Artificial Intelligence

Automated driving is back on the industry’s front burner, a resurgence led primarily by advances in artificial intelligence and marketplace success, industry insiders said at CES 2026 in Las Vegas in early January.

Automaker interest in pursuing top-tier Level 4 and 5 automated driving cooled in recent years as the technology hit some speed bumps, OEMs backed away from mobility-service schemes and available product-development dollars largely were directed toward battery-electric vehicles instead.

But with a policy-driven slowdown in the U.S., continued cost disadvantages compared with internal-combustion-engine vehicles and a consumer base still reluctant to make the switch to full-electric vehicles in big enough numbers, industry investment has begun to flow away from BEVs and back toward automated driver-assistance system technology.

But it isn’t just the lower boil in BEV activity that is sparking renewed ADAS interest, executives told WardsAuto.

“I think automakers worldwide are seeing the benefit automated driving can bring,” said Rajat Sagar, vice president-product management for the Snapdragon Ride Pilot platform (developed with BMW) at chip and software-solutions provider Qualcomm.

Automakers in China and others such as Tesla have been deploying ever more-sophisticated ADAS capabilities as a way to entice consumers, Sagar noted. “That’s what is fueling the need for these implementations.”

Why automakers are investing more in ADAS

C.J. Finn, U.S. automotive leader for consultant PricewaterhouseCoopers, contends automakers now see being among the first to offer higher levels of automated driving as the best near-term opportunity to gain market share.

“There’s a constant fight of what’s that [next] technology where if you’re the first mover you can really capture market share,” Finn told WardsAuto. “Some thought electrification may have been it, but autonomous is that next thing ... When you move from Level 2-plus to Level 4, that’s the game changer.”

Vlad Voroninski, CEO at California startup Helm.ai, agrees the money is once again flowing into automated driving.

“OEMs [realize] the main revenue driver is going to be in assisted driving for commercial vehicles. That’s where the capital's flowing,” he said. “It is probably the No.1 topic in the boardrooms of automotive companies.”

Even in the U.S. market, there’s evidence buyers are beginning to see a real demand for the technology from consumers. Researcher AutoPacific said in July that 43% of U.S. car buyers point to hands-off semi-autonomous driving for highway use — Level 2-plus systems such as General Motors’ Super Cruise, Ford’s BlueCruise and Tesla’s Full Self Driving — as the technology they want most in their next vehicle. That marked a hefty 20-point increase from a year earlier.

 A 2023 McKinsey study showed two-thirds of consumers would be willing to pay a $10,000 premium for Level 4 automated driving technology (hands off and eyes off on both highway and urban roads) on their next vehicle. Precedence Research predicts the autonomous-vehicle market could top $2 trillion annually by 2035, growing more than 35% per year between now and then.

How GenAI is helping build the momentum

Making all this possible is the rapid advancement in technology — thanks in big part to the leap in AI capability that has altered the approach to self-driving software development, and the improvements in — and proliferation of — sensor technology that is bolstering the quality and volume of data required to perfect ADAS operation. Mobileye, for example, said it collects data in real time from some 8 million vehicles globally, harvesting 32 billion miles (51 billion km) of data in 2025. 

“What we’re doing today isn’t something we just decided to do yesterday,” said Manuela Locarno Ajayi, a product-tech executive with TomTom, noting with half the cars on the road now connected, there’s a wealth of real-world performance data available for developers to help fine-tune their software. “We’ve been trying to do it for a long time. Only now the technology is catching up to be scalable and cost-effective [and] to have this level of precision.”

TomTom, which used CES 2026 to show off its next-generation high-definition mapping for navigation systems it is supplying to Volkswagen’s CARIAD software-development group, among others, said it now can map 470,000 miles (750,000 km) per day thanks to the advances in AI technology and the growing amount of real-world data available.

“I think the technology is closer to being ready,” Jagan Rajagopalan, head of strategy and portfolio for integration and software specialist Elektrobit, said in explaining the recent surge in ADAS interest from automakers. “It’s just AI being ready; the lidar, the cameras and technology getting more standardized. In 2030, you’ll begin to see more mainstream autonomous vehicles.”

The arrival of Generative AI and development of visual language models has changed the way automated-driving software is being written and tested, industry insiders said. With VLMs, AI software now is capable of understanding and acting on image inputs, rather than just language.

“Overnight we can train [virtually] with data that no real-world data can match,” said Mobileye CEO Amnon Shashua, adding Mobileye can run billions of simulations a day.

“The models you’re seeing now are designed to be end-to-end models,” said Nakul Duggal, executive vice president and general manager of automotive for Qualcomm, meaning the software thinks more like a human driver and can analyze the scene and determine the vehicle’s path much more quickly now. “That evolution in AI has only happened in the last three to three-and-a-half years. The data has existed but the visual-language models didn’t.”

 “As you train those newer models, you get much more robust outcomes,” he said. “Once you have the recipe in place, which is not easy to get to, then its [simply] a data-driven obstacle.”

In earlier attempts at solving autonomous driving, Helm.ai’s Voroninski said: “You would have a very serious ceiling on what you could do. You might never be able to offer Level 3 or Level 4 features with that approach.”

