On the surface, not in service

Why is this interesting?

After 2023, the year in which generative ai changed everything, its superficial presence at CES 2024 showed that companies are still trying to make sense of its transformative potential.

Key products:

  • Volkswagen / Cerence IDA

  • BMW / Amazon in-car assistant

  • Mercedes-Benz / Will.I.Am Sound Drive

The critical questions:

  • How can you deploy generative AI and machine learning in ways that differentiate your brand, rather than making it another *me too*?

  • How can you ensure that these tools build on a consistent and coherent baseline user experience, rather than further confusing customers?

  • Have you ensured that you've got the basics right — such as charging, ADAS, and everyday use — before you start to extend the experience in to new, ai-enabled realms?

The detail:

Although many companies claimed to have integrated generative AI in to their products, the truth revealed something more superficial.

Whether it was Volkswagen and Cerence updating IDA to work with ChatGPT, BMW's collaboration with Amazon to make it easier to understand an overly-complex HMI or Mercedes working with Will.I.Am to bring an AI-driven DJ to MBUX, each of these explorations failed in different ways.

With IDA, complex searches still resulted in a list from which the driver had to physically tap to select. The opportunity to reduce eyes-off time was missed.

With BMW and Amazon, a request to explain various drive modes offered by the car resulted in lengthy streams of PR-speak, even when instructed to speak like a child. Why not use machine learning, in combination with generative AI, to assess, suggest and activate the ideal mode for a given situation?

And by generating music in response to the vehicle's sensor suite, including acceleration, braking, and steering, have Will.I.Am and Mercedes not simply created an inducement to aggressive driving, one that assumes that every customer likes ambient electronica?

While it's early days for the integration of generative AI and machine learning in to automotive interfaces, by launching half-baked products too soon, automakers risk setting high expectations, failing to meet them, and denting consumer enthusiasm as a result. This is especially true when many OEMs are struggling to deliver the basics of the EV ownership experience.

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