Getac on AI’s transformative impact on automotive production and aftersales

Artificial Intelligence (AI) has the power to revolutionize the whole automotive value chain, by fully connecting the process from the sourcing of manufacturing inputs through to vehicle end-of-life.

Digital twins of vehicles on the road will allow OEMs to monitor vehicle performance and provide detailed insights into driving behavior, environmental conditions, and vehicle specifications, which will significantly enhance the industry’s ability to predict and prevent failures.

AI is not only making the driving experience more accessible and affordable but also addressing key industry challenges, such as the shortage of skilled technicians. Getac is at the forefront of this revolution, developing AI-ready rugged tablets and laptops designed to support technicians and streamline operations.

Automotive Industries (AI) asked Russell Younghusband, Global Automotive Director at Getac, to share his thoughts on how AI is revolutionizing automotive production and aftersales.

Younghusband: AI is revolutionizing automotive production and aftersales for its ability to harness vast amounts of data.

It can optimize operations, enhance customer experience, and boost profitability.

The industry is grappling with manufacturing costs to remain competitive, especially in the EV segment. AI allows full connectivity through-out the value-chain from engineering, manufacturing, logistics and downstream in retail aftersales.

Digital 2.0 will allow OEMs to monitor connected vehicles (vehicle-to-cloud) and key parts such as EV battery behavior.

In the future, every vehicle on the road will have its own digital twin, so we can see every aspect of driving behavior, environment conditions, and the vehicle specification to better predict failures before they happen and when they do, ensure the data can be used in R&D and manufacturing to apply real-time changes.

In aftersales, outdated dealer management systems often hinder efficiency and customer satisfaction.

AI offers the automotive industry the opportunity to transform aftersales service from a transactional to a connected relationship-driven experience.

AI streamlines processes like self-service appointment scheduling, resource allocation based on technician skill levels, and repair order management, such as time clocking and automated status updates for the customer.

By automating certain tasks, dealerships can reduce errors for improved first time fix rates, improve efficiency by eliminating idle time, and free up staff so they can focus on the business with real-time reporting to react and keep the customer front and center of their mind.

Additionally, AI empowers dealerships to build stronger customer relationships.

Predictive maintenance alerts based on driving habits and vehicle data are just one example of how AI can help increase customer loyalty by avoiding costly repairs.

AI can also play an important role in helping technicians, whether they are experienced or junior, to remain in touch with the latest technical information.

AI will provide guidance to speed up the diagnostic process by recommending the root cause probability of DTCs and can access siloed technical systems such as the knowledge base, wiring diagrams, EPCs and warranty to save the technician the laborious task of clicking through copious amounts of technical documentation and schematics to locate the information they need.

AI: What specific AI-driven innovations are you most excited about in the automotive industry?

Younghusband: There’s a lot to be excited about when it comes to the potential of AI in the automotive industry. It can be a game changer in so many ways.

For example, the trend towards software defined vehicles (SDVs) continues to be a paradigm shift in the industry and will serve to connect the OEM to the end customers better – providing drivers with more convenience, safety and comfort.

All the data collected will be feeding the AI super computers to adapt the vehicles of today into a truly optimized mobility future that meets the regional needs of the global car parc.

AI will also use the data collected to feed the eco-system of mobility service and solution providers to develop new applications to enhance the driving experience and make it more accessible and affordable, for example insurance premiums tailored to a fleet or individual’s circumstances.

AI that automates mundane processes so humans can get on with more interesting work or that helps technicians solve problems make a lot of sense. Especially with the flood of vehicle types entering the market and the diminishing talent pool of skill technicians to work on them, which is a real industry problem.

AI: How is Getac integrating AI solutions to streamline operations and improve processes within the automotive sector?

Younghusband: Getac is particularly focused on how AI can help technicians work smarter, not harder, and we’re integrating AI solutions in a few key ways.