Now, with its perception stack said to function more like a human brain, there’s line of sight to the ultimate goal.

“Seventy percent of your brain is fully dedicated just to being able to perceive the world,” Voroninski said. “That’s a problem we’ve spent years solving and have effectively solved from an automotive production-grade perspective.

“We've shown that we can actually train an autonomous driving system that can do 20-minute drives in cities it's never seen before, because we have this production-grade perception stack.”

NVIDIA President CEO Jensen Huang summed it up succinctly. “You no longer program the software, you train the software,” he said, pointing to his company’s new Alpamayo open-source platform launched at CES 2026 that underpins the automated drive stack deployed on the Mercedes CLA. He says $100 trillion of global R&D has shifted to AI from classical methods of product development in the last few years.

As costs fall, ADAS could be built for the masses

While the technology is improving, costs are declining, meaning higher-end Level 2-plus capability will begin to filter down to more mass-market vehicles. Ford’s move to develop its own technology in-house is just one example of the drive to get Level 2-plus functionality into entry-level vehicles.

Mobileye, for instance, said the program for its Surround ADAS platform with a U.S. automaker beginning mid-2028 will cover up to 9 million vehicles. That comes on top of a similar program Volkswagen announced in 2025 that is expected to include up to 10 million Surround ADAS-equipped vehicles.

“This is a very cost-efficient solution,” said Nimrod Nehushtan, executive vice president-business development and strategy for Mobileye. “These are two of the top six automakers in the world that are sourcing their high-volume models with this solution. It shows there’s a trend, that there’s a lot of focus on the cutting-edge performance in the premium segments, but there is also an interesting development coming from high-volume cars.”

Qualcomm says its Ride Flex is another less costly platform gaining traction. It can combine cockpit infotainment functions and ADAS controls on a single system-on-chip, making it well-suited for lower-priced vehicles. Its first production application debuted in 2025 on several Chinese models, including BAIC’s ARCFOX Alpha T5, General Motors’ Buick Electra L7 and Dongfeng Nissan’s N6, and Qualcomm says it has more than 10 partners with programs in the works utilizing the Ride Flex SoC, including a collaboration with Hyundai Mobis.

“What this means is, you are starting to see the implementation of ADAS and [in-vehicle infotainment] across almost every tier of vehicle,” Qualcomm’s Duggal said. No vehicle segment is being left out of the movement for Level 2-plus and above autonomy, “which increases the market size for us and everybody else,” he added.

Costs are certain to come down further as volumes rise, executives contend.

Shashua said Mobileye is targeting a 40% cost reduction for its L2-plus Surround ADAS system in 2028 and will continue to lower costs of its entire suite of technology beyond that. With its Chauffeur Level 4 software VW is deploying in its MOIA fleet, Mobileye is looking to slash costs by reducing the number of lidars required from three today to just one.

After years of promise, some developmental stumbles and on-and-off interest and investment from global automakers, it appears automated driving is gaining a solid foothold.

“I’m very certain in the next 10 years a large percentage of cars will be autonomous, or highly autonomous,” NVIDIA’s Huang predicted.

Artificial intelligence is changing the way consumers shop for cars, and dealers who take advantage of AI can use it to make those consumers more satisfied.

But that just scratches the surface of the impact AI can have on a dealership’s operations, industry experts told WardsAuto. From the showroom and service departments to used car operations, AI can give dealers new tools to sell and source more vehicles as well as keep customers coming back for service.  

“I think AI is one of those fundamental technologies that is going to change the way we are going to consume cars, buy and own and sell,” Arun Kumar, a partner and managing director in the automotive and industrial practice at consultancy AlixPartners told WardsAuto in a Zoom call.

Dealers’ first AI investment should be on the front end of dealership operations, to enhance sales, Kumar said.

When a customer arrives at a dealership, AI can help a dealership maximize the chance of converting that visit to a sale by making sure the salesperson has accurate data reflecting what the consumer has already learned online, he said.

That not only helps drive more throughput, it reduces the number of people needed to make the sale happen so the dealership sells more cars with fewer people. “I think that, to me, is pretty number one,” Kumar said.

AI can also make a dealership’s salespeople more effective communicators.

Brendan Harrington, president of dealership group Autobahn Fort Worth, told WardsAuto on a Zoom call that he is working with a developer on a prototype AI agent that provides prompts for salespeople.

As a customer asks questions, the “AI angel” shows the salesperson answers to straightforward questions such as, “Is that blue Toyota Camry still available?” as well as potentially unexpected questions like, “How big is the trunk?”

“So, you sound really intelligent,” Harrington said.

Communication is the most common AI usage at dealerships, Erin Lomax, Cox Automotive vice president, operations, consumer marketplace & AutoTrader, told WardsAuto on a Zoom call.

“I think it's one of the easiest and most common entry points to understand, like, ‘OK, how can I automate these communications that were once manual?’” she said.

Cox Automotive’s Auto AI Readiness in Auto Retail Study, conducted in three phases between April and August of 2025, found that 52% of the 537 franchised dealers who participated were using AI to engage with customers 24/7 using real time text, chat or email.

Another 48% were using it to create personalized email and text while 39% used AI to predict which consumers are ready to buy and target them with personalized messages.

