AI in the automotive industry: Trends, benefits & use cases (2025)

Automotive artifical intelligence is becoming the backbone of innovation across design, manufacturing, and in‑vehicle performance. From smarter powertrains to predictive maintenance, AI is reshaping how the industry thinks about efficiency, safety, and customer experience.  

What is AI in the automotive industry?

AI in the automotive industry spans the use of smart technologies like machine learning, deep learning, and computer vision to improve how vehicles are designed, built, run, and supported. By turning real‑time data from sensors, factories, and drivers into clear insights, AI is helping automakers and suppliers deliver vehicles and services that are safer, more efficient, and ready to meet evolving consumer expectations.

What lies ahead for the future of AI in the automotive industry?

Over the next decade, AI will transition from pilot programs to integrated systems that power autonomous fleets, smart factories, and live-vehicle diagnostics. One of the key game-changers is predictive thermal management in electric motors.  

ZF’s TempAI solution uses machine learning models that boost temperature management in electric powertrains, increasing forecast accuracy by over 15%, and unlocks approximately 6% more peak power due to the precision of TempAI’s temperature prediction. Read more on ZF’s product development as an AutoTechInsight customer.

OEMs can translate these gains into performance specs and range uplifts, while data analysts can overlay TempAI’s precise thermal metrics with on-road telematics. For suppliers, embedding AI at scale into subsystems creates up- and downstream value differentiation, positioning their offering as future-ready by design.

Get the market intelligence, analytics, and expert insights on automotive technology like AI in the auto industry and the supply chain and more, to make business decisions and plan with confidence.

There are several AI-driven trends reshaping development and operations in 2025.

Predictive thermal control in EV propulsion systems

When we talk about optimizing the propulsion system in an electric vehicle, it helps to first break down what that system actually consists of. At its core are electric motors, batteries, and a suite of power electronics—including onboard chargers, DC‑DC or AC‑DC converters, and controllers—that work together to deliver power efficiently and reliably.  

AI is already accelerating battery innovation, but it is also driving advances across these other components.

For example, in electric motor design, AI is increasingly used in material selection, simulation, and multi‑variable modelling to boost power output, reduce weight, and enhance overall efficiency. These optimizations directly translate into better performance and lower energy consumption.

Another major shift is the move from 400‑volt to 800‑volt architectures. Earlier generations of BEVs typically used 400V motors and supporting electronics. Now, with technology advancing rapidly, many OEMs—particularly in China—are migrating to 800V systems, which enable significantly faster charging because of being able to handle flow of high-voltage currents with capable power electronics, improved thermal performance, and higher overall efficiency. 

Achieving these gains requires compatible power electronics and motor designs, areas where AI‑driven design and validation tools are proving indispensable.

ZF’s TempAI is a leading example of how AI is shaping these propulsion systems, already showing quantifiable boosts in efficiency, reliability, sustainability, and power. OEMs can unlock measurable performance gains by running A/B tests across standard drive cycles to validate AI-driven thermal control. Suppliers can accelerate adoption and add value by providing smart hardware integrated with sensors and analytics for seamless system optimization.

Accelerated battery design via AI-enabled platforms 

AI algorithms and automotive artificial intelligence are reshaping battery development from end to end. Engineers are using AI to improve thermal management and prevent risks such as thermal runaway, which can lead to fires.

Machine-learning models also guide the selection and optimization of cell chemistries, striking a balance between fast-charging capability and long-term durability. This allows batteries to accept high-voltage currents without accelerating chemical degradation.

Additionally, AI is helping to increase energy density, which delivers longer driving ranges without adding weight, and to refine recharge cycles so batteries last longer. These advances are not just about performance; they also shorten R&D cycles and reduce development costs. In markets like China, where new-age BEV manufacturers are leveraging AI to release new products at speed, established OEMs are being forced to accelerate their own innovation cycles.  

