Automakers Say AI Is A Must But Worry About Job Impact

Leading battery companies concede AI is needed to stay competitive to meet stringent time-to-market needs.

Spencer Chin, Senior Editor

May 13, 2024

3 Min Read
AI will play a larger role in the development of electric vehicle batteries.
AI will play a larger role in the development of electric vehicle batteries despite job concerns, according to a Forrester survey.SweetBunFactory/ iStock / Getty Images Plus

At a Glance

  • Forrester study says automotive companies know AI is needed to streamline battery development cycles.
  • Engineers remain concerned about potential changes or loss of jobs due to AI.

Although proponents of AI (artificial intelligence) and machine learning are adamant that tools to implement these technologies will make existing engineers more productive, engineers in many sectors nevertheless remain concerned AI could adversely affect their livelihoods. The latest evidence comes from a recent Forrester Consulting 2024 study titled “AI for EV Battery Validation.”

The study surveyed 165 senior decision-makers in automotive engineering in North America and major European automotive markets, exploring their views on applying EngAI in developing EV batteries. In an industry increasingly dominated by balancing the seemingly conflicting goals of faster time to market and maintaining high product quality, the study gives first-hand insights into the pressures that automotive engineering players are facing in the race to develop industry-leading vehicles, and where intelligent technologies such as AI can address these urgent challenges to accelerate innovation.

AI A Competitive Must

According to the study, nearly two thirds of automotive leaders expect AI’s impact to be extremely or very significant, with over half indicating that Engineering AI (EngAI) - a sensible form of AI that learns from masses of engineering data to help test teams understand otherwise intractable problems, will be key to staying competitive in electric-vehicle (EV) battery development.

Related:AI: Friend or Foe of Chrysler Designers?

Because the automotive industry places a strong emphasis on time-to-market, the study noted that 64% of automotive engineering leaders stress the need to reduce the time and effort spent on EV battery validation. In the same vein, 66% of respondents believe it’s imperative to reduce dependency on physical tests, while still ensuring compliance with safety and quality standards. But 62% agreed that their current virtual validation tools, including physical simulation, do not fully ensure that battery designs meet all validation criteria.

Uncertainty About Jobs

Survey respondents conceded that while AI is needed, there is underlying concern about how the tools will affect their careers. While 44% of respondents express serious concern about the potential effect that the technology could have on their business’ staff-count, over half (58%) say AI is critical to ensure they stay competitive in EV battery development.

Dr. Richard Ahlfeld, CEO and Founder of Monolith, said: “EV and particularly battery development is highly competitive, and with that comes a lot of pressure to move faster. Engineering AI can learn to solve problems much faster than any human, and that’s what automotive leaders are starting to understand. Monolith offers a SaaS machine learning platform that uses no-code, machine learning software.

Related:Analyst Confirms Consumer Skepticism Toward EVs

 Although EV sales have been unpredictable, the survey found that engineering decision makers remain steadfast in seeking smart solutions to reduce costs and development time , which EngAI is expected to help resolve.

 Respondents expect EngAI to cut years, quarters or months in development cycles – including in cell characterisation testing (61%), module and pack testing (56%) regulatory testing (53%) and charging optimisation testing (48%). Meanwhile, they anticipate AI will help them achieve cost savings from $10 million to over $100 million in ageing and lifetime battery testing (37%), repeating tests due to failures (39%), thermal runaway testing (36%), and regulatory testing (32%).


About the Author(s)

Spencer Chin

Senior Editor, Design News

Spencer Chin is a Senior Editor for Design News, covering the electronics beat, which includes semiconductors, components, power, embedded systems, artificial intelligence, augmented and virtual reality, and other related subjects. He is always open to ideas for coverage. Spencer has spent many years covering electronics for brands including Electronic Products, Electronic Buyers News, EE Times, Power Electronics, and electronics360. You can reach him at [email protected] or follow him at @spencerchin.

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