India’s Next AI Race Will Be Won by Companies That Understand Data First: SG Analytics

India is fast becoming a top market for enterprise AI adoption. Supriya Dixit, Senior Vice President and Head of Global Marketing at SG Analytics, believes “Indian enterprises are fast catching up and in many areas, even leapfrogging their global counterparts thanks to cloud-native and AI-first operating models. Many organisations are not constrained by decades of legacy infrastructure, allowing them to move directly towards cloud-native, AI-first architectures.”

Dixit added that sectors like BFSI, retail and e-commerce, where there are huge amounts of data available and where the use cases are very clear, are already seeing returns on their investment in AI technologies. “BFSI is transforming risk management, fraud detection and personalised financial services through AI, while retail is seeing strong returns from supply chain optimisation, dynamic pricing and customer sentiment analysis. Healthcare is also emerging as a significant growth area, particularly in patient data management,

Continual learning was another aspect she stressed as the use of AI spreads through enterprises. According to Sane, “reskilling is no longer optional. As AI takes over routine execution, organisations must reinvest that capacity into developing strategic, analytical and decision-making capabilities across their workforce.” Since foundation models have been getting easier to access, Madhav thinks that the advantage will lie more in owning enterprise knowledge than selecting models. “Everyone can access the same foundation models. What competitors cannot replicate is an organisation’s proprietary data, business context and domain expertise. The real moat is no longer the model itself but how effectively organisations embed that context into their AI systems and business workflows,”

Moving forward, SG Analytics sees that enterprise AI leadership would be determined by the extent to which an organization has data governance, contextual intelligence and scalable operating models along with responsible AI approaches. According to Madhav, those enterprises which treat AI deployments as mere pilots will lag behind those enterprises that design their business process for AI from day one. “Three to five years from now, market leaders won’t be distinguished by who has access to the biggest models. They will be the organisations that got the architecture right, grounded AI in enterprise context and redesigned workflows to create measurable business value,”