Generative AI has been very effective in terms of providing prescriptive analytics to prevent plant equipment failures and breakdowns, thereby reducing the need for an unplanned repairs and replacements.Generative AI is revolutionising the automotive sector by driving efficiency, enhancing recyclability, and minimising environmental impact. Advanced AI tools have the potential to transform every stage of a vehicle's lifecycle—from conceptualisation and design to manufacturing, testing, operation, and end-of-life processing. By enabling material and design optimisation, improving energy efficiency, and supporting sustainable manufacturing practices, Generative AI empowers companies to develop environmentally responsible vehicles with a reduced ecological footprint throughout their lifecycle.
Beyond sustainability, Generative AI is also enhancing user experience by enabling more natural and intuitive interactions with vehicles. Through voice commands and contextual understanding, advanced AI algorithms allow seamless communication between driver and vehicle. By analysing driving patterns and personal preferences, it can anticipate user needs and proactively deliver relevant features—such as suggesting optimal routes, adjusting cabin temperature, or managing in-vehicle infotainment —creating a smarter, and more personalised driving experience.
Says, Jeffry Jacob, Partner and National Sector Leader for Automotive, KPMG in India, “Generative AI is helping bring in innovation and efficiency by assisting companies to create more advanced, lightweight and optimised designs.” Some of the major areas where Generative AI is already used widely include rapid prototyping, optimizing performance to meet the required design/safety standards, as well as lightweighting (especially for electric vehicles).
“It helps reduce the time needed from design to prototype through rapid iterations. It also helps improve safety and performance by evaluating designs for crash optimisation systems, more effective thermal management for EVs and improving battery performance, as well as better integrating smart features in connected vehicles,” shares Jacob.
Highlighting that Generative AI can help drive material optimisation through improved prototypes, Jacob expresses, “At the initial stage itself, it can help design components with minimal materials thereby reducing raw material costs and resource utilisation. It can help reduce waste via design of products considering end-of-life recyclability, as well as optimised production processes which reduce scrap during cutting and machining. Dynamic resource and supply chain management can help reduce energy consumption and optimise inventory.”
Yogesh Deo, EVP & Global Head – DES Delivery, Tata Technologies, states, “Generative AI can contribute in a big way to create digital prototypes that can be virtually tested, reducing the need for physical prototypes and accelerating the design process. It can generate personalised designs based on individual preferences, enabling mass customisation without compromising efficiency. AI-powered tools can create modular designs, allowing for greater flexibility and customisation in product configurations.”
Indicating that Generative AI enables dynamic, adaptive production systems by optimising manufacturing workflows in real time, Deo adds, “Generative AI is evolving the maintenance and diagnostics systems by analysing vast amounts of data to identify trends, anomalies, opportunities for improvement, by optimising maintenance schedules and resource allocation, thereby reducing maintenance costs and improve overall equipment reliability.”He also highlights that Generative AI can accelerate research and development in sustainable materials, enabling businesses to create and deploy sustainable solutions faster.
Generative AI has been very effective in terms of providing prescriptive analytics to prevent plant equipment failures and breakdowns, thereby reducing the need for an unplanned repairs and replacements. Moreover, it can also identify potential disruptions in the supply chain, such as natural disasters or geopolitical events, and develop contingency plans to mitigate their impact.
Reflecting on the role of open car operating systems in advanced software-driven vehicles, Deo explains, “Generative AI can enhance the flexibility of open car operating systems through rapid application development, seamless integration of diverse software components and services, and by ensuring a smooth, cohesive user experience.”
Generative AI is poised to play a pivotal role in the adoption of software-defined vehicles (SDVs), where millions of lines of code enable greater flexibility and responsive solutions for consumers. It can accelerate the creation and optimisation of software and control systems while also improving the performance and efficiency of a vehicle's hardware. This integration of AI-driven software and intelligent hardware is expected to drive innovation, offering smarter, more adaptive automotive experiences.
However, there are also concerns about ensuring the privacy and security of user data, ethical considerations where AI algorithms must be designed and trained responsibly to avoid biases and discrimination. AI-powered systems must be highly reliable and robust to ensure safety and prevent accidents. “AI models can sometimes exhibit unexpected behaviour, especially in cases with unfamiliar situations. They often require large amounts of data, including personal information, which could be vulnerable to breaches. The use of AI for surveillance and data collection raises concerns about privacy and surveillance,” explains Deo.