AI is a “key enabler to removing friction out of employee, dealer and consumer processes,” Lomax said.

The case for AI in the service department

Given the number of customers in a Dealer Management System, and a dealership’s weekly volume of service appointments, “it’s pretty difficult to overstate the ROI of AI in fixed operations and service,” Devin Daly, co-founder and CEO of AI service platform Impel, told WardsAuto on a Zoom call.

Impel mines the DMS to find customers who are due for service and contacts them with personalized offers, he said. It schedules appointments, sends follow-up messages, and answers all voice mails.

“It provides a significantly better customer experience,” Daly said.

Numa, another AI-enabled software, uses AI to centralize service department communication with customers, picking up missed calls, routing calls to the correct service advisor and making appointments

Stocking the right used inventory

AI is also infiltrating the dealerships’ pre-owned operations, helping them decide what used vehicles to acquire.

CDK’s Vehicle Inventory Suite includes AI that uses CDK’s “vast amount of data” to help dealers make better purchasing decisions, Dan Doolin, lead product marketer at CDK Global, told WardsAuto on a Zoom call.

If a dealer is considering acquiring a used vehicle on trade-in or at auction, AI can see if the model sold well at other dealerships and show what the retail and wholesale pricing has been for the past six months as well as where it is predicted to go for the next 90 days, Doolin said.

“It can help a dealer determine a unique vision of what is going to happen with the pricing related to the vehicle,” he said.

It’s all about the data

While 74% of dealers in the Cox Automotive AI Readiness study thought AI “has the potential to generate more benefits than risks for my dealership,” 25% are still taking a “wait and see” approach to AI adoption.

The dealers want to trust they are getting compliant solutions that adhere to privacy and anti-discrimination laws, Lomax said. Crucial to that is what data the AI is using, she said.

“There’s a ton of companies out there pitching these great solutions,” Lomax said, “but where is the data coming from?”

The use case can also be different depending on the dealer size and segment, she said.

“But the underpinning of all of that, you know, what you continue to hear and read about, is that the data quality is what matters the most, right? And making sure of that because AI is only as good as the data that fuels it,” Lomax said.

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Just-in-time (JIT) manufacturing principles have been a cornerstone of automotive supply chains for many years. They have allowed original equipment manufacturers (OEMs) and their down-tier suppliers to minimize inventory carrying costs, reduce waste and maximize capital available for inventory and growth.

But fast forward to today, and the JIT model has begun to show vulnerabilities. To remain resilient, organizations must evolve beyond conventional frameworks. Smarter systems must incorporate predictive analytics, real-time visibility and agile logistics to anticipate problems before they disrupt operations.

“The recent surge in market volatility has pushed OEMs and suppliers to invest in more digital tools to enable proactive decision-making and reduce risk,” says Rakesh Puri, vice president, marketing (global verticals) at FedEx.

How predictive analytics enable real-time visibility

Traditional supply chains lean heavily into historical data to make decisions, leaving them vulnerable to risks that do not conform to historical trends. This model cannot account for real-world factors as they happen.

Advanced analytics allow manufacturers to identify potential disruptions, such as weather events, capacity constraints, or geopolitical events—before shipments get affected. By gaining a better understanding of risk patterns and incorporating real-time threat data, supply chain teams can develop contingency plans that do not require significant excess inventory.

According to Puri, FedEx is investing heavily in this area to help OEMs and suppliers transform their supply chains. “The Global Delivery Prediction Platform enhances customer experience by shifting from reactive tracking to proactive transparency, providing two-hour delivery windows, confidence scores and delay risk alerts that help customers plan with confidence,” he says.

How AI drives automated rerouting and intervention

When delays occur, the severity of the impact on the supply chain depends on how quickly the manufacturer can address them. To this end, automotive stakeholders must shift from reactive measures to the more predictive capabilities offered by enterprise data platforms. Combining automated systems with visibility into delays caused by bad weather, customs bottlenecks, or other factors enables manufacturers not only to reroute shipments automatically when a delay occurs, but potentially to reroute them before the delay occurs at all.

“This capability is evolving into dynamic orchestration, where AI not only predicts outcomes but actively optimizes them in motion,” Puri says.

Adapting to the next generation of JIT

In a world defined by volatility and complexity, efficiency alone is no longer enough. The old ways are being supplanted as the philosophy moves to keep pace with the needs of a modern automotive supply chain.

For OEMs and automotive suppliers planning for this new normal, the time for waiting and seeing is over. Those who prioritize reducing the impact of supply chain disruptions and building a more robust infrastructure must achieve enhanced visibility and improved predictability across the value chain.

“Step back, evaluate the current environment and leverage AI and visibility tools to improve predictability and more accurately forecast parts required for production plants and aftermarket dealers,” Puri says.

Logistics and supply chain partners who can deliver foresight via transparency, trust and technology will define the next decade of the automotive supply chain. The most successful automotive supply chain operations will be those that can anticipate disruptions and respond before they crystallize into delays.

As a true strategic partner, FedEx creates a digital and operational bridge between suppliers and OEMs. To see how we can help you build a more modern, resilient automotive supply chain, please visit fedex.com.

