29 AI Use Cases - Transforming the Automotive Industry

At present, companies in the automotive sector only utilize 12% of the available data. The rise of Industry 4.0 and its big data usage capabilities have been the key drivers in developing data-driven manufacturing strategies. One such design idea is that of the digital twin; this simulation-based endeavor allows manufacturers to simulate car parts and thereby embark on faster cost-effective development and match their quality expectations.

Digital twin integrates data from many sources and provides a reliable way to understand design integrity and success. It also uses historical and current sensor data to predict downtimes and help businesses proactively reduce unwanted interruptions. Furthermore, digital twins collate customer experience data to understand customer demands and design choices, enabling manufacturers to develop solutions to improve customer experience.

Also, read – Three reasons why next-gen manufacturing required digital twins

With predicting the customer demands becoming more complex every passing day, manual fulfillment forecasting no longer seems to cut. Instead, AI-based algorithms that use data science to churn out demand and supply sets are the trendsetters currently. Each data set will have its shortcomings, so the auto-replenishment AI platform combines simulation with replenishment to deliver autonomous and optimized results.

For this, most car manufacturers are deploying MILP (Mixed Integer Linear Programming) capable tools that can use many parameters and combine them with simulations to ensure they never run out of stock or are not over-burdened with excess inventory.

General Motors, the legendary car manufacturer, introduced the self-driving car's concept back in 1939. But there was little to no critical development in this area until AI came into the frame. One of the examples of automatic guided vehicles would be Tesla's Autopilot. It uses a more complex vision-based approach that generates up to 1.5 petabytes of data comprising a million 10-second videos and 6 billion objects.

It is annotated with depth, velocity, and bounding boxes to deliver meaningful results to the users constantly. It has also packed in auto-annotation tools that improve accuracy and human review elements. It is merely one of the examples, and several other vehicle types can also benefit from AI's inculcation.

Insurance is a vital part of the automotive industry, and vehicle owners often forget to renew it, which can be hazardous for them and their families. AI can play a significant role in improving renewal rates by managing customer communication effectively. The algorithm can automatically detect when the user's vehicle insurance expires and send them reminders to renew the plans.

It can benefit both parties and prove a vital cog for the industry. In addition, users can also upload images of their damaged vehicles allowing the software to gauge the damage level and provide an accurate representation of repair costs and insurance claim amount in real-time.

As per a PwC report, 40% of the mileage driven in Europe could be from autonomous vehicles in 2030. While several automotive manufacturers, such as Tesla, Ford, and the likes, have tested and implemented autonomous vehicle features in their cars, today's results have been nothing to write home about. But that doesn't deter us from concluding that autonomous vehicles would be a full-fledged reality in the next few years.

While the technology would require testing, infrastructure, and approval, the idea of a partially autonomous vehicle is already at play. Tesla's Autopilot and several other brands offering self-parking and lane assists are examples of partially autonomous vehicles.

AI and its predictive capabilities and ML abilities are also reshaping the overall car dealership experience. It assists dealerships in finding out the right clients to focus on at the right time. In addition, it also has been instrumental in tweaking marketing strategies that have enabled them to up their game and meet customer expectations with ease.

Further, AI is smarter than most human resources in gauging opportunities for upselling and cross-selling. It predicts the right time to focus on a customer depending on where they are in their purchase cycles, enabling the dealerships to retain more customers and generate higher revenues.

AI is also impacting the production and assembly lines in the automotive industry. They use a mix of robotics, human-machine interactions, and quality assurance parameters to augment the overall efficiency and produce better results.

For example, today, smart, collaborative robots work in conjunction with humans in the shared assembly space. They have been instrumental in detecting and sending human motions to prevent fatal mishaps in the factories. These robots can also identify defects and irregularities in materials and components used for production and raise alerts, if necessary.

Also, read – How is RPA redefining the manufacturing business landscape?

Autonomous vehicles and connected vehicles are closely knit endeavors that would find difficult to sustain without each other. The latter is very much a new concept, and the manufacturers are in the stage of testing and increasing their production capacities. But with IoT making a splash in the car industry, the level of connectivity is soon going to see a significant overhaul.

It would enable the players in this industry to focus on interoperability and ensure seamless connectivity without negatively affecting user safety and security. With 5G bringing in faster and more reliable networks, there would be greater dependence on near-perfect connectivity. Over-the-air fault detection and repair would be integral to building a superior customer experience.

Also, read – Connected Factories – top use cases and benefits

The availability and usage of big data have had pervasive impacts on car manufacturers. One of them has been their ability to automate CPQ (Configure, Price, Quote) software by leveraging AI's prowess. The traditional CPQ, manual, or via software, had a tough time dealing with the multitude of data available around. But with AI baked into this software and its ability to process gallons of data simultaneously, car manufacturers can now undertake cross-channel endeavors, such as managing contracts, incentives, and discounts with ease.

Automating these processes will enable them to undertake seamless cross-selling and up-selling. In addition, cloud computing support brought in by AI has also enabled seamless and automated collaboration of this CPQ software with existing ERP and CRM systems.

AI-powered chatbots have become a thing in all the consumer-facing business models. It is no different for the automotive industry too.