29 AI Use Cases - Transforming the Automotive Industry

Feb 16, 2022
Manufacturing | 17 min READ
AI In Automotive Industry
The customers are willing to easily adopt even the most complex tech to make their lives easier. Before introducing smartphones and them going mainstream, the perception was quite the opposite.
Manoj Singhal
Manoj Singhal

EVP & Global Head



Sangram Kadam
Sangram Kadam

Former VP & Sales Head



Shirish Sahay
Shirish Sahay

VP & Sales Head

Manufacturing & Life Sciences- Europe


Since then, it has been an overhaul, one industry after another. The automotive industry was late to the party, and people started paying attention only after Elon Musk predicted that autonomous driving would be very much a thing within the next ten years back in 2017.
As per Deloitte's report, the global automotive industry would have a total value of USD 27 billion by 2025. It emphasized the potential of artificial intelligence (AI) and machine learning (ML) in reshaping the way we look at cars and the industry.
This article discusses 29 use cases of AI in the automotive industry that are paving the way to a connected future.
How Is AI Impacting the Automotive Industry?
Automation has been a part of the automotive industry for decades now, but with AI, it is different. ML and AI's ability to predict what's coming means that its ability to reshape the world of vehicles is far higher than what we have previously experienced.
The advent of Industry 4.0 means that AI's impact is no longer constrained to help develop self-driving vehicles but has percolated to create far more profound and more meaningful results. The car manufacturers are now looking to harness AI and ML to reduce costs, optimize products, improve efficiency, supercharge development cycles, and create a more sustainable ecosystem.
Its impact can certainly be understood when we say that it is no longer optional for car manufacturers to imbibe it – they are bound to, for their survival.
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AI Use Cases in Automotive Industry
AI today has become an indispensable part of the automotive industry. Here is why –
  1. Access control using facial recognition
  2. Analyzing Road Conditions
  3. Auto parts design using digital twins
  4. Automated replenishment
  5. Automatic guided vehicles
  6. Automotive insurance
  7. Autonomous vehicles
  8. Car dealership experience
  9. Collaborative robots
  10. Connected vehicles
  11. CPQ automation
  12. Customer support chatbots
  13. Demand forecasting
  14. Design and development
  15. Driver Behavior Analytics
  16. Driver monitoring system
  17. Emission monitoring
  18. Fleet monitoring System
  19. Generative designs
  20. Driver Behavior Analytics
  21. Marketing
  22. Predictive Vehicle Maintenance
  23. Manufacturing Process optimization
  24. Quality control
  25. Route optimization
  26. Supply chain optimization
  27. Vehicle prototyping
  28. Video analytics for shop floor
  29. Warehouse sorting
Access control using facial recognition
In the past, we would often run into "Work-under-progress" sites and would have to wait or take an alternate route, thereby wasting our time. With AI-powered automotive systems, you can now expect to be updated on any accidents, road closures, and construction work before it is too late.
AI-based navigation has become smarter with time and assists drivers by analyzing conditions and suggesting the best routes. In addition, if any pothole/hump is approaching, the driver is warned, ensuring they are not caught off-guard. Such information is invaluable for drivers, especially those who hustle in areas with frequent traffic issues.
Analyzing Road Conditions
Over 1000 people are massacred by vehicles running a red light needlessly in the US alone every year. While the traffic system cannot be questioned, it is the human tendency that often runs riot. Smart cities and the development of AI-powered vehicles could be a possible solution to this.
With the help of AI-based systems, vehicles can now analyze road conditions automatically. For example, the software can recognize traffic lights via automated imaging, and if it finds out the color to be red, the brakes are automatically induced. In addition, the solution also uses other factors, such as pedestrian mapping and traffic flow analysis, to further improve user experience.
Auto parts design using digital twins
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.
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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.
Automated replenishment
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.
Automatic guided vehicles
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.
Automotive insurance
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.
Autonomous vehicles
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.
29 AI Use Cases That Are Radically Transforming the Automotive Industry
Car dealership experience
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.
Collaborative robots
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.
Connected vehicles
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.
CPQ automation
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.
Customer support chatbots
AI-powered chatbots have become a thing in all the consumer-facing business models. It is no different for the automotive industry too.
AI-powered chatbots are reinventing customer service, and their multi-faceted abilities can prove beneficial for customer experience, outreach, engagement, and query support. These tools are capable of hyper-personalizing and allow the visitors to suggest the best fit. It also helps car manufacturers solve queries provide faster resolution and post-sales facilities.
Demand forecasting
