AI: A New Tool for the Automotive Innovator

The drafting board and ruler that Fabio Taglioni used were the tools to help him make his imagined engine innovations come to life for the Ducati motorcycle company. His “desmodromic” valve layout made Ducati engines special, and known all over the world. If AI had existed, he no doubt would have used this tool as well. All to help generate the physical reality and riding experience he was looking for.

Today, everyone can use AI, and everyone can use it to accelerate the ideas they want to bring to life. Automotive examples abound, and connected vehicle services are no exception. Since these technologies are designed to bring services from outside the car, into the car, and provide a more personalized experience, AI has great advantages.

But AI needs data to work. The massive firehose of data being exchanged between automobiles and their connected cloud platforms and mobile applications is a perfect way to use “generated” data to further enhance what is possible for vehicle drivers and riders.

The oft-repeated adage over the last decade or two has been that “data is the new oil.” What this clever analogy points out is that there is rich value in robust data. Much like 19th century oilmen striking it rich, that’s just the beginning. Early wildcatters in California and Texas expanded the market for oil, and today entire nations’ economies are based around oil refining. However, just like refineries transform crude oil into gasoline and other products, data requires transformation to become something of value to an end user.

From raw telemetry to real insight

The first hurdle is collecting the data. There is an estimated installed base of 400 million vehicles worldwide with connected car capability, so the data is definitely flowing into the system. Telematics and cloud platforms have evolved greatly and these systems seem to get more advanced and widespread each year. The process by which insights are derived from the data requires a good sense for how vehicles are used today and what the pain points are for consumers. Well-crafted insights can clarify problems as well as answer questions.

Now, the capability of AI is added to the equation and faster processing of data and insights is driving the industry to seek more data monetization pathways. AI can more rapidly turn useful data into meaningful insights but the key to monetization is a keen understanding of what adds value for a consumer; what turns curiosity into willingness to pay. Put another way, economic value requires using the data generated to actually make substantive improvements that provide real service to both end consumers and to generate business for the automotive ecosystem — things that impact real decision making. Where AI has power is not just in the ability to extract insights, though, but also the speed at which the insights are presented, meaning there is value in real-time insights. In other words, historical analysis helps to understand what was working, but real-time insight can provide predictive analysis and unlock the ability to advise.

With the right degree of accuracy, this opens up the possibility for things like predictive maintenance. Why is that worth paying for? Well, there is value in both avoiding (or at least minimizing) vehicle downtime as well as increasing the lifespan of a vehicle or a major component of a vehicle such as tires, EV battery or transmission. These sorts of value-adds become even more powerful in a fleet environment where utilization rates, repair costs and vehicle longevity compound in value and importance.

Who is already using the data

For many years, third-party use of connected vehicle data was mostly with insurance providers, who realized dynamic driving data could help them more accurately determine driver behavior predictions, and therefore underwriting costs. Today, many types of connected vehicle data are used for many different use cases, such as smart city applications. Audi provides a great example of this type of application where the upcoming intersection timings for a green light are provided to the connected car. This service helps the driver sail through a series of intersections without stopping, reducing traffic congestion in the process.

With AI, data can become insights and insights can drive products and services. It can also help monitor usage and performance, using that feedback to determine value to a customer. Whether these systems save consumers and fleets money through predictive maintenance or other systems, understanding what a consumer is willing to pay for is largely driven by a deep understanding of what the consumer values.

Opportunity is not the same as revenue

Yet, the connected vehicle ecosystem is at a decisive point. The installed base of connected cars is growing, the AI capability to extract real value from vehicle data is maturing, and the market for data-driven services is projected to grow at double-digit rates through the end of the decade. The opportunity is real and it is substantial.

But opportunity alone does not create revenue. What separates the companies that will capture value from those that won’t is not the volume of data they collect or the sophistication of their AI models. It is whether they can translate those technical capabilities into services that real consumers and real fleet operators are willing to pay for, month after month. That requires a disciplined approach to product design, a genuine feedback loop with end users, a clear-eyed assessment of the competitive dynamics with Big Tech, and a data governance infrastructure that can withstand the regulatory scrutiny that is already here in Europe and building in the United States.

The companies best positioned to succeed in this environment share a common profile: they combine deep automotive domain expertise with modern software and AI engineering capability, and they approach connected vehicle strategy not as a technology project but as a business transformation. They understand that the value chain from raw telemetry to recurring revenue is long, complex and filled with integration challenges — and they have the cross-functional capability to navigate it. Innovators use all the tools that are at hand. Fabio Taglioni certainly would.