Generative AI in automotive

While Generative AI’s prowess in content creation is well-known, there is a much wider variety of use cases viable for the automotive sector, ranging from production through driver and customer experiences to sales and services.

Prototyping

Generative AI can help generate rapid prototypes, reducing both the time and cost traditionally associated with prototyping and iteration cycles.

Autonomous Vehicle Behavior

Generative AI can be used to simulate various driving scenarios, aiding in the training and testing of autonomous vehicles to improve their decision-making capabilities.

Predictive Maintenance

Generative AI can help predict vehicle maintenance needs by analyzing sensor data and supporting customers in scheduling servicing before problems occur.

Loyalty Programs

Generative AI can assist in developing rewards programs by analyzing customer data and preferences to offer incentives tailored to individual drivers’ preferences.

The above examples are merely scratching the surface of what’s possible with Generative AI in an automotive context. That being said, randomly throwing Generative AI at a complex challenge is a surefire recipe for failure: Today’s solutions are much more limited than they seem at first glance, plus a lot of them come with characteristics that can render them useless for businesses. These challenges are especially – but not exclusively – relevant when employing out-of-the-box / consumer grade solutions:

• Lack of transparency: The composition of the underlying data sets is unclear to the user.
• Lack of explainability: Most solutions are not designed to explain and contextualize the way they arrive at a given conclusion.
• Lack of robustness: Today’s Generative AIs suffer from low accuracy when confronted with adversarial inputs.
• Lack of privacy: Some service providers reserve themselves the rights to utilize both user inputs and tool outputs in unspecified ways.
• Lack of fairness: All available solutions suffer from inherent bias as the act of curating their underlying data sets inevitably leads to unintentional bias.

In some instances, Generative AI can also hallucinate facts due to its limited (and outdated) corpus of knowledge and complete lack of world knowledge. Add to the above the still muddy regulatory waters in terms of ownership and copyright of Generative AI outputs and it becomes evident that designing and employing solutions that consistently add tangible value is no mean feat.

Due to the complex nature of the challenges that Generative AI comes with, it is important to do the right things, the right way. This means, among other things:

• carefully crafting a strategic vision,
• setting up a value focused operating model,
• incorporating AIOps into engineering & operations,
• building upon the right data sets, using the right technology,
• focusing on acquiring and nurturing talent early,
• cultivating a culture of change and co-creation.

Generative AI offers an ever evolving set of impressive and unique opportunities. Unlocking their business value is complex – get started today!