Rajiv Pandey, CTO and VP, Tata Motors, about software, data, and artificial intelligence in driving modern business.
For much of the past century, the car was defined by mechanical engineering. Power, durability, and manufacturing scale determined success. That hierarchy is now shifting. Software, data, and artificial intelligence are beginning to shape how vehicles are designed, built, and operated.
Tata Motors, says that this transition has unfolded steadily rather than suddenly. The company says that it has been systematically collecting data across vehicles, manufacturing plants, dealers, service networks, and parts logistics since two decades. What started as operational record keeping has since evolved into a system that increasingly influences how decisions are made.
“Data started as something we observed,” says Rajiv Pandey, chief technology officer and vice-president of Tata Motors. “Today, it is something that drives the business.”
That shift, he argues, mirrors a broader change across the automotive industry. “The modern vehicle is no longer just a mechanical product. It is a computing system on wheels, making decisions in real time.”
An industry redefined by software
The automotive sector is undergoing one of its most consequential transitions since the advent of mass production. Electrification, emissions regulation, and changing consumer expectations are forcing manufacturers to compete on intelligence as much as on hardware.
Industry estimates suggest that software and digital services, which currently account for a modest share of automotive revenues, could represent a majority of industry value within the next decade. In India, the pace of change has accelerated as connected features and advanced driver-assistance systems move rapidly from premium models into the mass market.
“This shift has elevated the role of technology leadership inside carmakers. The ability to collect data, analyse it quickly, and translate it into operational advantage is becoming a key differentiator.” ~ Rajiv Pandey
From data to operational leverage
At Tata Motors, artificial intelligence moved beyond experimentation roughly six years ago. The focus shifted from isolated proofs of concept to embedding analytics directly into core workflows.
“We realised AI could not sit on the side as a pilot,” Pandey says. “It had to be part of how logistics, supply chains, and manufacturing actually work.”
Logistics provided an early demonstration of impact. By digitising planning and applying predictive algorithms, the company reduced the time required to move parts from warehouses to assembly lines from nearly twelve days to about one and a half. The improvement was not merely one of speed. Faster parts movement helped stabilise production schedules and reduce inventory risk, particularly during periods of supply-chain disruption.
Similar approaches were applied to parts planning and distribution. These efforts improved availability and contributed to a marked expansion of the parts business, while also lowering the likelihood of production stoppages.
Pandey discussed these changes recently while speaking at the Oracle AI World Tour in Mumbai, placing Tata Motors’ experience within a wider industry context.
Intelligence inside the vehicle
The application of AI is equally visible in Tata Motors’ products. Modern vehicles increasingly rely on software to manage safety, performance, and energy consumption. Multiple systems operate simultaneously, taking real-time decisions on braking, airbags, battery health, and range optimisation.
“In today’s vehicles, multiple AI systems are continuously running in the background,” Pandey explains. “They are making decisions that directly affect safety and efficiency.”
Electric vehicles intensify this trend. Managing charging behaviour, predicting usable range, and maintaining battery performance under varied conditions require continuous analysis of data. In this environment, AI becomes less an optional enhancement and more an operational necessity.
Infrastructure as an economic choice
Scaling these capabilities has required significant investment in digital infrastructure. Tata Motors has built platforms capable of supporting a growing ecosystem of manufacturers, dealers, service partners, and drivers, while maintaining high levels of availability.
The company has also connected its manufacturing plants through resilient networks designed to minimise operational disruption. Improvements in system reliability and network stability have translated into lower operating costs and reduced downtime.
“Infrastructure decisions are no longer just technical choices,” Pandey notes. “They have a direct impact on cost, resilience, and speed.”
What comes next
Looking ahead, Tata Motors plans to deepen its use of AI across manufacturing and service operations. Areas under consideration include intelligent devices on the factory floor, digital representations of dealer operations to improve service efficiency, and tighter integration of data across the enterprise.
Regulation will shape the next phase. As data protection requirements evolve, automotive companies will need to balance innovation with stronger governance and security.
Pandey suggests that Tata Motors views this transition as structural rather than cyclical. The company is not simply adopting new tools. It is adjusting to a world in which software, data, and intelligence increasingly determine how vehicles are built, sold, and used.
For an industry long defined by metal and machines, that represents a transformational change.