Using Generative AI to Revolutionize the Automotive Customer Experience

Why Generative AI enables a leap forward in the automotive customer experience 

Advancements in artificial intelligence are reshaping the art of the possible in many industries. Given the transition to electric vehicles, the rise of the software-defined vehicle, and emerging advanced driver-assisted and autonomous-driving capabilities, the automotive industry now stands at the forefront of this AI-inspired wave. This is evidenced by the fact that Automotive Original Equipment Manufacturers (OEMs) are investing billions of dollars to optimize their products and infuse them with the latest AI technologies.

 Most OEMs, however, are falling short in their efforts to use AI to transform the customer experience. For example, a recent study by Boston Consulting Group (BCG) revealed that, while the quality of the car-buying experience is the most important decision factor for many customers, only 52% of customers say they are completely satisfied with their most recent car-buying experience. By leveraging new technologies, particularly GenAI, OEMs can transform the customer experience to outperform their peers in an increasingly competitive and dynamic market landscape.

GenAI can revolutionize the customer experience by creating more personalized, proactive, and meaningful interactions. The technology's natural-language capabilities enable more intuitive, human-like interactions between customers and their vehicles, dealers, OEMs, and third-party service providers to create a more seamless, engaging user experience. By combining and extending these capabilities with GenAI’s ability to mimic human-like reasoning and logic, OEMs that take the lead in leveraging this technology are positioned to offer more sophisticated and more highly tailored products and services.

One often-overlooked advantage of GenAI is the unparalleled insights it gives automotive into their customer base. Modern vehicles are equipped with a host of advanced sensors and connectivity features that provide rich sources of user information. Much like how telcos have been able to use customer data to better understand smartphone usage, OEMs can use GenAI-generated data to gain similarly powerful insights into how customers use their cars.

Exploring the most impactful use cases along the customer journey

GenAI is a critical tool for those OEMs that are ready to reshape the car-buying experience. In the following graphic, we describe five high-impact GenAI use-cases that OEMs can implement across the customer journey. Two of these use cases have a particularly high potential to revolutionize the customer experience: hyper-personalized marketing and enhanced in-car interaction.

Hyper-personalized Marketing: As specific individuals consider which vehicle to purchase, customer data can be leveraged to hyper-personalize marketing content. Instead of generic promotional communications, OEMs can use GenAI to send tailored content directly to each customer. These messages can resonate deeply with buyers on an individual level, mirroring buyers' specific interests, preferred tonality, and overall preferences. Instagram ads could be tailored to match the target customer’s context, for example, and create an image that places the preferred car model in front of the skyline of the city closest to the buyer. The post's copy could be crafted to refer to a customer's love of yoga or to highlight the car’s safety features for children, then end with a call to action to book a test drive at the dealer closest to the customer. This one-to-one communication approach that GenAI makes possible creates a highly personalized car-buying experience that can lead to increased customer satisfaction and higher sales conversion.

Enhanced In-Car Interaction: GenAI can transform the customer experience in many ways—even within the car itself. Many leading OEMs are already leveraging GenAI to create interactive, personal, and immersive in-vehicles assistants to help customers understand the car’s features, customize settings, and even improve their ability to interact with assisted-driving systems. Using natural language, the in-car GenAI assistant could, for example, explain to the driver why the car has chosen to change lanes or suddenly apply the brakes. Similarly, car owners could use the assistant to book a repair appointment at their local dealership. When combined with other technologies such as augmented reality, GenAI has the potential to truly revolutionize the in-car experience.

Three enablers to realize GenAI's potential in the automotive customer experience

Automotive OEMs should focus on the following three enablers to realize the potential of GenAI in the automotive customer experience:

First, OEMs should develop a prioritized roadmap of GenAI applications. This step will align the organization behind a common goal and ensure that the most impactful use cases are implemented first. This process might include:

• Using focus groups to brainstorm potential GenAI applications along the customer journey
• Evaluating these applications within a consistent framework to assess impact, risk potential, and feasibility
• Prioritizing these applications based on the assessment to develop a comprehensive roadmap
• Resourcing and implementing the prioritized applications with a strong focus on speed to value

Second, OEMs should double down on their customer-data strategy. To enable a truly customer-centric experience, OEMs need to know as much as they can about their customers. They can do so by:

• Creating an overview of the customer data available, adding new sources of customer data as they become available
• Facilitating access to internal users so that they can then leverage this data
• Ensuring that the data remains accurate and relevant throughout its lifecycle
• Setting up archival and deletion processes that conform to legal and regulatory requirements
• Establishing effective mechanisms to keep a thorough record of how data is used, processed, edited, accessed and, eventually, deleted

Third, OEMs should design an optimal tech stack. GenAI use cases can require significant compute resources, so a careful examination of the tradeoffs between value, costs, latency, and quality of response need to be made for each use case.

For example, smaller, less-expensive models with lower compute needs may offer a better overall performance than larger models. Maintaining flexibility to leverage different models depending on needs can lead to significant cost optimization, which is the key to broader adoption of GenAI capabilities.

Beyond compute, GenAI use cases will often need to access expert knowledge databases or require an agent mechanism to trigger actions such as booking customer test drives. Services like Amazon Bedrock include a variety of GenAI models for building use cases. The solution can also enable access to relevant knowledge stores, and is able to take actions through agents—all in a single service.

From creating brand awareness to improving aftersales and reinventing how customers interact with their cars, GenAI is poised to revolutionize the automotive customer experience. OEMs that seize this opportunity and invest in the future will be well-positioned to differentiate themselves from their competition.