American Honda laid out a generative AI strategy in 2023, focused on immediate risks, bringing capabilities in-house, training and long-term planning. As the company advanced on its broader goals, executives increased the focus on responsible AI and the data foundation that powers the technology.

“We’re trying to improve our decision-making speed and our decision-making quality at every level of the organization,” said Bob Brizendine, SVP of IT at American Honda and deputy head of the digital unit in the corporate administration operations at Honda Motor. “The reason we put brakes on a car is so we can go safer, faster, and data governance and AI governance are really in that same domain.”

As enterprises invested in generative AI, adequate governance became critical to reach AI ambitions. Poor frameworks can lead to a legion of roadblocks, from increased costs and subpar performance to project delays and biased results.

To up the ante, strengthening governance isn’t a one-time affair. Plus, aiming for what the industry has labeled as AI-ready data is a “mythical” endeavor, Brizendine told CIO Dive. 

“I really do use the word mythical because I don’t know how to measure exactly when data is ready for AI, or for what decision,” Brizendine said. “It’s a tall order. It comes down to the individual use case, the risk of the decision being made and the importance of that decision.” 

American Honda has worked to get its arms around data and AI governance by establishing a responsible AI council, creating a unified data platform and fostering digital literacy.

The council, composed of representatives from various business units, keeps an eye on model drift, privacy-related compliance obligations and how AI-generated outputs are used. The group advises teams on expectations around AI use and when to use what model. Around mid-year, Brizendine said the group will evaluate how it’s executed and adjust if necessary.

To support the council’s efforts and the overall strategy, American Honda employees are participating in digital literacy training. The automotive manufacturer created a program with research and consulting firm Gallup to establish a digital literacy metric to see where teams stand. 

“If we know the digital literacy of a team, we can target the education programs for their level, whether it’s the executives who need to learn how to drive innovation using these capabilities or the staff,” Brizendine said. 

Strengthening the data engine

In addition to its governance efforts, American Honda has elevated data ownership and worked to democratize AI and data tools across the organization in recent years.

Enterprise AI efforts have amplified data’s importance within most organizations. More than 4 in 5 technology decision-makers said data ownership has shifted over the last year as adoption initiatives expand, according to a Collibra and Harris Poll survey published in May 2025.

American Honda was an early adopter of Microsoft’s Copilot and rolled it out broadly in 2023. Since then, the company has explored AI tools tailored to several business units and tasks. The automotive company has also worked to build out a unified data platform and accelerate its data maturity, fueling its generative AI endeavors. 

“This is where all of these things tie together,” Brizendine said. “We’re making a pretty large play for building a unified platform for some of our strategic elements of our data: integration of various data, ensuring we have seamless data flow. We’re working on that in a priority order by data domain.”

As the organization continues to incorporate unstructured data — such as owners' manuals — and increases the quality of structured data, it’s identifying room for improvement. 

“When we talk about quality or defect information in manufacturing, if you don’t have a common language, it becomes difficult to socialize and look beyond the boundaries of one particular plant,” Brizendine said, giving an example of a use case that’s not quite ready yet. 

American Honda’s Arun Yarlagadda, deputy general manager of global services and data platform and North American chief data officer, told CIO Dive that scrapping a potentially valuable use case is not as common as putting it in the not-for-now pile. 

“If we are using photos of a factory to chart some next-best action and the quality is not good, we would work on improving that source,” Yarlagadda said. “ We didn’t just turn away, but we have to postpone the activity.”

Long term, Yarlagadda said having the right talent, observability and monitoring capabilities will continue to be vital for its success. 

For organizations that get data and AI governance on the right track, technology leaders will need to maintain their diligence — and keep an eye on the horizon. 

“Data and AI are twin engines driving our digital transformation,” Brizendine said. “Keeping the finger on the pulse of this changing world is very difficult, but we’re investing more in advanced technology planning and proofs of concept to try to be ready.”

NOVI, MICHIGAN — With the auto industry’s shift toward building more connected vehicles powered by software continuously updated over-the-air, OEMs are rapidly moving from hardware-centric vehicle development processes to a software-first approach. This pivot also includes the integration of AI and adoption of a cloud-based development environment for software-based vehicles.

However, to support this transition, legacy automakers still face challenges in data management and technology integration, according to a recent panel discussion on the topic at the AutoTech 2025 conference in Michigan. 

The panel, which was moderated by Maite Bezerra, principal analyst for software-defined vehicles at Wards Intelligence, included industry experts from Bosch, Stellantis, Toptal, and the Scalable Open Architecture for Embedded Edge (SOAFEE) industry group, which is working with automakers to expedite development of software-defined vehicles. 

SOAFEE aims to create an open source vehicle platform using cloud-native architecture that supports multiple hardware configurations. 

A shift toward a cloud development model

“SOAFEE is really kind of more about bringing some of the modern software techniques to automotive software development,” said panelist Robert Day, the group's governing body representative. “Over the last couple or three years, people are actually starting to do their development in the cloud using the tools, technologies and methodologies that are well developed and well used in cloud development.”

Although adopting a cloud-based software development approach is a common practice for developers working in the tech space, it's an entirely new field for some legacy automakers. 

"The problem is the car is not the cloud,” said Day. “It has things like safety and things like mutual physicality, heterogeneous computing.” 

