By Suhail Gulzar, Senior Manager, Solutions Engineering, Neo4j
As India accelerates its AI journey, the conversation has shifted from everyday personal use to enterprise-scale deployment across banking, telecom, e-commerce, and public sector innovation. We have already seen early examples, from SBI using AI-driven insights to improve fraud detection, to government-led initiatives like ONDC and India Stack exploring AI-powered interoperability and citizen services.
Yet, with nearly 95% of global AI initiatives still struggling to demonstrate tangible ROI, it’s becoming clear that simply experimenting with AI isn’t enough. The next phase of adoption will be defined by trusted execution, where governance, integration, security, and contextual understanding become the foundation, not the afterthought.
This need for trust becomes even more critical as AI agents mature from simple automation tools into intelligent co-workers supporting core business functions. To make that shift responsibly, enterprises must ensure oversight, transparency, and alignment between human intent and machine output. This is where graph technology becomes essential. Companies like Uber, Novo Nordisk, and more already rely on graph databases to maintain real-time contextual understanding.
By connecting fragmented data, preserving relationships, and grounding responses in context, graph databases and contextual retrieval technologies dramatically reduce hallucinations and unlock explainable AI at scale.
By 2026, the leaders in India’s AI landscape won’t be defined by who adopts AI the fastest, but by who deploys it responsibly, building scalable, auditable, context-aware systems that meet regulatory and operational realities.”