation with the necessity for control. Sahni notes a shift among successful leaders from a focus on speed alone to a more responsible approach that incorporates governance into the data framework. In highly regulated sectors like banking and healthcare, it is imperative that AI outputs are clear, traceable, and auditable, leading to a governance model that integrates with the data itself. Despite significant investments in AI, nearly 90% of initiatives fail to progress beyond the trial phase, a situation Sahni refers to as 'pilot purgatory.' This failure is primarily due to the reliance on static datasets and controlled environments that do not reflect real-world complexities. Without cohesive data streams and well-defined key performance indicators (KPIs), these projects often fall short of delivering measurable business outcomes. To help bridge this gap, Confluent has teamed up with companies like Deloitte, Infosys, and Tata Consultancy Services to assist enterprises in scaling AI deployment. These partnerships aim to merge domain expertise with real-time data infrastructure, facilitating the transition from experimentation to large-scale production. The shift to real-time data is already transforming various sectors. In banking, it allows for instant payments and fraud detection; in retail, it enables dynamic pricing and tailored customer experiences; and in manufacturing, it enhances predictive maintenance and supply chain efficiency. The significance of real-time data orchestration is particularly evident during high-demand periods, such as India’s festive seasons, when digital platforms like Swiggy and Zomato successfully manage surges in demand without disruptions. Sahni asserts that the next phase of India's digital progress will focus on transitioning from data ownership to data orchestration, where real-time intelligence fosters productivity, financial inclusion, and better public services. "The opportunity is massive, but directing investments and adopting the right technology with clear objectives will be crucial for our nation’s success. We are not merely participating in the digital economy; we are defining its future operations."