The software development challenges for automakers also create the need for OEMs to recruit top talent to integrate the technology into next-generation vehicles, often from outside of the industry. Some companies are providing services to expedite such recruitment. For example, Toptal operates a freelancing platform that connects companies with in-demand software engineers and other technology specialists. 

“We have a lot of partners in the automotive space,” said panelist Paul Timmermann, VP of product at Toptal. 

Stellantis is one of the automakers encountering the challenges of shifting towards SDVs for its future vehicles. 

"We [automakers] are always hardware first, and now the switch is happening to, you know, software, and then comes the hardware," said panelist Sangeeta Theru, director of virtual validation platforms at Stellantis. “Tools, processes…everything is changing,” she said. 

Theru also highlighted the importance of training internal teams at Stellantis, adding that the automaker recently launched "big training on AWS cloud and architecture” for employees. “There was a lot of effort in upskilling and training internal people,” she said.

Automakers building SDVs must be adept at managing large data sets

A major driver of increasing vehicle complexity is automakers launching more advanced driver assist systems and autonomous driving functionality using AI-powered software, according to the panelists. 

Vehicles with automated driving capabilities, for example, are equipped with dozens of cameras and sensors, generating "many, many terabytes of data" for a single car, scaling to "well beyond petabytes" across large fleets, explained panelist Steven Miller, product management of ADAS and technical expert at Bosch. 

“Clearly you're not going to upload all of that data,” he said. “The other even harder data problem is okay, what's the right data to upload to the cloud?”

With rollout of more advanced autonomous driving features, automakers need to be adept at processing and merging extremely large data sets. One of these challenges is processing high volumes of vehicle data in real-time, as well as making it more manageable to transfer to and from the cloud. 

Automakers must also decide which vehicle data to upload to the cloud to train AI models. Therefore, the panelists emphasized the need for OEMs to create efficient data pipelines to manage this complexity. The panelists also foresee AI being integrated into other vehicle systems, such as remote diagnostics and infotainment. The use of AI will also likely extend to corporate organizational processes.

"This is one of the most transformational shifts that we are seeing in the automotive industry," said Bezerra.

Using software with a higher degree of standardization

The panel discussion also delved into automakers’ adopting open source software with a higher level of standardization to reduce development times and costs.

In November 2024, Panasonic Automotive Systems and Arm announced a collaboration to standardize automotive architecture. The two companies said they recognized the need for the industry to shift from a hardware-centric to a software-first development model to address challenges created by high-cost, vendor-specific proprietary interfaces for vehicles. 

While the use of open source automotive software has traditionally been met with caution due to safety and liability concerns, an April 2025 report from the Eclipse Foundation found a significant jump in industry appetite to use it for safety-critical vehicle systems. According to the report, 79% of automotive software professionals currently use open source tools and/or in-vehicle software for development, and the number of users actively contributing to open source projects increased by 4% from 2024.

The big advantage of open source is it provides a standard between companies, explained Day. “If you're starting to use open standard or open source, it makes that collaboration easier,” he said. 

Day also highlighted another long-term strategy decision facing OEMs. "What would you choose to open source first? What would you actually keep in-house?" he said.

Despite the prospects of adopting open source software for vehicles, the panelists acknowledged that some key areas needed more attention, including cybersecurity. This area is even more critical for automated driving and connected infotainment systems that can be used to pay for goods and services, such as EV charging sessions. 

Day raised a critical point about security. “I don't think it's placed enough attention to, and certainly don't think [automakers] spend enough money on it,” he said.

According to chipmaker Arm, a modern vehicle can have up to 650 million lines of code, and this number will only increase in the future. But software will revolutionize how drivers interact with their vehicles and redefine the relationship between OEMs and vehicle owners, according to the company. 

AI is beginning to transform the automotive design process by allowing designers to rapidly ideate, sketch and render new designs, experts say.

Until recently, designers had to spend several weeks or months sketching before refining and rendering those images in 3D. 

The experience can be frustrating for designers because of the immense manual labor required to produce dozens or hundreds of drawings that differ “in the tiniest ways,” said Alan Macey, an automotive design and brand consultant and faculty member at the ArtCenter College of Design.

However, Vizcom, a cloud-based design tools platform, now uses AI to quickly generate images based on user prompts. This enables designers to move from sketching to refining within days or even hours in some instances, according to Raphael Zammit, associate professor and chair of the master’s program in transportation design at the College for Creative Studies. 

While the AI-generated images are often imperfect, those imperfections would be undetectable to “the untrained eye,” he said. 

“The computer will get any untrained person 95% of the way there,” Zammit said. “You’re just going around cleaning stuff up.”

From 2D to 3D

AI-powered design tools fast-track the process of transforming 2D drawings into 3D renderings, which can be “time-consuming,” ArtCenter’s Macey said. 

Typically, when designers have an idea, they must draw several 2D images in multiple views, such as a front three-quarter view, and combine them to create a 3D model. 

It’s “one of the hardest things we have to teach our students,” Macey said. “It’s a very challenging thing to master.” 

The latest AI image generators, however, can create 3D models “instantly” because they have been programmed extensively with massive data sets, including photos of vehicles, CCS’s Zammit said. Vizcom, for example, can create a 3D model using a single image, such as a side view, and one additional reference point, like a center line.