Factorial’s Gammatron is a strong example of this shift. It uses a hybrid physics + machine learning approach to simulate battery outcomes in days—and is already helping partners achieve up to twice the cycle life in lab tests. Suppliers can use this insight to inform cell chemistry roadmaps; OEMs can track life-cycle cost improvements by comparing predicted vs. actual battery behavior.

While battery design and development remain key, AI’s impact goes further. It’s shaping the entire battery ecosystem. AI optimizes supply chains for critical materials like lithium, cobalt, and nickel. It also boosts manufacturing efficiency by cutting production scrap. Additionally, AI improves recycling to recover valuable materials as EV batteries reach end-of-life. These applications highlight AI’s broad potential to make batteries more sustainable, cost-effective, and resilient.

If you’re an existing AutoTechInsight customer, simply log in to learn more from Factorial’s advanced modeling used to improve battery design. If you’re new to the AutoTechInsight platform, why not inquire about access?

AI-powered digital twins and generative simulation  

Across factories and vehicle systems, AI is enabling rapid scenario testing. Adding digital twins to R&D pipelines enables early-stage validation, with data-layered feedback loops that reduce downtime and development costs. Analysts can triangulate efficiency spreads across OEMs using these methods.

By aligning design and manufacturing with these new paradigms, companies can systematically reduce time-to-market and support data-intensive product lines.

AI technology is transforming the automotive industry, enhancing advanced driver-assistance systems (ADAS) with machine learning for real-time safety improvements and generative AI for personalized in-car experiences. AI is not only optimizing manufacturing processes, it also reduces time-to-market and encourages greater innovation in vehicle design.

How AI is driving transformation across the automotive value chain

1. R&D and simulation

As the industry moves toward autonomous vehicles, edge computing and reinforcement learning are becoming crucial for safety-critical functions. AI speeds up simulation processes, cutting evaluation times from days to just minutes, which leads to quicker design iterations and better vehicle performance.

In R&D and simulation, machine learning and generative modeling cut development cycles and costs. Platforms like Gammatron use predictive modeling to forecast battery life, reducing iterations from months to days. OEMs can strengthen these gains by pairing test-bench data with field data to validate simulation results, enabling more accurate performance modeling and powering predictive maintenance programs across vehicle fleets.

2. Manufacturing and supply chain

In manufacturing and supply chain operations, computer vision delivers real-time quality control, while predictive analytics streamline demand forecasting and reduce working capital. Suppliers providing smart manufacturing modules can stand out by offering analytics‑as‑a‑service models, and OEMs can track forecast accuracy by comparing AI projections with actual delivery performance.

3. Sales, marketing and customer engagement

On the commercial side, sales, marketing, and customer service are increasingly AI-driven. Dynamic pricing, lead qualification, and conversational agents help teams convert faster and support customers 24/7. Dealers and OEMs can track funnel efficiency and personalize offers to prove ROI, while predictive field-service alerts help boost retention. 

4. Diagnostics and service

Vehicle diagnostics and service benefit from AI-powered onboard systems that predict parts failures before they happen. This enables proactive service campaigns and creates openings for data‑sharing ecosystems between OEMs and suppliers to refine these signals.

5. Vehicle intelligence and in-car experience

Moreover, agentic AI enables real-time decision-making, predictive maintenance, and reduced operational costs for OEMs—while also delivering personalized, adaptive driving experiences for users. Combined with multimodal input methods like voice, touch, and gesture controls, these advancements create a more intuitive and engaging driving environment.

6. Architecture and regulatory considerations

The shift to centralized high-performance computing further improves the management of complex software functions and supports new business models, like subscription services for software features. However, the industry does face challenges in data management, privacy, and cybersecurity, highlighting the need for robust systems to comply with regulations such as GDPR and ISO 26262.

 Across each of these functions, AI is a foundational enabler—driving smarter decisions, faster cycles, and stronger customer outcomes. Consider visual diagrams or interactive tools to map these applications, linking directly to solution pages or internal content that showcases specific expertise.

The integration of artificial intelligence is redefining how vehicles are designed, built, and experienced. It is no longer just an operational tool—it is a strategic enabler driving efficiency, innovation, and customer value across the entire automotive ecosystem.