The traditional demand forecasting is often mismanaged owing to human bias and errors in judgment, causing havoc for the car manufacturers. But the new-age AI and ML-powered algorithms have enabled industry players to structure demand forecasting by proactively detecting changes in customer behavior.
In addition, these algorithms are well equipped to identify cross-product and complicated relationships and patterns present in large datasets and use Big Data to capture potential demand fluctuation signals in advance and make the requisite changes throughout the process.
29 AI Use Cases That Are Radically Transforming the Automotive Industry
Design and development
It is known that designing and development takes up a significant chunk of time for car manufacturers looking to innovate and earn a competitive advantage. The design engineers are responsible for many tasks that start from conceptualizing to simulation and execution, and they always have to be on their toes to ensure optimum results. But AI can prove to be the game-changer here too.
For example, NVIDIA released its Quatro RTX graphics card powered by NVIDIA Turing™, designed to accelerate workflows. It uses AI ray tracing and programmable shading to reinvent how we see product designing in the automotive sector.
Driver Behavior Analytics
The inculcation of IoT sensors has had a tremendous impact on understanding and decoding driver behavior. In addition, AI and deep learning-based software's inculcation in the automotive industry has meant that car manufacturers can now bring about solutions to improve overall driver efficiency.
For this, IoT sensors are placed on board to analyze the driver's behavior throughout the journey. In addition, these can also act as early warning signals and take the requisite steps to eliminate potential distractions or bottlenecks. Furthermore, ML is deployed to continuously track driver responsiveness and use a signal to alert the driver of their abnormal behavior and its potential impact on the vehicle.
Driver monitoring system
With AI in the mix, the system can automatically detect the driver's presence and make the requisite adjustments, such as mirrors, temperature, and seats. Furthermore, the software continuously detects the driver and their behavioral attributes, such as eye openness, head position, and more, to figure out if they are feeling drowsy and take steps to prevent any fatal damage to the vehicle.
Even in case an accident takes place. The AI chops can also adjust airbags according to the body's position. In addition, the presence of an AI system has allowed the inculcation of gesture navigation which has allowed the drivers to manage infotainment merely by gestures.
Emission monitoring
With the AQI (Air Quality Index) of several cities in the world already beyond alarming levels, there is a drive to control or improve automotive emission levels and reduce the overall carbon footprint. AI and ML can lead to a major turnaround for the vehicle industry.
As per a BCG study, applying AI to corporate sustainability can increase savings and revenue to the tune of USD 1.3 trillion to USD 2.6 trillion by 2030.
It would require vehicle companies to employ AI-powered data engineering that helps in automated emission tracking. It can also collect data from several operational activities throughout the value chain. It can also source data from other sources, such as satellites, layer them up to find missing data, and undertake meaningful emission monitoring actions.
Fleet monitoring system
It is needless to state that AI can collate a multitude of complex data to figure out patterns beyond the periphery of a normal human brain. AI automatically breaks big data into actionable insights via digestible reports and visually appealing dashboards. It uses camera vision and advanced data, such as driver behavior and environmental conditions, to automate and augment the decision-making process regarding fleet management.
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Generative designs
Modern-day vehicles are engineering marvels and require a lot of sophistication. So engineering companies are quickly adopting generative designs to help them develop highly-optimized vehicles. They leverage AI and ML to break down complex engineering processes and train these software to curate optimized designs based on certain parameters.
These work in partnership with other techs, such as 3D printing, to undertake rapid prototyping and mass customization. The adoption of generative design can have myriad benefits for car manufacturers. For example, General Motors used it to reduce the weight of their vehicles.
In-car voice assistant
Alexa has become an integral part of our homes. Similarly, the voice assistant is slowly becoming a vital addition to cars and other vehicles. While general intelligent assistants inform us about the topics we require them to, in-car voice assistants use sensors to monitor blind spots and assist the driver in steering, pedestrian, and obstacle detection. These real-time updates can help curtail unwanted road accidents and enable drivers to be more proactive than reactive.
Scalability and precision are the two primary reasons car manufacturers are increasingly adopting AI for their marketing. Automotive manufacturers can undertake smart targeting with AI in the mix that uses historical data and predictive analytics for optimum results. In addition, it can also help you with your targeting and re-targeting endeavors that would help you level up your marketing without overspending on resources.
Predictive Vehicle Maintenance
A study by McKinsey states that AI's strongest suit is its ability to inspect automotive parts and products over time, enabling its ML algorithms to get stronger in identifying shortcomings over time. In the 20th century and even in the first 15 years into the 21st century, automotive maintenance was more preventive than predictive.