“All you have to do is take that model, turn it and draw on it to make some corrections,” Zammit said. “It renders as you draw on it.”

Is AI the future of automotive design?

Automakers, suppliers and design studios will increasingly rely on AI to design vehicles and components, experts say. 

So, how could AI transform the automotive design process in the future?

“We have no idea,” Zammit said. “Every three months, that question has a new answer. The development is exponential.”

It’s also unclear how AI will affect demand for designers because the technology is changing quickly. According to the U.S. Bureau of Labor Statistics, job growth for industrial designers will grow 3% from 2023 to 2033.

AI-powered design tools have the potential to give a trained designer “superpowers” or eliminate the design process entirely, depending on how the technology develops and how businesses implement it, Zammit said.

“I think the answer will be somewhere in the middle,” said Jon Hull, CEO of Hydra Design Labs, an automotive design studio.

Currently, a manufacturer’s engineering, design, planning and marketing teams collaborate to realize an idea, CCS’s Zammit said. 

There is also a “direct correlation” between people who are good designers and those who can get the most out of today’s AI-powered design tools, ArtCenter’s Macey said.

“You can’t hire somebody who’s not a good car designer who will be able to use these tools effectively,” Macey said.

But product planners or marketers may soon be able to enter a prompt and receive a high-quality design, reducing the need for professional designers. For instance, an automotive executive without a design background may be able to select photos of an existing product, like the Toyota Tacoma pickup truck, and ask for a version “with more of a Jaguar feel” to get nearly finished new truck design, CCS’s Zammit said.

But while AI-powered design tools are good at copying or modifying existing designs, they’re trained on images and data about what’s already been done, Zammit said. He said one unnamed company began using AI for sketching but were “getting the same old stuff” and “went back to drawing it themselves.”

Instead, they began using AI to create inspirational images rather than manually search Pinterest and other sources for inspiration, Zammit said.

Exploring uncharted territory

Experimentation is the key to successfully using AI-powered design tools, experts said.

“Don’t be afraid to experiment and try new things. It can be easy to get stuck in a certain direction with AI, whether it’s a prompt-based image generator like Midjourney or a rendering tool like Vizcom,” Hydra’s Hull said. “When you can find an experienced design team that is also adept at prompt generation and is good at creative thinking to really push tools outside of the normal use case, that is where you will find unique design elements emerge.”

Over time, the public will also improve at evaluating “what is and isn’t good design,” Hull said.

“Before AI, photorealistic images of cars were pretty sparse, but with AI we are and will continue to be over inundated with endless amounts of realistic-looking car images. Once the audience gets past the initial wow factor, they will start analyzing and comparing which of the images they’re seeing are actually successful designs,” Hull said.

So, will automotive design become bland if manufacturers get rid of designers?

“Yes,” CCS’s Zammit said. “But if the designer stays in the equation, we will get the most expressive, radical, crazy designs that we've ever seen before.”

New capabilities, such as fully 3D polygonal model generation, will soon become part of the design process, but it probably won’t eliminate the need for designers, Hydra’s Hull said.

“Don’t be worried that AI is stealing your job,” Hull said. “Learn it, stay on the leading edge and become a better designer with new tools at your disposal.”

Hertz, the global rental car giant, raised customers’ hackles after it deployed an AI system to scan vehicles for damage and bill them automatically.

Adam Foley, an AI and automation consultant and founder of Foley Digital Advisory, was one of those customers. When Foley picked up his rental car from Hartsfield-Jackson Atlanta International Airport, he noticed high-resolution cameras but thought they were for internal maintenance.

He never expected to receive a bill for vehicle damage without speaking to a human but received an automated text message and email informing him that Hertz had found damage, along with $350 in charges.

“It definitely blindsided me,” Foley said. “There wasn't any clear warning that damage detection would take place — probably buried somewhere in the fine print.”

Foley was particularly miffed that Hertz charged $190 in “fees.”

When he contacted Hertz to complain, Foley was unable to speak with a human. Instead, the company’s chatbot “looped” through the policy, Foley said.

“I decided I wasn't going to pay it and made a LinkedIn post that went viral,” Foley said. “After seven weeks and multiple media outlets calling them, they finally decided to waive my fees.”

Hertz is just one of many companies to spark consumer backlash as businesses everywhere rush to adopt AI and deploy the technology before it’s ready, said Audrey Chee-Read, principal analyst at Forrester.

In an emailed statement, Hertz defended its decision, arguing that the system is more efficient and addresses a customer pain point.

“For years, vehicle damage inspections have caused confusion and frustration. The process was manual, subjective, and inconsistent, and that isn’t good enough for our customers or our business,” Hertz spokesperson Emily Place said. “We know change of this scale takes time, and we’re listening, learning, and improving every day.”

But that’s unlikely to assuage customers’ concerns, Chee-Read said.

“Nobody wants to be charged more, even if there was damage, right?” Chee-Read said. 

The plan at Hertz

Hertz says it is rolling out the technology gradually and continually refining its process.

When a vehicle is picked up and returned, the UVeye-developed system captures 360-degree images of the vehicle's body, tires, glass and undercarriage, Place said.

