The automotive industry is rapidly changing as connected and autonomous vehicles — enabled by AI and machine learning — are transforming transportation to create a seamless and personalized customer experience. The modernization of systems and software is steering vehicles to be more intelligent than ever, improving driving experiences and propelling operational efficiencies. From simulation testing on the factory floor to lifecycle predictive maintenance, connected vehicles drive success in an increasingly competitive landscape. 

The brand new age of connectivity has pushed original equipment manufacturers (OEMs) to rethink how they develop vehicles that may benefit from data, automation, and connectivity and meet customer demands for more personalized and predictive products. Because of this, the long run of mobility shall be a digital ecosystem wherein digital services, connectivity, and data are linked in an end-to-end architecture.   
 
MIT Technology Review recently sat down with Eddie Sayer, chief technology officer at Kyndryl and Maria Uvarova, head of software product management at Stellantis to debate the ways advanced technologies can infuse efficiencies, predict issues, improve performance, and create an optimal customer experience.  

A customer-centric approach to digital modernization 

As digital technologies like AI turn into ubiquitous, the automotive industry has a chance to answer customer needs as they arise based on real-time collected data and insights.   

Sayer offers an example of the dreaded service indicator light coming on. Typically, a customer would see the dashboard light and follow up with a mechanic to get a diagnostic code to categorise the difficulty. But Sayer paints an image of a connected vehicle that attracts on data from a large internet-connected ecosystem that gives a customer with a diagnosis of the difficulty via phone notification. Even further, a connected vehicle can reference service history to suggest and schedule a service appointment and find essentially the most viable navigation route, offering customers much more convenience.  

Connected vehicles provide OEMs insight into how customers are driving in real time and permit them to make faster adjustments to enhance experiences and optimize their manufacturing processes.   

“We are able to use the identical cycle of test and get feedback, construct further, optimize, improve, which is same cycle because the software industry has been using for years. Now we will use it with connected vehicles as well. And this truly enables us to be much closer to the purchasers within the automotive industry and work backwards from the shopper if you happen to wish,” explains Uvarova.  

OEMs seeking to modernize their processes and keep industry pace have to follow a customer-centric approach that tackles innovations working by backward from customer needs. This method looks to construct innovations and solutions that meet specific issues identified by customer data and research. Built-in automobile features like music-syncing often turn into obsolete quickly because corporations fail to assume how they fit right into a customer’s life and the present technologies they favor.   

But untapping the potential of digital technologies means also considering the privacy and security implications of getting access to a 360-degree view of customer driving habits, application usage, maintenance, and repair history. Governance and oversight are a critical component of implementing digital technologies.   

“Just like several other data-driven, connected style of device, there’s going to be data management implications across the board that perhaps have not been considered previously, but will must be addressed going forward,” says Sayer.  

Reimagining approaches to innovation

The changes ushered in by digital technologies are forcing OEMs to rethink how they operate in all areas of business. To reimagine research and development, supply chains, and manufacturing, many corporations are adopting a customer-first, data-driven mindset to include advanced technology resembling AI, machine learning, cloud and edge computing, and digital twins into each production and products.   

The automotive sector generates vast amounts of knowledge; and the quantity of this data will only proceed to extend as autonomous and connected vehicles collect real-time data on customer habits and preferences. Turning this data into relevant insights will depend on an organization’s approach to innovation.   

In comparison with a phone application, a connected vehicle software malfunction can have dangerous safety consequences while driving. Due to this fact, automotive production and innovation cycles must turn into interconnected and pass many quality assurance checkpoints before they will be sold. But as customers grow accustomed to rapidly evolving digital technologies and the market continues to evolve, automakers and OEMs need to shorten these cycles without compromising safety and security.  

Digital twins, a virtual analog of a physical automobile’s software and mechanical and electric components that may carry real-time inspection data, maintenance history, warranty data, and defects, are one among the various emerging technologies that might help bridge this gap, Uvarova says.    

Driving continuous improvement in services and products means working methodologies must also complement the technology used to innovate modern software-defined vehicles. Uvarova notes that the agile working methodology — which manages projects through iterative phases that involve cross-departmental collaboration and a continuous improvement feedback loop — would align with modern innovation practices and serve OEMs well.   

“With a purpose to be certain that we support innovation and produce state-of-the-art, latest generation software defined vehicle to market,” says Uvarova, “quite a lot of departments need to work together, and so they need to work together in a short time, actually, in an agile manner.”  

What is commonly missing from traditional OEMs is collaboration between departments as many processes proceed to work from the top-down and are confined to silos.    

“A number of great innovations, they’re born from cross-pollination, from collaboration, from synergies between very different departments of the identical company, also sometimes from partnerships,” says Uvarova.    

Data silos, where insular processes and data streams can’t be easily shared between departments and operation phases, often cause inefficiencies and duplication of labor. Historically, Sayer says, many industries, including auto, have excelled working in these silos. But working with agility, creating connected products, and getting essentially the most out of the info it produces requires collaboration and data sharing.   

“It then opens up many other possibilities for doing cross-departmental, cross-functional business use cases. It’s going to require less silos and more collaboration, and I believe that is key,” says Sayer.   

To interrupt out of legacy working methods, many OEMs are embracing partnerships with large technology corporations to learn find out how to incorporate modern software development practices. 

For instance, Microsoft offers automotive OEMs the framework and infrastructure to develop their very own custom autonomous development tools. Providing non-differentiated tools and technology that may give OEMs greater efficiency enables a continuous feedback loop to create repeatedly improving products. Daimler Trucks North American used Microsoft Azure, its cloud computing service, to construct a program for cloud-connected vehicles that makes higher decisions, improves fuel efficiency, and optimizes road time productivity.   

Ultimately, the particular working methodology is less vital than the prioritization of customer needs and an understanding of the worth of collaboration each internally and with external technology corporations.    

“At the tip of the day,” says Uvarova, “it’s really not about one or the opposite methodology, but it surely’s about ensuring that as an industry, we’re very much open to opportunities, to partnerships, and to truly empowering our teams to work together and to do the best things, slightly than simply expecting them to operate in a top-down regulated environment.”

The long run of the automotive industry   

It’s clear that digital modernization could have a profound imprint on the automotive industry as connected and autonomous vehicles gain popularity, distant repair and analytics are enabled by AI and machine learning, and OEMs collaborate with technology corporations to construct recent innovations. Finding their footing in the long run of mobility would require corporations to prioritize customer needs and maintain the careful balance between governance and modernization.   

The important thing trends Sayer and Uvarova see driving the long run of the automotive industry include autonomous vehicles, connectivity, shared mobility, and sustainable solutions. And while rapid changes flood the automotive industry, corporations are tasked with finding compatibility between oversight that protects consumer safety and privacy and agile working methods that innovate and iterate on the speed of business.   

“It will require more of an engineering mindset and a customer-central style of mindset to enable the probabilities which can be on the market,” says Sayer.