For more than a century, the auto industry has revolved around one thing: the machine itself. Better engines, higher efficiency, tighter manufacturing: these were the markers of progress. The engine wasn’t just a component; it was the centrepiece, both in design and in value. But today, we are witnessing a fundamental decoupling of value from hardware. The industry’s next great shift will not be defined by the roar of the engine, but by the intelligence embedded within it. What a vehicle knows, how it interprets its surroundings, how it reacts in real time: these capabilities are beginning to matter just as much as horsepower or mileage.
This is no longer a future trend; it is a current economic reality. We are moving from a model where value is locked at the point of sale to one where a vehicle is a living, evolving asset. Increasingly, the idea of a “smartphone on wheels” is not just a metaphor, it is becoming a business model.
AI shift in mobility is already underway
Today, most new vehicles come with some form of advanced driver assistance. Features that once felt premium, like lane keeping or adaptive cruise control, are quickly becoming standard. Electric vehicle makers aren’t really selling cars in the traditional sense anymore. The product keeps evolving. Software updates roll out over the air, tweaking performance even after purchase. Many newer players treat the car as a software platform, something that can be updated and improved over time. It changes not just how vehicles function, but how value is created.
Even at the retail end of the automotive value chain, AI-led platforms are beginning to automate customer engagement processes such as service reminders, feedback calls, and insurance follow-ups, reducing operational costs while improving response times. Early implementations suggest that such systems can lower voice operation costs by up to 30–40 percent, while maintaining or even improving conversion rates. As these AI platforms integrate voice, chat, and backend systems, they are turning traditionally manual, fragmented workflows into more seamless and data-driven processes.
Industry estimates suggest that by 2030, software and electronics could account for up to 30 percent of a vehicle’s total value. AI-driven features alone are expected to open up massive new revenue streams across the sector. For an industry long defined by thin margins in hardware manufacturing, this shift toward intelligence layers represents one of the most significant margin-expansion opportunities in its history.
Global shifts and opportunities
The shift is global, but the opportunity will be shaped by how markets build on their own strengths.
The United States leads at the architectural level: AI models, chip design, and foundational technologies. China, meanwhile, has mastered speed and scale, integrating software into manufacturing with remarkable agility.
India stands at a unique crossroads. It has strength in mechanical engineering and software services, but these capabilities often operate in silos. As one of the top three automotive markets globally, driven by growth across both two-wheelers and passenger vehicles, it also represents a significant real-world environment for mobility innovation.
The focus now is on leveraging India’s specific needs and conditions, while drawing on learnings from the West and advances from leading global markets. The next wave of competitiveness will come from co-designing software and hardware from the outset, enabling solutions that are both locally relevant and scalable.
India presents both structural gaps and significant opportunity
India’s mobility landscape is hard to standardise. Road conditions shift quickly, traffic is unpredictable, and multiple vehicle types share the same space, making it challenging for AI systems trained in more structured environments. While India generates vast amounts of mobility data, turning it into clean, usable datasets remains uneven, especially compared to global players that have logged billions of kilometres.
There are capability gaps too. India is strong in mechanical engineering and manufacturing, but automotive AI depends on embedded systems, real-time processing, and safety-critical software, areas still evolving at scale. The ecosystem is also less integrated than in the United States and China, and policy is skewed towards manufacturing over software and intellectual property, risking a shift of higher-value layers elsewhere.
Why does this matter?
Because the rules of competition are shifting. As vehicles become more software-driven, value is moving toward intelligence layers. A McKinsey report suggests the automotive software and electronics market could reach nearly $519 billion by 2035. If India stays focused mainly on manufacturing and assembly, it risks being pushed into lower-value segments of the global chain.
Also, there’s another side to this.
The opportunity is significant—arguably bigger than the risk. India’s automotive sector is already massive, and still growing. Even a partial shift toward AI-led value creation could unlock real gains. Take logistics. Small tweaks in route optimisation or fleet efficiency can save serious money in a country where logistics already takes up a big chunk of GDP. Or look at urban traffic. Systems that ease congestion could directly improve productivity in major cities.
In the near term, the biggest wins are unlikely to come from fully autonomous vehicles. More likely, they will come from applied intelligence. Practical, targeted use cases.
For fleet operators, even a 5 percent improvement in fuel efficiency through AI-led routing can drive meaningful savings. For manufacturers, the focus is on real-time traceability and waste reduction. For cities, it is about congestion management and recovering lost human-hours. It’s already starting to show in some setups.
The direction is hard to ignore.
For decades, the engine defined the automobile. It shaped how vehicles were designed, built, and sold. That era is now giving way to something more dynamic. Engine era is maturing to intelligence era. Intelligence is becoming the core infrastructure of mobility. The race is no longer about who can build the fastest machine, but who can build the smartest one.