Then it uses AI to compare the before and after images, identifies any damage and bills customers automatically for damages exceeding its billable threshold, Place said. Fewer than 3% of the vehicles scanned by the system are billed for damages, Place said.

Customers can contact Hertz’s customer care team to dispute damage charges by chat, email, phone or social media, Hertz said. However, customers cannot contact a human through Hertz’s mobile app, though the company says it plans to add that functionality.

But that's little solace to affected customers. In designing its mobile experience, Hertz appears to have prioritized its short-term financial interests over customer experience. That could have lasting effects on its brand reputation and lifetime customer value.

"It feels like it's really benefiting the company more than the customer,” Chee-Read said. "Having a technology to assess damages that may be general wear and tear or invisible to the human eye will feel even more emotionally upsetting to the customer, where it doesn't feel like it's value to the customer."

For instance, the company made it more difficult for customers to dispute charges by not allowing them to do it through its mobile app.

To Foley, who has deployed AI systems for his clients, that’s bad design. "Any system I've built has humans take over after two to three disgruntled messages from a user, especially at the very beginning," Foley said.

Human oversight is essential because AI-powered systems cannot effectively make judgment calls, especially those based on emotion, Chee-Read said.

“For the AI technology, it’s much more black and white,” Chee-Read said.

The butler approach

To avoid AI-related consumer backlash, CX leaders must exercise caution before implementing consumer-facing technologies, experts say.

“I often advise our retail clients to think of your customer-facing AI implementations from the perspective of a butler and less like a stalker,” said John Dubois, EY Americas AI strategy leader. “So, these systems should be in service of the customer and workforce and less about maximizing every dollar you can get out of your customer.”

Hertz, for instance, could have piloted the technology, not just the location, and it could have allowed customers to opt into the pilot, Chee-Read said.

Clear, consistent and transparent communication with customers is key, experts say. 

“Without transparency, consumers will assume that organizations are using AI to automate processes like this to maximize their own profits,” Dubois said. It should feel “more like they are optimizing the process in service of their customer rather than doing something to our customers or to maximize our financial gains.”

Hertz sent mixed messages with its carrot-and-stick approach, Foley said. The invoice said he could get a discount if he paid immediately and that “if you don’t pay, there could be additional court fees and administrative costs.”

"They've looked into all the legality but none of the ethics or consumer experience,” Foley said.

Ultimately, CX leaders must ensure that their organization, and its technology, are treating its customers well, experts say. Adopting technology without taking the customer’s experience into consideration can quickly turn into lost loyalty.

Automotive companies are accelerating AI adoption while battling headwinds from economic uncertainty driven by shifting U.S. trade policy. 

The financial strain cannot be overstated, as General Motors projects up to $5 billion in tariff costs in 2025, Toyota Motor Corp. forecasts a net income decline of 21% amid levies and Ford Motor Co. expects tariffs to cost the company about $2.5 billion in gross profit in 2025.  

In the face of economic uncertainty, major carmakers are turning to AI, automation and strengthened data analytics capabilities to unlock efficiencies, save costs and help buoy the business.

Ford COO Kumar Galhotra said the company is working with several partners on AI projects that will “save us substantial amounts of costs” during its Q1 2025 earnings call. 

The company is implementing AI within its product development system, which is already saving weeks worth of effort as part of the design process, Ford executives said. The carmaker is also experimenting with AI-powered robotics, such as its Boston Dynamics robot dog deployed in an assembly plant in Spain. 

“It has sensors on it that can see, hear, feel vibrations, smell any leaks of oil,” Galhotra said. “It just literally walks around the plant all day long and has changed how we do our preventive maintenance because it can see and hear and look for errors well before a human being could.”

General Motors has relied on partners to beef up its AI capabilities, too. The company partnered with chipmaker Nvidia in March to accelerate the integration of AI-powered hardware and software in its vehicles. The partnership was announced just a few weeks after the automotive manufacturer appointed its first chief AI officer.

“We have many other partnerships going on because we think we can leverage AI to make our business much more efficient and faster,” GM CEO Mary Barra said during the company’s Q1 2025 earnings call. “We just hired a new head of AI who's working across each area of the business, and each of my directs has a goal to demonstrate how they're leveraging AI to either improve the way we do business or the cost of how we do business.”

GM is using AI to bolster safety, improve quality and unlock efficiencies as well. The company implements digital twins to simulate a running production line to optimize planning processes and save time and money, according to a March blog post. Other use cases include pinpointing potential leaks in battery packs, inspecting welds and painting coats, and locating optimal locations to install electric vehicle chargers. 

Toyota executives also highlighted the potential benefits of AI in its latest earnings call. The company plans to use the technology to improve vehicle development and build assistants that boost customer satisfaction. 

“We intend to materialize the diverse values that data and AI will create, such as database vehicle development and service evolution, AI agents that grow together with customers and services that utilize onboard sensors,” President and CEO Koji Sato said during the company’s Q4 2025 earnings call. 

In 2024, Toyota became an early adopter of agentic AI, deploying around nine AI agents to help store and share knowledge as part of a Microsoft partnership. Each agent has a different expertise and role, from helping engineers answer design efficiency questions to suggesting better ways to meet regulatory standards. The AI system is built on Microsoft Azure OpenAI Service and uses OpenAI’s GPT-4o model.

