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CES Panel: AI Strategies for More Transportation Transformation

Massive disruption is expected -- again -- in the automotive, transportation scene if companies do not get caught up in AI hype.

Joao-Pierre S. Ruth

January 9, 2024

5 Min Read
GK Images via Alamy Stock Photo

At a Glance

  • Panelist at Ernst & Young session see investing trends in AI comparable to prior buzz about autonomous driving.
  • If applied carefully, AI could give a boost to customer-facing resources and customer retention.
  • The patchwork of data privacy regulations companies face in different countries may affect data sources AI needs to evolve.

The potential for greater efficiency and retaining customers could be under the hood of AI, according to a panel convened at CES by Ernst & Young (EY).

The session, held Monday in person in Las Vegas as well as streamed online, focused on how AI-driven strategies might fuel new changes in the automotive and transportation industry.

Moderator Randy Miller, global advanced manufacturing and mobility industry market leader at EY, discussed the potential shakeups and opportunities AI could introduce with Constantin M. Gall, managing partner at EY; Sabine Scheunert, advisory board member with Planet First Partners and former vice president, digital and IT sales and marketing with Mercedes-Benz; and Damian Barnett, CTO for automotive at Luxoft.

Gall reflected on the similarities of AI’s current time in the limelight with other technology that raised eyebrows at CES in the past. “Ten years back we had a big discussion around autonomous driving,” he said. “It was the hype back then. Billions and billions have been poured into that.” Gall also spoke about the debate over the success of such investments and efforts. “This time we call it AI, and if you look at the numbers, they are almost the same size in terms of investment dollars like back then for autonomous. By 2030, we expect that $75 billion [will] have been invested into AI in the automotive space. The question now is, ‘What do we do with that money, and how do you commercialize?’”

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AI on a Journey in Mobility

He said EY sees three areas where OEMs and Tier 1 suppliers to the mobility space currently focus. The first, Gall said, is called proactive care, where everything related to cars, such as procuring a car or ensuring maintenance is taken care of with AI helping to provide a seamless user experience. The next area is the proactive journey, for instance commuting, where AI might assess a morning schedule, upcoming appointments, their locations, and figure out the best time to get on the road. The final area, he said, is called proactive mobility, which is tied to autonomous driving. “What can you do while you spend time in the car?” Gall asked. “It’s the mobile office; it’s kind of augmented reality; it’s kind of infotainment while you’re standing there waiting at the charging pole.”

Real-world examples of change AI has already brought include 3D, interactive sales experiences, he said, where consumers can try out and explore cars without being physically present.

As with most evolving technology, some caveats come with such promise and potential. “There’s a big ‘but’ -- you should not get carried away by the things that technology enables these days,” Gall said. A risk of getting swept up in the hype are potential costly oversights AI might engage in. “You either think it’s great having AI out there or you will curse like hell because you sold your SUV for a dollar to a customer,” he said, describing unintended consequences if a chatbot were to engage unwittingly in a legally binding contract.

Related:Intel, DigitalBridge Create New GenAI Software Company

Scheunert said many manufacturers in the car industry have already dabbled with AI and are considering new ways to expand on its use. “Nowadays, there’s not a single OEM out there who’s not utilizing AI,” she said. “It starts with clear potential for huge efficiencies.”

What is crucial for the car industry, Scheunert said, is that putting AI to work shortening development life cycles on cars can be a significant efficiency driver. OEMs may already have AI in their production lines, but she also said the technology is being integrated into customer-facing needs such as answering queries.

“The opportunity is large -- absolutely,” she said. “Pitfalls -- definitely.” Investing in AI, Scheunert said, must include understanding where it can really start to pay off, which might be in customer retention. “What we did is, we started in predicting maintenance issues,” she said. “The after sales is still a pain point because all the OEMs concentrate primarily on conquering customers. It costs seven times to reconquer someone that you lost.”

Related:Generative AI an Emerging Risk as CISOs Shift Cyber Resilience Strategies

Data Management and Integrity Crucial for AI

Gall pointed out there are compliance and regulatory matters -- particularly in data privacy -- that could also affect how AI develops as a tool for customer interaction. “You really need to get a handle on the entire data management and data integrity,” he said. “Data governance and data integrity are two key topics that we still see today being some of the most challenging parts, especially if you have separate legal entities in jurisdictions like Europe.” Companies that operate in the region may face mission critical concerns trying to comply with different data protection and privacy regulations in each country, Gall said, which could impact the merging of such data lakes to let AI have a cohesive view of the customer and customer journey.

More than one year since generative AI took off, industry should recognize that it is not a perfect, magic elixir for their needs. “Organizations in the industry have to be aware that GenAI or AI in general is just a new technology,” Luxoft’s Barnett said. “It’s not going to solve the many challenges that the industry and the organization has today through digitization.” It is important to get a grasp on data in the context of privacy concerns or how to create a standard data platform, he said. “Without the data, AI falls apart because I need that data to really train my large language models in order to really reap the benefits of AI moving forward.”

About the Author(s)

Joao-Pierre S. Ruth

Senior Editor

Joao-Pierre S. Ruth covers tech policy, including ethics, privacy, legislation, and risk; fintech; code strategy; and cloud & edge computing for InformationWeek. He has been a journalist for more than 25 years, reporting on business and technology first in New Jersey, then covering the New York tech startup community, and later as a freelancer for such outlets as TheStreet, Investopedia, and Street Fight. Follow him on Twitter: @jpruth.


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