How Artificial Intelligence Will Reshape the Auto Industry in an Experience-First World

Consumer-Experience AI for Automotive 

Artificial intelligence’s impact continues to expand rapidly across all major industries, with companies around the globe investing heavily in AI technologies to advance their competitive edge. Future trends suggest that without adopting an AI strategy now, organizations are likely to fall far behind their competitors.

When implemented correctly, AI and machine learning (ML) will deliver wide-sweeping value to businesses across many industries. In fact, companies that have already adopted AI report that it has directly impacted their customer satisfaction and ultimately boosted their bottom-line results. Our research and experience have found one of the easiest ways to move AI pilots to scaled deployments with tangible profits is to focus on one key objective – the consumer experience. 

For the automotive industry racing to build the most efficient, safest self-driving car, auto investors must adopt a consumer-first mindset to come out ahead. The winners of this race will be those who invested considerable effort in enhancing consumer experience – whether it be inside the cabin as a passenger or driver, or outside the car to improve safety and autonomy.

AI is Driving a Profound Transformation in the Auto Industry

Artificial Intelligence Will Reshape the Auto Industry

As the idea of a driverless future grows into a much more realistic possibility, AI and auto technology are becoming increasingly intertwined. Already, AI and electric are changing the way companies build cars and influencing the type of consumer that will ultimately buy or use those cars. According to Goldman Sachs, “In the next ten years, the auto industry will undergo a profound transformation: the cars it builds, the companies that build them and the consumers who buy them will look significantly different.” 

The future of transportation will be built with world-class AI, ultra-fast connectivity, and environmental impact in mind. Because of this, the scope of potential use cases for AI is massive. And while business use cases for AI and ML are becoming more varied (ranging from supply chain and manufacturing to autonomous vehicles and mobility-as-a-service), consumer experience-centric applications continue to be the most common and successful to deploy at scale. This is because both in-cabin and out-of-car experiences are directly tied to clear KPIs – such as the driver’s intention is clear and executed on, or the car is able to navigate without human intervention from point A to point B in diverse weather conditions. Additionally, many automotive companies have large amounts of untapped data that can be leveraged to improve these experiences.

High-quality Data is Essential for Auto Companies

For companies heavily investing in self-driving technology and the future of the connected car, teams are busy building a fully autonomous vehicle, improving driver assistance features, or any solution in-between. To do this, they often have to work with multiple vendors and applications to collect, label, prepare, and converge all that data to train their AI models effectively. But building the future of transportation is complicated enough without having to connect several dozen different data pipeline components, integrate and maintain a growing list of APIs. 

In order for a car to “see,” “hear,” “understand,” “talk,” and “think,” it needs video, image, audio, text, LiDAR, and sensor data to be correctly collected, structured, and understood by its machine learning models.

In the case of autonomous vehicles, like with healthcare or other use cases where risk management is critical, training data needs to be annotated and verified by humans at scale, so machines deliver the best accuracy every time and make AI that works for everyone. Combine that with the fact that cars not only need to abide by strict national and regional regulations, but also have to understand hundreds of languages and dialects, and it becomes an exponential challenge. 

This is where a data partner can help streamline and scale your data needs. Their expertise will enable you to put together these demanding components for the consumer experience, paving the way for success and scale.

Any business implementing an AI strategy should use high-quality data to maximize their chances of success. After all, industry analysts point to the fact that only 20% of aI projects, on average, make it to production. For the automotive industry, companies enhancing the in- and out-of-cabin vehicle experiences for consumers are only beginning to unlock the full potential of AI, so working with experienced partners and reliable processes are critical to increase their success odds and deliver a seamless car and driver experience.

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