By Vaibhav Kaushik, Co-founder & CEO of Nawgati
If you’ve been around India’s fuel retail or fleet business for any amount of time, you already know how much of it runs on gut feel. A fleet operator takes a rough guess at how much fuel his vehicles are going to burn this week. A station owner looks at last month’s numbers and hopes this month follows the same pattern. A distributor plans his deliveries around averages that made sense two years ago but haven’t really kept up with how the roads and logistics hubs have shifted since. Nobody’s particularly happy with this, but it’s been the reality in a business that’s built on physical assets and a lot of manual back-and-forth.
So what’s actually changing now? AI is coming into this space, and I don’t mean in a gimmicky way. The real shift is simpler than people make it sound, it’s about closing the gap between what’s happening on the ground and what the people running operations actually know. And more importantly, when they find out.
Here’s the thing. A fuel network generates an enormous amount of data every single day. Transaction times, vehicle movement, how much fuel is going out, how long vehicles are sitting at a station, queue lengths. All of this has been getting generated for years. But most of it either sat locked inside individual systems or just got discarded. And whatever insight was buried in that data usually showed up too late to do anything about it.
That’s the part that’s genuinely changing. Instead of looking backwards at what happened yesterday, operators with the right systems are now getting a picture of what’s likely to happen tomorrow, or even in the next few hours. A demand spike building on a busy transport corridor. A stockout forming at a high-volume outlet before it actually hits. A fleet whose consumption patterns are quietly going in the wrong direction before the dispatcher has noticed anything unusual. And in a business where margins are already tight, that kind of early warning is genuinely worth something. The earlier you know about a problem, the more you can actually do about it.
Now, knowing something’s going to go wrong is only half the job. The more useful question is, what do you do about it? This is where things get practically interesting. Instead of restocking on a fixed weekly schedule, AI can figure out the right replenishment window based on real-time consumption, what demand is expected to look like over the next few days, supplier lead times, and what the roads are actually like right now. Less deadstock sitting around, fewer stockout situations, lower costs overall. Same with tanker routing, anyone who’s tried to plan fuel deliveries across India’s road network knows how messy that gets. Variable traffic, checkpoints, tight delivery windows. Systems that can keep adjusting the plan in real time, based on what’s actually happening on the road, do a noticeably better job than anything static.
And then there are the bigger calls, where to open the next site, how to sequence network expansion, where to put capital. These have always been complicated decisions, and honestly, the information going into them has often been pretty patchy. What’s interesting now is that AI doesn’t replace the judgment call, it just gives the person making it a much better picture to work from. Instead of trying to hold location data, traffic trends, competitor density, and demand forecasts in your head at the same time, you have a system that’s already done that synthesis and is showing you the options with the reasoning laid out. That’s a genuinely different quality of input than what most operators have had access to before.
But here’s what I’d say to anyone looking at this space, the technology isn’t actually the hard part. The organisations that are getting real results aren’t necessarily the ones spending the most. They’re the ones that got their data infrastructure sorted first. And in fuel retail, that’s a real challenge, because so much of this industry’s data has historically lived on paper. Manual dip readings. Verbal handoffs. Register entries. Getting that data captured digitally, in real time, at the point where it’s actually being generated, that’s the foundation. Everything else, the prediction, the optimisation, the smarter decisions, all of it sits on top of that. Get the data layer right, and the value of everything built on it keeps growing over time. Skip it, and the most sophisticated AI in the world isn’t going to help much.