Thursday, June 13, 2024

Artificial Intelligence news

Apple is promising personalized...

At its Worldwide Developer Conference on Monday, Apple for the first time...

What using artificial intelligence...

This story originally appeared in The Algorithm, our weekly newsletter on AI....

The data practitioner for...

The rise of generative AI, coupled with the rapid adoption and democratization...

Five ways criminals are...

Artificial intelligence has brought a big boost in productivity—to the criminal underworld.  Generative...
HomeNewsModernizing the automotive...

Modernizing the automotive industry: Creating a seamless customer experience 


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 new age of connectivity has pushed original equipment manufacturers (OEMs) to rethink how they develop vehicles that can take advantage of data, automation, and connectivity and meet customer demands for more personalized and predictive products. As a result, the future of mobility will be a digital ecosystem in which 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 discuss 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 become ubiquitous, the automotive industry has an opportunity to respond to 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 classify the issue. But Sayer paints a picture of a connected vehicle that draws on data from a wide internet-connected ecosystem that provides a customer with a diagnosis of the issue via phone notification. Even further, a connected vehicle can reference service history to suggest and schedule a service appointment and find the most viable navigation route, offering customers even more convenience.  

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

“We can use the same cycle of test and get feedback, build further, optimize, improve, which is same cycle as the software industry has been using for years. Now we can use it with connected vehicles as well. And this truly enables us to be much closer to the customers in the automotive industry and work backwards from the customer if you wish,” explains Uvarova.  

OEMs looking to modernize their processes and keep industry pace need to follow a customer-centric approach that tackles innovations working by backward from customer needs. This method looks to build innovations and solutions that meet specific issues identified by customer data and research. Built-in car features like music-syncing often become obsolete quickly because companies fail to imagine how they fit into a customer’s life and the existing technologies they favor.   

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

“Just like any other data-driven, connected type of device, there is going to be data management implications across the board that perhaps haven’t been thought of previously, but will need to 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 companies are adopting a customer-first, data-driven mindset to incorporate advanced technology such as AI, machine learning, cloud and edge computing, and digital twins into both production and products.   

The automotive sector generates vast amounts of data; and the amount of this data will only continue to increase as autonomous and connected vehicles collect real-time data on customer habits and preferences. Turning this data into relevant insights depends on a company’s approach to innovation.   

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

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

Driving continuous improvement in products and services 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.   

“In order to ensure that we support innovation and bring state-of-the-art, latest generation software defined vehicle to market,” says Uvarova, “a lot of departments have to work together, and they have to work together very quickly, actually, in an agile manner.”  

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

“A lot of great innovations, they are born from cross-pollination, from collaboration, from synergies between very different departments of the same 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 work. Historically, Sayer says, many industries, including auto, have excelled working in these silos. But working with agility, creating connected products, and getting the most out of the data it produces requires collaboration and data sharing.   

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

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

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

Ultimately, the specific working methodology is less important than the prioritization of customer needs and an understanding of the value of collaboration both internally and with external technology companies.    

“At the end of the day,” says Uvarova, “it’s really not about one or the other methodology, but it’s about making sure that as an industry, we are very much open to opportunities, to partnerships, and to actually empowering our teams to work together and to do the right things, rather than just expecting them to operate in a top-down regulated environment.”

The future of the automotive industry   

It’s clear that digital modernization will have a profound imprint on the automotive industry as connected and autonomous vehicles gain popularity, remote repair and analytics are enabled by AI and machine learning, and OEMs collaborate with technology companies to build new innovations. Finding their footing in the future of mobility will require companies to prioritize customer needs and maintain the careful balance between governance and modernization.   

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

“It’s going to require more of an engineering mindset and a customer-central type of mindset to enable the possibilities that are out there,” says Sayer. 

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.



Article Source link and Credit

Continue reading

OpenAI’s latest blunder shows the challenges facing Chinese AI models

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday. Last week’s release of GPT-4o, a new AI “omnimodel” that you can interact with using voice,...

Meta says AI-generated election content is not happening at a “systemic level”

Meta has seen strikingly little AI-generated misinformation around the 2024 elections despite major votes in countries such as Indonesia, Taiwan, and Bangladesh, said the company’s president of global affairs, Nick Clegg, on Wednesday.  “The interesting thing so far—I stress,...

Noise-canceling headphones use AI to let a single voice through

Modern life is noisy. If you don’t like it, noise-canceling headphones can reduce the sounds in your environment. But they muffle sounds indiscriminately, so you can easily end up missing something you actually want to hear. A new prototype...