First, we’re developing hardware specifically designed for AI applications, like our AI-ready rugged tablets and laptops. These devices are essential for running the complex AI software needed for tasks such as:

• AI can automate process and take on the heavy lifting of repetitive tasks
• AI can self-learn and make decision on the business with the help of data – For example which task should go to which technician based on the vehicle type, age, complexity of the task, availability of bay, parts, and special tools
• AI can help technicians identify a part on a vehicle
• AI can help technicians by searching data base silos and deliver the required information in real time along with recommend actions
• AI can replan a busy work in seconds factoring in all job cards, customer promise times, teams, customer waiting, courtesy vehicle availability
• AI training and neuroscience methodology can act as an AI mentor and assistant to technicians when they need extra training and guidance
• AI applied in the diagnostic process can provide a probable root cause fault of the vehicle when the technician pulls multiple DTCs, saving significant time when investigating on DTC at a time. This can also factor in with historic service, warranty, insurance, and geographical data.
• In some markets, data privacy will not allow an application to capture, record humans or license plates without the consent of the individual or owner of the vehicle. AI redaction techniques will ensure the dealership operation is not exposed to accidently recording data they are not allowed to.

Please provide an example of how AI has improved quality control and enhanced customer experience in automotive aftersales?

Younghusband: Ford’s adoption of AI in manufacturing is a prime example of this technology’s impact.

By using AI to detect defects in real-time, Ford has significantly reduced the number of faulty components and improved overall vehicle quality. This not only saves the company money but also enhances customer satisfaction by minimizing recalls and breakdowns.

While the industry hasn’t implemented AI in quality control yet, there is big potential.

For example, AI-powered image recognition could inspect parts for defects more accurately and quickly than human inspectors. This would lead to fewer faulty parts reaching customers and improve overall product quality.

Additionally, AI could analyze customer feedback to identify recurring issues, allowing us to address problems more efficiently and enhance customer satisfaction.

Also, AI can analyze repair data to identify recurring problems, leading to preventative measures and optimized repair processes.

On the customer-facing side, AI-driven chatbots provide instant support, answering frequently asked questions and scheduling appointments.

Predictive maintenance alerts, powered by AI, keep customers informed about their vehicle’s health and prevent unexpected breakdowns, fostering trust and loyalty. As fixed operations contribute significantly to dealership profits, these AI-driven improvements are crucial for boosting customer retention and overall dealership success.

Ultimately AI will speed up processes and help eliminate re-repairs and unwanted manufacturer recalls by using the power of the data to make more accurate decisions and making sure the vehicles on the road are designed and manufactured to perform taking into account the nuances within each market.

AI: What do you see as the biggest challenges automakers face in adopting AI technologies, and how can these be overcome?

Younghusband: The automotive industry is undergoing a digital transformation, with over 550 manufacturing plants globally adopting smarter technologies.

However, integrating these innovations into a decades-old industry presents challenges, and automakers face several hurdles in their AI journey.

First, managing vast amounts of data is crucial for AI, but collecting, cleaning, and normalizing this information presents significant challenges. Additionally, safeguarding sensitive vehicle and customer data from cyberattacks is paramount.

Attracting and retaining AI talent with automotive expertise is another obstacle.

Lastly, the evolving regulatory landscape for AI, particularly in autonomous vehicles, creates uncertainty. To overcome these challenges, automakers must prioritize data management and security, invest in talent development, and actively engage with policymakers.

Building robust data infrastructure, implementing strong cybersecurity measures, and fostering partnerships with universities and tech companies are essential steps.

Getac is focused on helping to address these complexities by combining connected AI automotive focused applications and rugged computing solutions.

How does AI enable automotive companies to provide digital vehicle health checks and track service progress in real-time?

AI: Younghusband: By processing data from various vehicle sensors, AI algorithms can monitor vehicle performance, identify potential issues, and predict maintenance needs. This information can be presented to customers in an easy-to-understand format, providing transparency into their vehicle’s health.