Smarter, faster manufacturing

AI-powered automation and robotics are optimizing production lines, reducing defects, and improving throughput. Algorithms trained on real‑time shop‑floor data can spot inefficiencies, guide predictive maintenance, and ensure consistent quality control—cutting costs and strengthening competitiveness. For example, AI-enabled inspection systems already detect micro‑defects that human checks can miss, reducing warranty exposure and scrap.

Connected vehicles and safer driving

AI is powering the brains of today’s connected cars. ADAS interprets sensor data in milliseconds, anticipate hazards, and trigger alerts or interventions that make driving safer. Beyond safety, in‑vehicle AI personalizes infotainment, learns driver preferences, and integrates seamlessly with mobile devices, elevating the driving experience.

Sharper supply chain decisions

In supply chains, AI-driven forecasting helps automakers and suppliers respond quickly to demand fluctuations, optimize inventory levels, and reduce lead times. By analyzing inputs such as market trends, weather patterns, and historical order data, AI is enabling more resilient and cost‑effective supply networks—critical in today’s volatile environment.

A new standard for customer engagement

AI is transforming how customers interact with OEMs and dealers. Virtual assistants answer questions instantly, predictive service alerts prevent unexpected downtime, and tailored offers make marketing efforts more relevant. These tools not only improve satisfaction but also build long‑term loyalty and measurable lift in conversion metrics.

Broader business impact

The influence of AI can shape workforce strategies by shifting routine tasks to machines and freeing people to focus on higher‑value engineering and data roles. It accelerates the entire vehicle lifecycle—from digital concept modeling to after‑sales support—enabling faster feedback loops and even circular manufacturing approaches. And it contributes to sustainability, using advanced analytics to monitor emissions, optimize material use, and support recyclability goals.

Data management and governance

Implementing AI in automotive software development certainly comes with its challenges, particularly in data management and intellectual property ownership within a fragmented supply chain. Navigating access rights to existing code and artifacts can be tricky, and ensuring data privacy is crucial when handling sensitive information from connected vehicles.

To tackle these challenges, having robust data management systems in place is essential. These systems utilize advanced encryption and anonymization techniques to comply with important regulations like ISO 26262, WP.29, and GDPR. 

Now, AI models are only as good as the data they learn from, and poor inputs can lead to poor decisions. Regular data audits and strong data governance are required to keep systems accurate and reliable.

Additionally, there’s increasing demand for automated data validation and machine learning pipelines to efficiently manage the scale and complexity of incoming data and AI model deployment. Scalable storage and high-performance computing are vital, along with targeted training programs and expert recruitment to make the most of AI technologies in the automotive sector.

Organizational complexity

It’s also important to navigate the complex decision-making structures within OEMs, as siloed organizations can slow down progress. By collaborating closely with OEMs, S&P Global Mobility can tailor AI solutions to meet a company’s specific needs.

Workforce transformation Another challenge is workforce displacement. As AI takes on routine tasks in design, manufacturing, and service, the need to upskill grows fast. OEMs and suppliers who measure the return on reskilling, through clear performance metrics, are better placed to keep teams relevant and engaged.

Cybersecurity and safety  

Cybersecurity is also a growing concern. More connected vehicles and smarter factories create more entry points for threats. Companies that openly map threats against mitigation plans—and publish clear compliance frameworks—show they take security seriously. 

S&P Global Mobility’s recent Connected Car Study revealed that Safety and Security had the highest average prices that respondents were willing to pay for, confirming the value of these features. As ethical and safety questions remain, autonomous systems raise tough liability issues. Using AI‑enabled traceability tools creates a clearer chain of accountability and a stronger audit trail when it matters most.

However, high upfront investment can be a sticking point as AI platforms often require significant early spend before results are visible. Building comparative ROI models, informed by real deployment case studies, can help to justify that investment and set expectations.

These challenges are also opportunities to choose partners who know how to manage risk, extract value, and guide the industry through an AI‑driven future with confidence.

FAQ – AI in the automotive industr​​y