On the heels of the chip shortage and other supply chain challenges, there is increasing pressure to have better and future-forward intelligence around supply inventory. Knowing—or better yet, predicting—what inventory is available can improve marketing efforts, hyper-personalization and the overall buying experience for customers. AI offers a tremendous opportunity to better model and predict future demand and supply of vehicles. Imagine knowing in advance who will be buying what vehicle and when. More detailed prediction analytics will inform the timing of offers, what kind of offer will resonate most with a customer and how an OEM or dealer can personalize the go-to-market offer faster based on customer data about preferences or past purchases.
AI also comes into play in the automotive aftermarket, which is projected to grow at a rate of over 6.4 percent CAGR from 2022 to 2028. The sequencing of parts is crucial to the production of a vehicle. Many suppliers struggle with overstock and dealers, or those fixing vehicles cannot always access the parts they need. In fact, automotive brands are losing market share to Independent Aftermarket (IAM) participants, which include suppliers of spare parts and accessories, independent dealers and garage service providers.
With the help of AI, digital twin technology can create a virtual replica of an entire vehicle (including its software, warranty data, service history and performance) and they can also simulate warehouse supply—and even an entire organizational structure. Organizations gain a view into real-time supply and demand.
This comprehensive, data-driven picture can also help with determining customer purchase propensity, loyalty, brand affinity, or product affinity, and it can also support stock optimization because supply and demand are accurately modeled.
 Global Market Insights, Graphical Research
Avoiding bumps in the road
AI is driving many opportunities in automotive, but to fully take advantage, companies need some basics in place. First, although it’s easy to buy and implement AI solutions, a business strategy should be driving and informing the use of AI. What is the organization trying to achieve and how can AI help solve those challenges or enable working in new ways?
To build AI models responsibly and minimize bias and risk, it will be important to know who has access to the models and who is consuming the models. Hold them accountable for ensuring that raw or structured data hasn't been changed by a model. Make data accessible in a safe environment for data scientists and analysts to experiment. This is how new use cases for AI will be revealed and ultimately adopted.