In an Indian automobile plant, artificial intelligence is inspecting welds on the assembly line, while on the highway a connected car predicts maintenance weeks in advance. India’s auto sector is rapidly transforming: AI is now embedded not just in factories but also in vehicles. Electric vehicles (EVs) with smart diagnostics and cloud-connected cars are no longer novelties. From predictive maintenance alerts to self-optimising production robots, intelligence is becoming a key differentiator in a competitive market.
India’s automotive industry, now the world’s third-largest by volume, produced over 31 million vehicles in FY2025, exporting more than 5.3 million of those vehicles abroad. Growth is fueled by rising incomes and an export push. Yet amid this optimism, automakers face complex challenges. Demand has been volatile – buffeted by economic swings and supply chain disruptions – while the supplier base is fragmented across thousands of suppliers.
A talent crunch adds to the pressure: 94% of Indian auto firms struggle to recruit skilled workers in fields such as AI, software-defined vehicles and advanced driver-assistance systems. This skills gap, coupled with the need for new digital capabilities, underscores that adopting AI-driven solutions is as much a business necessity as it is a tech trend.
AI across value chain
From design and manufacturing to on-road operations, generative AI is transforming each link of the automotive value chain. On the shop floor, Indian OEMs are adopting Industry 4.0 practices at scale: using IoT sensors for real-time machine data and automated corrective actions (ensuring consistent quality while lowering costs) and AI-driven computer vision systems for quality inspection. IBM’s AI and data platform watsonx is an example of a system that enables analysis of such data by AI systems to help predict problems before they occur. These advances translate into tangible gains: better uptime via predictive maintenance using AI agents, optimised production schedules, and fewer recalls thanks to early anomaly detection.
Srivats Ram, MD, Wheels India, said that the company is using AI extensively, “in the inspection process with a view to deskill, improve consistency and also bring predictability into the process. This has been done to enhance the reliability of our manufacturing process.”
On the road, vehicles are becoming rolling computers, generating vast amounts of data that automakers leverage for smarter services and predictive upkeep. Connected vehicle platforms – such as Ashok Leyland’s iAlert – link thousands of trucks and buses, streaming real-time engine and sensor readings to the cloud. AI algorithms analyse these feeds to predict issues, allowing proactive maintenance. The market for remote diagnostics and over-the-air updates is surging, growing over 22% annually in India as fleet operators and consumers embrace predictive maintenance.
“Artificial Intelligence is emerging as a key technology that is reshaping the automotive industry,” says Sanjay Singh, Executive Vice-President, Production, Suzuki Motorcycle India Pvt. Ltd. “It is enabling change across the value chain, from design and production to quality and after-sales. At Suzuki Motorcycle India, we look at AI as an evolving capability that brings new learnings and opportunities to make our processes more robust and efficient.
“We have already begun leveraging AI in our quality inspection processes. AI-enabled cameras are helping us ensure correct part fitment and placement, as well as accurate verification of engine and chassis numbers by matching alphanumeric data with system records,” he says. “Looking ahead, with several projects in the pipeline, we expect AI to play a critical role in enhancing precision, efficiency, and reliability across our manufacturing operations, making them future-ready.”
A blend of edge and cloud computing enables these capabilities. Modern vehicles carry onboard AI chips for instant processing (for safety alerts or driver monitoring) while offloading heavier analytics to the cloud. This hybrid approach ensures critical insights are available when and where needed – on a factory line or in a moving vehicle. By combining edge AI with cloud-based machine learning, OEMs deliver remote diagnostics and fleet intelligence at scale, turning data into actionable insights across the vehicle lifecycle.
For many OEMs, early AI wins – like automated quality checks or AI-driven supply chain forecasts – have validated its promise. The focus now is on scaling these pilots company-wide to deliver strategic value, whether a more agile production network or vehicles that get “smarter” with every software update.
What Makes This AI Revolution Possible?
On the technology front, a hybrid cloud-edge architecture provides the backbone for AI at scale, allowing data to flow seamlessly from connected factories and vehicles into central cloud systems for analysis; an open IT system that integrates IoT sensor streams, enterprise data, and external sources into a unified platform is essential. Equally critical is a strong data strategy: Maruti Suzuki, for instance, built a unified data lake integrating shop-floor machine data, customer feedback and connected car telematics – enabling it to forecast demand, predict quality issues, and loop insights back into product design.
Biswajit Bhattacharya, Lead Client Partner & Automotive Industry Leader, IBM Consulting India & South Asia said, “We are enabling automakers to integrate IoT, enterprise, and connected car data into a single platform. It is so that insights flow seamlessly across design, production, and on-road operations. This convergence of cloud, edge, and AI is what allows OEMs to move from reactive fixes to predictive intelligence at scale. Our work with global and Indian OEMs shows that when leadership aligns digital investments with workforce skills, business strategy and responsible AI framework, AI delivers measurable impact – from cutting supply chain risks to boosting productivity and customer experience.”
An AI-ready workforce and an innovation-friendly culture are crucial. That means upskilling teams in data science, integrating IT and operations, and fostering an agile mindset. According to an IBM study, an AI-savvy workforce is key to unlocking tangible results from AI investments. Organisations that invest in talent and break down silos are better positioned to leverage AI than those treating it as a back-room experiment.
AI can’t be confined to labs – it must solve real shop-floor and on-road pain points, so successful firms start by targeting use cases with tangible ROI – boosting line productivity by 10% through AI automation or cutting warranty costs via predictive diagnostics – then iterate. Leaders must ensure the right digital infrastructure and skills are in place to support these ambitions, or pilot projects will flounder in isolation. Security and scalability are non-negotiable: as vehicles and factories become software-centric, the risk profile changes, so scaling AI solutions securely is vital.
Collaborating with firms offering proven automotive AI solutions – along with the cloud-edge infrastructure to deploy them – can accelerate progress while mitigating risks.
Ultimately, in the automotive sector, AI isn’t about chasing the latest buzzword – it’s about building a smarter, more resilient business. From the assembly line to the city street to highways, those that leverage AI effectively today will define mobility’s future.
Published – August 30, 2025 05:36 pm IST
