Our perspective
Our perspective
''With enough data auto makers can promise buyers that as they get used to their new car, their new car will get used to them. That’s a powerful selling proposition.''
Automobile manufacturers face two great challenges: electrification and softwarization. The global industry rose to the electrification challenge with innovation after innovation. Globally, 14% of new cars sold in 2022 were electric, almost triple the number sold in 2020, with China accounting for more than half of all electric vehicles sold worldwide. In the U.S., nearly 6% of new cars purchased were electric, doubling the amount sold in 2021. At least one study has suggested that 5% is a tipping point, after which sales speed up.
The next big challenge that’s coming fast is softwarization: the use of a software solution, rather than traditional hardware, to solve a problem. This is perhaps an even harder problem because it requires a fundamental shift in how manufacturers think about themselves. Companies with a rich history of producing brilliant pieces of hardware may find it hard to imagine themselves as a computer or an AI company. But, as the auto industry moves closer to making smart devices with wheels, they must.
Globally, auto makers are already investing billions in AI, and that number is expected to grow at a compound annual growth rate (CAGR) of 22.7% from 2023 to 2030. To date, AI is routinely used in product design and planning, warranty management, building long-term customer relationships, and more.
The next frontier is Generative AI, which can create code, digital content, simulations, and more. As the automotive industry progresses towards smart devices with wheels, manufacturers must embrace tools that empower them to quickly develop, deploy and maintain software — and that’s exactly the power that Generative AI brings to the table.
Tesla is the only domestic car manufacturer that is a tech company first and a vehicle manufacturer second. They are already the biggest AI company on the planet — bigger than Google, Amazon, or Microsoft. Their capabilities are completely grown in-house, and that makes an enormous difference. From a softwarization perspective, they have built a significant lead.
As a software company that makes cars, they have been leveraging AI at fleet-scale to develop new innovations faster. They recently announced they’re leveraging a state-of-the-art generative modeling technique enabling them to predict possible outcomes given past observations, in a jointly consistent manner across multiple camera views. That’s the big promise of Generative AI: to relieve the code department chokehold on innovation, making it radically easier to turn an idea into a software feature without having to rely so heavily on an already overburdened coding department.
There’s competitive pressure from China, too. About 1.4 million Chinese engineers are qualified annually, six times as many as in the US, and at least a third of them in AI. As the Japanese business daily Nikkei Asia noted, “China is the undisputed champion in artificial intelligence research papers … far surpassing the US in both quantity and quality”. Baidu, Alibaba and JD.com all have Generative AI services that are either in the trial stage or being tested by corporate users. US Senator Mark Warner has warned that China is “very much ahead of the game”. And don’t forget that Chinese vehicle manufacturers like BYD are 70% to 80% vertically integrated across their entire supply chain, vs. the US which is less than 25%. They’re able to manufacture batteries for far less, which gives them far greater latitude in pricing their vehicles aggressively.
What does that have to do with Generative AI? Everything. Less expensive vehicles mean more vehicles on the road, which means more data.
China is keenly aware of Generative AI’s potential to drive powerful efficiencies in softwarization. What’s more, they have the technology base and engineering talent to translate that potential into reality. Affordability in vehicles always matters, and softwarization will be key to delivering value at the time of purchase — and increasing the residual value as the vehicle ages.
There’s a lot that automobile manufacturers can do to update their vehicles’ capabilities over time with software, but ironically hardware is the limiting factor: vehicles need to ship with larger computing power than the bare minimum.
This would enable them to operate a software sandbox in every vehicle they ship, using it to analyze the performance of their software against the human baseline of usage.
It would enable them to test new functions in the real world, on a limited set of vehicles.
Manufacturers could boost their ability to experiment with new ideas, while gathering invaluable data about how the new software features work in the real world, in real vehicles with real drivers. All of this would mean they can turn new features around faster, with failure becoming less of a risk.
With a more robust computing environment and Generative AI, they could enable vehicles that analyze driving behavior and personalize and optimize the driving experience for different drivers. With enough data they can promise buyers that as they get used to their new car, their new car will get used to them. That’s a powerful selling proposition.
By providing enough computing space for innovation, manufacturers could leverage vehicle-specific data to provide superior customer support, enabling service representatives to understand the user experience better — and push customized messaging out to the vehicle owner via email, text, and maybe one day to the dashboard itself.
Modern vehicles are essentially computers on wheels, with their computer platforms made of high performance, high speed connected ‘vehicle servers’, executing over a hundred million lines of code. Integrating and updating these systems over the supply chain of existing and new partners is critical to safety, longevity, durability, sustainability and performance. Studies show that a comprehensive yet agile approach to automotive software can increase speed to market by 20%, while digital twins and integration testing can reduce costs by 30%.
As the automotive industry races toward its future, software and connectivity are redefining the next generation driving experience. This transition challenges traditional manufacturers whose hardware-focused culture doesn’t align with the needs of a software-centric world. Today, the software engineering department at most auto makers is chronically oversubscribed and under resourced. This is a challenge, but for forward-thinking auto makers it’s also an opportunity.
Wipro – a leader in engineering services for software-defined vehicles (SDV), cloud and automation – has both the in-house capabilities and the Cloud Car ecosystem to help bridge the gap. With regard to Gen AI, Wipro's Engineering Edge business line is aggressively piloting Code LLMs for developer productivity, while training the next generation of Gen AI engineers. We’re also building the industry’s first automotive Gen AI Engineering Lab with Cloud Car 2.0, which will combine Design Thinking methodology and AI accelerators / AI infrastructure at the hardware and silicon level.
Wipro partners with OEM’s and suppliers to accelerate their growth through cloud-native engineering principles. By combining traditional solutions with innovative platforms, our Cloud Car ecosystem and SDV expertise enables the auto industry to reimagine how safety and entertainment features are developed, deployed and maintained — ultimately delivering a new car every day.
The future of the automotive industry is amazing, and the new tools that are now available have the potential to make meaningful innovation faster and easier than ever.
The time for Generative AI in automotive is here.
Thomas Mueller
VP, CTO and Automotive Lead, Wipro Engineering
Thomas is a Vice President in Wipro Engineering and has more 30 years of experience across the automotive, cloud, and financial sectors. He is currently responsible for Wipro’s automotive engineering and innovation services, which encompasses everything from software-defined vehicles and autonomous driving to 5G and cloud-native engineering.
