There is a moment that separates the old automotive business model from the new one.
Under the old model, a commercial fleet owner buys a set of tyres, gets an invoice, and doesn’t hear from the supplier until something goes wrong. Under the new model, sensors on those tyres flag abnormal pressure 48 hours before a blowout, a predictive alert reaches the fleet manager, and a service appointment is already booked.
That shift from transactional vendor to proactive partner is the defining challenge facing India’s automotive sector right now. And the companies getting it right are not just winning on customer experience. They are building an entirely different revenue model.
India’s Automotive Moment
India’s automobile market is expected to grow from $137 billion in 2025 to $147.58 billion in 2026, with forecasts reaching $213.74 billion by 2031. Debashish Roy, Chief Digital Transformation Officer at CEAT, speaking with Soumya Majumdar, Regional Vice President of Sales, Salesforce India on the Great India Industry Transformation podcast, outlined the forces reshaping the sector: rising incomes, EV adoption, SUV premiumisation, and a China+1 tailwind that is directing global supply chain investment toward Indian manufacturing.
But the bigger story underneath all of this is a structural shift in how automotive companies make money. Selling a vehicle used to be a single-point revenue event. Today, every vehicle on the road is an ongoing relationship: service cycles, warranty, connected data, upsell, fleet management. As Majumdar puts it: “Earlier, selling a car was a single-point revenue for OEMs. Now it is lifetime revenue, because they are interfacing with customers at different lifecycle stages of the car.”
OEMs and component manufacturers that recognise this are restructuring accordingly. Those still running on manual processes and fragmented data systems are leaving significant revenue on the table.
When Complexity Becomes a Ceiling
The problem is that most automotive organisations were built for the old model: Dealer networks operating in isolation. Secondary sales data compiled monthly through MIS reports rather than available in real time. Manual steps scattered across the order-to-cash cycle. Warranty claims requiring multiple iterations over days.
When your business model was transactional, this was survivable. But when your model depends on continuous, personalised engagement across thousands of dealers, hundreds of distributors, and millions of end customers, this becomes a structural ceiling.
CEAT confronted this directly as it sought to modernise its commercial operations: more than 3,000 dealers, 300+ distributors, 800+ dealer sales executives, 50,000+ sub-dealers, and over a lakh transactions every month. At that scale, fragmented systems don’t just create inefficiency, they create invisibility. As Roy explains, the goal for digital transformation was clear from the start: “Growth through better customer reach, better dealer coverage, less leakage in the systems, and killing each and every manual step across the order-to-cash cycle.”
What Winning in Automotive Actually Requires
The gap between automotive organisations pulling ahead and those falling behind is not the quality of their product or the size of their dealer network. It is the infrastructure sitting underneath how they sell, serve, and engage. Three capabilities now define this gap — and the companies investing in all three are the ones converting India’s automotive moment into a durable competitive position.
A Connected Foundation
Before intelligence can be applied, data has to be unified. Most automotive organisations operate across fragmented systems — separate platforms for sales, service, dealer management, and customer data — that make it structurally impossible to have a single, accurate view of any relationship in the network.
Agentforce 360 for Automotive addresses this with a unified automotive data model that brings together Driver 360, Vehicle 360, and Dealer 360 into one connected system. Every purchase history, service interaction, warranty record, open lease, and in-vehicle subscription sits in one place — accessible to every team that needs it, in real time.
For CEAT, the impact of this unification was immediate and measurable. Dealers moved onto a single connected portal where they could order stock, check schemes, and raise claims themselves. The “configuration over code” principle that governed the build meant CEAT’s entire dealer app was deployed in approximately four months, fast enough to matter in a market moving this quickly. The key outcomes were:
- 100% dealer adoption on the connected portal, with dealers managing orders, schemes, and claims autonomously
- Warranty claim processing reduced from multiple days of manual iteration to one hour
- Secondary sales data now available in real time across 3,000+ dealers and 300+ distributors, replacing monthly MIS reports
- NPS tracking shifted from periodic surveys to live data
From Reporting to Action: Prescriptive Intelligence
Once the data foundation is in place, the question becomes what you do with it. Most Indian automotive organisations are still at the descriptive stage: reporting on what already happened. The competitive gap is opening at the prescriptive layer, where the system anticipates what is about to happen and acts on it.
As Majumdar puts it: “We are now into prescriptive, which is the agentic layer. That means you can take action on certain things which you preempt before they happen.”
This is where Agentforce 360 for Automotive moves beyond a data platform and into an action layer, with Agentforce embedded into the flow of work. Agents handle the entire lead-to-conversion journey — scoring incoming leads from digital campaigns, routing them to the right dealer or sales representative, managing trade-in appraisals and inventory checks, and ensuring no lead falls through the gap between online interest and dealership interaction. On the service side, service agents flag potential vehicle issues before the customer reports them, automatically triggering the right intervention at the right time. Agents also surface next-best-action recommendations to dealer teams based on real-time inventory, scheme eligibility, and customer history, shifting the role of the dealer from reactive order-taker to proactive relationship manager.
Tableau and Tableau Next sit across all of this as the visibility layer, giving fleet owners, dealer principals, and commercial teams a live operational view rather than a monthly retrospective.
For CEAT, moving from monthly MIS reports to real-time secondary sales data across 3,000+ dealers and 300+ distributors was the clearest expression of this shift. SKU-level propensity models now predict which products are likely to be needed at specific dealer locations before gaps appear. NPS, previously tracked through periodic surveys, now runs on live data. The organisation is no longer reading last month’s story. It is acting on what is happening right now.
Hyperpersonalisation Across the Channel Mix
The third capability is ensuring that the intelligence gathered across the network is used to engage with the end customer in a personalised, contextually relevant way, at every channel and every touchpoint.
Majumdar describes what this looks like in practice: “The next time a customer comes onto the website, it is now contextual. If they love a red car, the car is red. And that personalisation flows down to WhatsApp, SMS, any channel. The experience across channels is hyperpersonalised and contextual.”
Agentforce Marketing makes this possible by unifying customer, campaign, and revenue signals so that teams can identify audiences, adapt content, and orchestrate personalised journeys across every channel in real time. The result is a continuous relationship from the first digital research touchpoint through the in-dealership experience to post-purchase service, with every interaction informed by the one before it.
AI That Works in the Real World
The third layer of intelligence runs deepest when it reaches the shop floor and the field — not just the dashboard. As Roy points out, “AI is not really real until it hits the shop floor or the market.”
At CEAT, machine learning models applied to batch mixing processes reduced energy consumption by 30% by minimising cycle time variance. An in-house root cause analysis tool compressed diagnostic time from four hours to three to four minutes. On the commercial side, Agentforce AI agents handle routine dealer and customer queries autonomously in real time, resolving issues instantly and freeing sales and service teams for the conversations that require human judgment. The intelligence layer and the human layer are not in competition. Each makes the other more effective.
The Companies That Get This Right Will Define the Category
India’s automotive sector is at a point where growth, electrification, and global export opportunities are all converging simultaneously. The organisations that capitalise on it will not be those with the best products alone: they will be those that built the data infrastructure, the dealer connectivity, and the customer intelligence to turn every vehicle sale into the beginning of a long-term relationship.
To hear how CEAT is navigating this transformation in practice, from real-time dealer data to AI on the shop floor, listen to the Great India Industry Transformation podcast episode featuring Debashish Roy, Chief Digital Transformation Officer at CEAT, and Soumya Majumdar, Regional Vice President of Sales, Salesforce India.
