The conversation around artificial intelligence in India has moved past wide-eyed wonder. Talk to enterprise leaders today and the questions have changed. Not “what can AI do” but “what is it actually doing,” and just as importantly, “what’s still missing before it can be trusted with more.” A look at how three sectors are putting AI to work, in critical infrastructure, enterprise data, and digital trust, shows a technology that’s maturing fast, but unevenly.
For Ravindra Singh, Managing Director of Delcom Telesystems, the shift is visible in an industry where failure isn’t an inconvenience but a cascading risk. In India’s power transmission and distribution sector, where a single equipment fault can ripple across cities, AI has moved from a nice-to-have to a strategic necessity. “The true value of artificial intelligence lies not in the technology itself, but in its ability to solve real-world challenges across critical infrastructure sectors,” Singh says. As utilities pour investment into smart grids and digital substations, he points out, they’re also taking on more complexity: more distributed assets, more interconnected systems, more room for something to go unnoticed until it’s too late.
That’s precisely the gap AI is closing. Video analytics that flag anomalies before they become outages. Edge computing that processes threats where they occur rather than after the fact. Intelligent monitoring that turns a utility from a system that reports problems into one that anticipates them. “The organizations that embrace AI-driven intelligence today will be better positioned to build the secure, resilient, and future-ready power networks that India’s growth ambitions demand,” Singh notes, framing AI not as an add-on, but as the connective tissue between surveillance, networking, cybersecurity, and operations.
Move from the power grid to the enterprise stack, and a similar theme plays out in a different key. Niraj Kumar, CTO of Onix, argues that the more interesting question isn’t whether AI is fast, but whether it can be trusted. “At Onix, we think about AI not as a standalone tool but as something that needs context to be genuinely useful,” he says. That philosophy underpins Wingspan, the company’s Enterprise Intelligence Fabric, built to give AI agents a living, connected view of an organisation’s data and business logic. The difference, as Kumar frames it, is between an AI that’s quick and one that’s actually dependable.
The payoff, he says, is already visible: enterprises modernising faster, manual effort dropping sharply, and, perhaps most tellingly, AI graduating from scattered pilots into something closer to an operating layer, one that lets teams make real-time calls instead of waiting days for an answer. “That’s the shift that matters,” Kumar says, “not marvelling at the technology itself, but recognizing its power to make enterprises fundamentally smarter and faster.”
Amit Relan, Co-Founder and CEO of mFilterIt, pushes the idea further still, arguing that the technology’s real contribution isn’t automation but transformation. “AI’s greatest contribution isn’t automation, it’s transformation,” Relan says. Its value, he argues, comes from stitching intelligence together across systems, people, and processes that would otherwise stay fragmented, turning scattered data into sharper, faster decisions, and giving businesses the confidence to get ahead of change rather than scramble to catch up with it.
But Relan is careful not to let the technology take all the credit. “Technology alone is never the answer,” he says. “AI brings speed, scale, and precision; people bring context, ethics, creativity, and judgment.” The organisations that will pull ahead, in his view, are the ones that treat AI and human judgment as partners rather than substitutes, a combination he believes will “redefine what’s possible and create wonders we have yet to imagine.”
Taken together, these three perspectives, from critical infrastructure, enterprise data, and digital trust, point to a technology that’s no longer being celebrated for what it might someday do. It’s being put to work, quietly and specifically, in substations, data pipelines, and decision-making processes across the country. But each of them also flags the same unfinished business: AI is only as good as the context, governance, and human judgment built around it. The real story of AI in India right now isn’t that it exists. It’s whether enterprises can make it dependable enough to build on.