We would do our guesswork and calculate a suitable time for an overhaul accordingly. But with AI and ML working in tandem, we are shifting towards predictive maintenance. The AI-powered software can provide the user actionable insights about the vehicle and its maintenance in real-time. It uses many data points to alert users timely, thereby improving vehicle availability efficiency and reducing depreciation.
Manufacturing Process optimization
AI is not only changing the way vehicles are built but is also changing the entire development process. With the increase of IoT and the massive adoption of AI to highly-critical tasks, we are already experiencing how it leads to manufacturing process optimization.
Its predictive abilities help reduce equipment failure; robotics and human collaboration automate tedious processes and enable humans to focus on strategic tasks and computer vision leading to reduced quality control issues. In addition, it leads to more intelligent project management led by a leaner supply chain and improved business support functions for holistic improvement of the entire process.
Quality control
The traditional machine vision systems quickly pointed out structural scheme issues but weren't capable enough to trace abnormality during the production process. It led to the process being handed over to humans who relied on their expertise and experience to point out issues.
But the, modern workplaces quickly realize ML's abilities and employ them in conjunction with automotive safety. They also use deep learning to enhance quality control, tracking further and checking 100% of their products instead of going the sampling way.
Route optimization
The ability of AI to fiddle with big data and process a multitude of data seamlessly impacts route optimization for car manufacturers. It brings about last-mile efficiency, which uses trip sheets and real-time statistics to calculate the approximate time for covering a route.
In addition, big data enables the usage of spatial information that considers a plethora of factors to suggest the best ways while managing inventory and other critical needs. If implemented properly, route optimization can save tonnes of money and time, which is invaluable for the automotive and logistics industries.
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Supply chain optimization
The supply chain was more straightforward back in the day. Today, the process has become far more complex with increased interlinking, making it difficult for humans to handle it manually. Further, the pandemic has increased volatility and a renewed focus on sustainability, forcing people to rethink supply chain optimization and resilience.
With AI in the mix, there is a drive to transform the supply chain, and car manufacturers see many benefits, with improved decision making and visibility leading the roost. The AI-powered supply chain will utilize cognitive predictions and recommendations to improve performance and is expected to power modern-day enterprises. In addition, ML can help them understand potential shortcomings and room for optimizations, thereby allowing them to embark on holistic improvement.
Vehicle prototyping
Like any other industry, the automotive industry suffers from cut-throat competition that necessitates rapid prototyping. But creating rapid prototypes that are not functional would not be feasible for car manufacturers. The new-age AI-powered prototyping uses innovative product development processes that eliminate several pain points present in the traditional prototyping and streamlines the entire process.
The usage of AI enables better CAD rendering and improves the prefabrication efficiency. In addition, it also helps enhance the quality of the product by allowing ML to point out design anomalies while supercharging the simulation process. In addition, artificial intelligence also helps automate repetitive tasks, enabling designers to focus on the more critical tasks.
Video analytics for shop floor
It took time for the world to realize that video analytics can have many more use cases extending far beyond mere surveillance and security purposes. The modern surveillance systems are integrated with computer vision, enabling them to make smart decisions. One such exciting use case for the retail sector would be to automate shop floor management, given that it is a repetitive task, but tedious presently.
For this, AI systems have transformed data into operable entities by assigning intelligent attributes. The algorithm can undertake data segmentation and establish patterns from the derived data sets. These use real-time feed and historical data to understand ideal behavior and violations and devise an optimum floor plan that would work in the best interest of automotive dealers.
Warehouse sorting
The automotive industry has been relentless when it comes to achieving operational efficiency. But with so much data in hand, warehouse management inadvertently became a significant pain point for many car manufacturers.
The inculcation of robots in the warehouse has enabled them to automate a considerable part of the logistics network where these machines can track, lift, and sort items. It has allowed humans to focus on the more critical processes and seamlessly excel at warehouse sorting. It has also helped them reduce costs while improving quality control, contributing to manufacturers' improved customer experience and profit margins.
Future of Automotive Manufacturing
AI was expected to make a splash. And it did.
The evolution of efficient algorithms combined with top-notch hardware gives automotive manufacturers a new lease of life. It has allowed them to streamline many processes, reduce human dependence throughout the value chain, and churn out improved results. However, we are yet to use AI to its full potential, and the future looks exciting.
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