As India advances its clean mobility ambitions, the conversation around the country’s transportation future has largely been framed as a contest between electric vehicles, ethanol-powered vehicles and hybrids. Policymakers, automakers and industry stakeholders often debate which technology is best positioned to lead the transition. However, for a country as diverse as India, with varying levels of infrastructure development, energy access and consumer needs, the answer may not lie in choosing a single solution.
In this article, Anand Mahurkar, Founder and CEO of Findability Sciences, explores why India’s mobility future is likely to be shaped by a combination of technologies rather than a winner-takes-all approach. He argues that artificial intelligence can serve as a critical decision-making tool, helping stakeholders identify the right mobility solution for specific regions, use cases and market conditions. By transforming vast amounts of data into actionable intelligence, AI could enable a more efficient, sustainable and inclusive mobility transition.
India’s mobility debate has a recurring problem: it keeps asking the wrong question.
The discussion is often framed as EVs versus ethanol versus hybrids, as though the objective is to identify a single winner and build the future around it. But for a country of India’s scale, geographic diversity and economic complexity, the more important question is not which technology wins. It is which technology works best, where, for whom and under what circumstances. That distinction is crucial—and today, with advances in artificial intelligence, finding those answers is becoming increasingly possible.
A closer look at India’s mobility landscape reveals that each technology is progressing along a distinct path. Electric vehicle sales reached 2.05 million units in FY25, an impressive milestone, but one largely driven by strong adoption in the two- and three-wheeler segments, where affordability and operating economics make a compelling case. In contrast, passenger EV adoption remains relatively limited beyond major urban centres, constrained by charging infrastructure gaps and vehicle price points that are still out of reach for many consumers.
Ethanol presents a different success story. India achieved its E20 fuel-blending target in 2025—five years ahead of schedule—while expanding ethanol production capacity from less than 2 billion litres in 2014 to nearly 20 billion litres today. This rapid scale-up represents one of the country’s most significant energy-transition achievements, though its impact is often overlooked outside industry circles.
Hybrids, meanwhile, continue to gain momentum despite frequently being viewed as an interim solution. Sales are projected to grow from around 323,000 units in FY24 to nearly 1.7 million units by FY30, reflecting their ability to bridge the gap in markets where full electrification or ethanol-based solutions may not yet be practical.
What emerges is not a contest with a single winner, but three technologies following different adoption curves and addressing different mobility needs across India. The real policy challenge is not choosing one over the others, but creating a balanced portfolio that allows each technology to play to its strengths in the markets where it can deliver the greatest impact.
The harder problem is this: managing that portfolio intelligently requires holding more variables simultaneously than any conventional planning process can handle. Fuel price volatility. Feedstock availability by season and region. Grid reliability district by district. Vehicle usage patterns across segments. Blending mandate interactions with hybrid uptake. Rural income linkages tied to ethanol off-take. These variables do not move independently, they compound, and they shift fast. That is not a data problem. It is a decision-intelligence problem. And that is precisely where AI earns its place.
We work within India’s sugar and ethanol value chain, a system where distillery throughput, cane yield cycles, logistics costs, and blending economics are tightly interdependent. Deploying AI as an active intelligence layer across that chain, not just for reporting, but for real-time decision support, consistently surfaces optimisation that conventional analysis misses. The complexity of mobility planning at a national scale is different in scope, but not in kind. The same principle applies: AI does not just process the data. It changes what decisions become possible.
An OEM mapping its 2030 powertrain strategy needs to know not just where EVs are growing, but what is driving that growth, and what would stall it in the next market. A fleet operator needs real-time guidance calibrated to their specific route, load profile, and fuel access. A policymaker designing the next ethanol phase needs to see how feedstock shifts ripple through pricing, blending rates, and farmer income in the same model. These are not the same question. Treating them as one is where planning goes wrong.
India’s mobility future will not be defined by a single technology emerging victorious over the others. Instead, success will depend on building the intelligence and decision-making frameworks needed to deploy the most suitable solution in the right geography, for the right application, and at the right time.
The foundation for this approach already exists. The data is available, computational capabilities are advancing rapidly, and AI-powered analytics can now process levels of complexity that traditional planning methods struggle to manage. What remains is a shift in mindset—from technology-centric debates driven by ideology to decisions guided by evidence, real-world conditions and measurable outcomes.
Ultimately, the leaders of India’s mobility transition will not be those who champion one technology over another, but those who can most effectively harness data and intelligence to integrate multiple solutions into a coherent, scalable and sustainable mobility ecosystem. That capability, more than any individual breakthrough in batteries, fuels or powertrains, is likely to shape the next phase of India’s transportation revolution.