Additionally, the vehicle health check process (or multi-point inspection) is a decades old process used throughout the industry to assess wear and tear items on the vehicle to ensure the safety and roadworthiness.

This is a duty of care provided by the OEMs network and service and repair operations to help the customer understand the full health of their vehicle. In the past (although still many are doing this) a paper check sheet is used by the service advisors and technicians to capture the results and to identify what needs attention.

These days there is a trend towards adopting an electronic version of the health check (EVHC) that can run on a rugged tablet and provides the staff a check sheet and opportunity to capture images and take videos to share along with a digital health report and estimate for required red or amber work.

This has proven to be an effective tool to bridge the technical gap between the customer and the SMR operation and generates a higher level of converted upsell work. AI plays a key role in providing accurate VIN based maintenance check sheets and can assign tasks to the workshop based on the type of work and the skills available.

EVHC systems with livestream video call capabilities, parts identifiers and AI body damage inspection further enhance the trust and transparency between the dealer and the customer.

AI: With only 3% of automakers currently taking advantage of AI for digital services, what are the barriers to wider adoption?

Younghusband: Several factors could contribute to this low adoption rate. To begin with, acquiring, cleaning, and structuring vast amounts of data from various sources poses a significant challenge.

Automakers often struggle with data quality and accessibility, which are essential for AI applications.

There is also a global shortage of AI talent, making it difficult to find and retain skilled professionals with automotive industry knowledge.

Additionally, implementing AI requires substantial investments in infrastructure, including computing power and data storage.

Cybersecurity concerns also hinder adoption, as protecting sensitive vehicle and customer data is paramount.

Furthermore, the evolving regulatory landscape for AI creates uncertainty for automakers, impacting investment decisions.

Finally, internal resistance to change and demonstrating a clear return on investment for AI projects can be obstacles to wider adoption. There is evidence of OEMs recruiting chief information officers (CIOs) from the tech industry to lead the transformation.

OEMs will become leading global software companies rivaling the best technology companies in the world. AI is expected to be used throughout the value chain and projected to rise from single digit percentages to upwards of 80% in the next 3-5 years.

AI: How will AI shape the future of automotive production and aftersales over the next decade?

Younghusband: On the manufacturing side, AI will optimize production lines through predictive maintenance, quality control, and robotics. It will enable real-time adjustments to assembly processes, reducing defects and improving efficiency. And AI-driven design tools will accelerate the development of new vehicle models and features.

In aftersales, AI will transform customer experiences.

Predictive maintenance, powered by AI, will allow for proactive service scheduling, minimizing vehicle downtime. Virtual assistants and augmented reality will guide customers through maintenance tasks or repairs.

Furthermore, AI-driven analytics will optimize inventory management, ensuring parts availability and reducing costs. As autonomous vehicles become more prevalent, AI will play a crucial role in managing vehicle fleets, optimizing routes, and predicting maintenance needs.

AI: How can automakers leverage AI to bridge the revenue gap and unlock the potential of digital services in the industry?

Younghusband: AI is a game changer for automakers seeking to diversify revenue, with access to data that can unlock new business opportunities.

By embracing AI, automakers can not only bridge the revenue gap but also position themselves as leaders in the evolving automotive industry.

Solutions including predictive analytics provide valuable customer insights, enabling tailored campaigns and personalized services. Additionally, automakers can monetize data (for a reasonable fee) through partnerships and by offering data-driven services like insurance telematics.

Beyond customer interactions, AI drives operational efficiency and new revenue streams.

Predictive maintenance, enabled by AI, optimizes service intervals, and reduces downtime.

Moreover, AI can optimize supply chains, forecast demand, and improve quality control.

Ultimately, if AI can bring down costs for customers, such as vehicle purchase price or aftersales services, it’s a win-win for everyone, and Getac is playing an active role in that transition.