“The proprietary system is grounded with Toyota’s design data — that includes its past engineering design reports, the latest regulatory information and even handwritten documents by veteran engineers,” Microsoft said in a November blog post. “In the future, the system will select the right agents on its own, without users needing to do so.”

The automotive industry’s early adoption of artificial intelligence has opened it to a sharp spike in cyberattacks. That’s the finding of cybersecurity specialist Upstream in its 2026 Global Automotive and Smart Mobility Cybersecurity Report published Feb. 18.

Upstream operates a cloud-based, AI-powered data management platform purpose-built for connected vehicles, smart mobility, physical AI and the IoT ecosystem, per the release. The company is backed by Alliance Ventures (operated by Renault, Nissan and Mitsubishi), as well as Volvo Group, BMW and Hyundai Motor Group.

The company’s report found that cybersecurity attacks targeting automotive and smart mobility organizations more than doubled in 2025. According to Upstream, ransom-related cyberattacks accounted for 44% of all incidents, which is twice the number reported in 2024.

Upstream’s report follows the auto industry’s most prominent cyberattack, which in 2025 targeted Jaguar Land Rover. The attack crippled the company’s IT systems and led to a worldwide vehicle production shutdown lasting nearly 40 days.

The rise of software-defined vehicles is also opening more avenues for potential cyberattacks, according to Upstream. In January, WardsAuto reported on NXP’s “post-quantum cryptography” technology, which the company claims may help offset the increased threats of automotive cybersecurity attacks.

The report concluded that the rise in cybersecurity threats is being driven by the rapid expansion of application programming interfaces (APIs) and AI-driven, software-based vehicle architectures, combined with the increased sophistication being used by organized threat actors.

It adds that there is a widening gap between the auto industry’s cybersecurity capabilities and those of sophisticated hackers, who are steering ransomware as the fastest-growing and most disruptive threat to the industry, per the report.

Upstream’s study analyzed 494 publicly reported cybersecurity incidents from 2025 within the automotive and smart mobility sector worldwide and identified two main trends.

First, AI-based architectures have dramatically expanded attack surfaces by introducing new entry points and systematic exposures across the entire ecosystem.

The second is financially motivated, with well-resourced and coordinated attack groups increasingly targeting the automotive and smart mobility sector. This led to a major escalation of ransomware attacks, which can potentially translate into billions of dollars in operational and economic losses.

Ransom attacks, however, are now spreading beyond IT and enterprise systems and into vehicles on the road. In mid-2025, attackers accessed remote vehicle command and control systems (through a companion app) and locked owners out by taking remote control of functions such as ignition and door locks. The attackers then demanded ransom payments to restore access to the vehicles.

The 2026 Global Automotive and Smart Mobility Cybersecurity Report found: 

• 71% of cyberattacks were from black hat actors, up from 65% in 2024.
• 92% of automotive attacks were conducted remotely, of which 86% required no physical proximity to vehicles and systems.
• 67% of incidents involved telematics and cloud systems as attack vectors, where APIs serve as the enabler of a significant portion of incidents.
• 68% of incidents involved data and privacy breaches, while 34% of incidents were focused on business and operational disruption.
• 61% of incidents had the potential to impact thousands to millions of mobility assets, 20% of which were massive-scale events.

However, while the auto industry’s early adoption of AI has accelerated cybersecurity risks, it also provides faster response time to attacks, said Upstream’s co-founder and CEO Yoav Levy in the release.

“AI is also enabling attackers to move faster, at greater scale and with more automation, while the industry is still relying on security models built for a far more static world,” he added.

“Our 2026 report shows that AI significantly expands the cybersecurity attack surface, as traditional perimeter defenses no longer suffice when AI systems adapt dynamically and directly influence physical outcomes,” Levy said.

Analysts’ view

AI is a real risk for the automotive sector, providing AI‑enabled attack tools to use against the industry's broad and diverse supplier ecosystem, Andrei Quinn-Barabanov, supply chain industry practice lead at Moody's Analytics, said in an email to WardsAuto.

“Many small suppliers may have limited resources, either human or financial, to implement advanced cybersecurity measures,” Quinn-Barabanov wrote.

“A practical way to help mitigate this risk is to design and follow a disciplined process of sharing sensitive information with vulnerable suppliers, focusing on what information is essential to share and how to share it securely,” he added.

Meanwhile, there is an expectation that AI will be capable of deploying malware that rewrites its own code, exploits previously unknown security flaws and launches automated campaigns against thousands of targets simultaneously, said Eric Greaser, vice-president of the corporate finance group Moody's Ratings.

“A growing concern is the use of AI to automate vulnerability discovery, continuously scanning networks and applications for flaws such as missing patches or misconfigurations, at a scale and speed that outpaces defenders' ability to remediate them,” he said in an emailed response.

As of December 2025, Moody’s Ratings found that the automotive sector had the highest percentage of issuers that experienced a cyber-incident within the last 24 months,” according to Greaser. “At the same time, implementation of key cyber-controls is uneven and supply chain vulnerabilities remain a key concern,” he said.