HOW FAR CAN AI DRIVE THE AUTOMOTIVE SECTOR? 

Co-authored by:  Giuseppe Pedretti (Regional Managing Director EMEA, PLI)   & Ravi Tallamraju (Chief Technology Officer, PLI) 

The conversations around AI in the automotive industry are often associated with self-driving cars. Yet, it’s the behind-the-scenes applications of AI, from design and diagnostics to driver experience and operational strategy, that are proving to be far more transformative.

Predictive maintenance has emerged as one of AI’s most powerful contributions to the sector. By continuously analysing telematic and operational data, AI enables early detection of mechanical stressors and anticipates component failures before they escalate. This foresight empowers fleet managers and workshops to schedule interventions with precision, minimising service interruptions, extending vehicle longevity, and curbing unnecessary expenditure.

AI-driven maintenance strategies are reshaping operational resilience, providing benefits such as real-time issues diagnosis, automated service reminders, and refined route efficiency, In fact, over half of fleet managers cite predictive analytics as a key lever for reducing overheads and enhancing performance, with nearly a third identifying AI and machine learning as the most influential technologies in fleet management over the next five years. From voice-enabled assistants that coach drivers to connected cameras that detect fatigue, the scope of predictive tools is expanding and ushering in a new era of intelligent, preventative care across the mobility landscape.

These innovations are especially critical for a sector that has faced considerable turbulence. In 2024, the automotive industry grappled with factory closures, supply chain fragmentation, and declining production across Europe and the West. Consumer demand softened under affordability pressures, while rising component costs and inflation compressed margins across the value chain, from workshops to fleet operators. Meanwhile, intensifying competition from Chinese manufacturers continues to push Western businesses to innovate and streamline.

Some argue AI has contributed to these pressures, but is also key to overcoming them. Its ability to convert data into strategic insights and automate complex workflows is helping businesses regain competitiveness, uncover new revenue streams, and reimagine their operating models.

Shifting Gears with AI

By 2032, the global automotive AI market is projected to reach $405 billion, with roughly 75% of automotive enterprises experimenting with at least one GenAI application. While major players are deploying AI across product design, supply chain optimisation, and customer engagement, fast accelerating smaller businesses stand to gain the most.

For these companies, the focus is on enhancing practical tools that drive measurable efficiency. Vehicle telematics, for example, enables workshops to diagnose issues in real time, store service histories, and anticipate future maintenance needs. This reduces reactive repairs and improves outcomes for customers and stakeholders alike.

Another area gaining traction is inventory intelligence. AI-powered forecasting tools analyse historical and repair data to predict parts demand with increasing accuracy. This not only prevents overstocking but ensures critical components are available when needed.

Fleet managers are equally enthusiastic as AI helps maintain uptime, optimise routes, and improve safety. Generative AI is powering in-vehicle voice assistants that guide drivers, flag risky behaviour, and even offer coaching. Connected cameras now detect signs of fatigue or distraction, reducing risk exposure and potential legal liabilities.

In-car connected services are also surging, with adoption expected to grow from 60% in 2024 to over 90% of new vehicles featuring voice assistants by 2028.

Why AI Belongs in Automotive Operations

The numbers speak volumes: the global market for automotive AI is forecasted to grow from $44 billion in 2025 to $74.5 billion by 2030. But beyond the figures, the rationale is clear.

Efficiency is a key driver of change. AI automates routine diagnostics, speeds up service checks, and simplifies documentation – allowing skilled personnel to focus on higher-value tasks. At the same time, these intelligent systems continue to learn and improve over time.

Safety is equally critical. Traditionally, the industry has taken a reactive approach, fixing problems only after they occur. AI transforms this model by enabling proactive vehicle management by detecting potential risks early, preventing failures, and ensuring compliance with increasingly strict safety regulations.

Cost control remains a key priority. In a margin-sensitive industry, even minor delays or downtime can erode profitability. AI helps minimise idle time, identify inefficient driving behaviours, and deliver more precise diagnostics. With fuel costs accounting for up to 40% of fleet expenses, AI plays a crucial role in pinpointing and eliminating waste, leading to more reliable operations and healthier bottom lines.

Still, adoption isn’t universal. Complex tools, fragmented data, and constrained budgets pose real challenges, especially for smaller players who rely more on experience than analytics. That’s precisely where AI excels: transforming existing knowledge into actionable intelligence.

How PETRONAS Lubricants International Uses AI

As a lubricant specialist, PETRONAS Lubricants International (PLI) leverages AI to accelerate R&D. Our models simulate lubricant performance under varied operating conditions, trained on extensive datasets of compositions. This allows us to predict outcomes before physical testing, sometimes revealing unexpected applications beyond automotive.

Smart tech and IoT devices also enable us to forecast lubricant degradation and advise customers on optimal service timing. Our Oil Condition Monitoring (OCM) system analyses samples for contaminants and wear metals, identifying potential issues before they become costly failures. Expert technicians deliver tailored reports that guide oil drain intervals and ensure consistent performance across fleets and machinery. This proactive approach enhances efficiency and extends equipment lifespan through intelligent, data-backed insights.

Internally, AI supports our production health: minimising waste, optimising throughput, and helping us meet sustainability goals by avoiding unnecessary downtime.

What More Can AI Do?

The potential of AI in automotive services is just beginning to unfold. The innovations we have achieved in lubricants alone demonstrate what is possible. As data becomes more accessible and algorithms more refined, even small operations will compete on insight, not just infrastructure. And as those innovations ripple across the sector, the competitive landscape will shift. That shift is coming. Best to be ready and in a position to lead.