As India pursues its clean mobility ambitions, the conversation about the country’s transportation future has largely been framed as a competition between electric vehicles, ethanol-powered vehicles and hybrids. Policymakers, automakers, and industry stakeholders often debate which technology is best positioned to lead the change. However, for a diverse country like 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, Findability Sciences, explains why the future of India’s mobility is likely to be shaped by a combination of technologies rather than a winner-take-all approach. They argue that artificial intelligence can serve as an important decision-making tool, helping stakeholders identify the right mobility solutions for specific sectors, use cases, and market conditions. By converting vast amounts of data into actionable intelligence, AI can enable a more efficient, sustainable and inclusive mobility transition.
India’s mobility debate has a recurring problem: it keeps asking the wrong questions.
The discussion is often framed as EVs vs. ethanol vs. hybrids, as if the aim is to identify one winner and build the future around it. But for a country of scale, geographic diversity and economic complexity like India, the more important question is not which technology wins. It’s about which technology works best, where, for whom, and under what circumstances. This difference is important – and today, with advances in artificial intelligence, it is becoming increasingly possible to find those answers.
A closer look at India’s mobility landscape reveals that each technology is moving on a different path. Electric vehicle sales reaching 2.05 million units in FY2025 is an impressive milestone, but it is primarily driven by strong adoption in the two-wheeler and three-wheeler segments, where affordability and operating economics make a compelling case. In contrast, passenger EV adoption is relatively limited beyond major urban centres, hampered by infrastructure gaps and charging vehicle price points that are still out of reach for many consumers.
Ethanol offers a different success story. India achieved its E20 fuel-blend target in 2025 – five years ahead of schedule – while ethanol production capacity has increased from less than 2 billion liters in 2014 to nearly 20 billion liters today. This rapid scale-up represents one of the country’s most significant energy-transition achievements, although its impact is often overlooked outside the industry.
Meanwhile, hybrids continue to gain momentum despite being seen as an interim solution. Sales are projected to grow from around 323,000 units in FY20 to around 1.7 million units by FY30, indicating their potential to bridge the gap in markets where full electrification or ethanol-based solutions may not yet be practical.
What emerges is not a competition with any one winner, but three technologies that follow different adoption stages and address different mobility needs across India. The real policy challenge is not to choose one over the other, but to create a balanced portfolio that allows each technology to play to its strengths in the markets where it can have the greatest impact.
The hardest problem is this: Managing that portfolio intelligently requires taking into account more variables at once than any traditional planning process. Fuel price volatility. Availability of feedstock as per season and region. Grid reliability district by district. Vehicle usage patterns across segments. Combining mandate interactions with hybrid adoption. Rural income relations are linked to ethanol uptake. These variables do not move independently, they are mixed, and they change rapidly. This is not a data problem. This is a decision-making problem. And this is exactly where AI earns its place.
We operate within India’s sugar and ethanol value chain, a system where distillery throughput, sugarcane yield cycles, logistics costs and blending economics are interdependent. Deploying AI as an active intelligence layer in that chain, not just for reporting, but for real-time decision support, brings out continuous optimization that traditional analytics misses. The complexity of mobility planning at the national level varies in scope, but not in type. The same principle applies: AI doesn’t just process data. It changes which decisions become possible.
OEMs mapping their 2030 powertrain strategy need to know not only where EVs are headed, but what is driving that growth, and what will hold it back in the next market. A fleet operator needs real-time guidance calibrated to his specific route, load profile and fuel access. Policymakers designing the next ethanol phase need to look at how the feedstock changes through pricing, blending rates, and farmer incomes in the same model. These are not the same questions. Considering them as one and the same makes the plan wrong.
India’s mobility future will not be defined by one technology triumphing over others. Instead, success will depend on building the intelligence and decision-making framework needed to deploy the most appropriate 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. All that is left is a shift in mindset – from technology-focused debates driven by ideology to decisions guided by evidence, real-world conditions and measurable outcomes.
Ultimately, the leaders of India’s mobility transformation will not be those who champion one technology over another, but rather those who can most effectively use 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.