Tools & Platforms
Will AI transform the way used cars are bought and sold?
03 July 2025
This year has seen a surge in artificial intelligence (AI) advances. But what impact has this technology made on the used-car retail industry, and what is yet to come? Autovista24 journalist Tom Hooker takes a deep dive into the subject.
Through the likes of ChatGPT, Google Gemini and Microsoft Copilot, AI has transformed the way we work. Forbes reported that the technology will reach a market revenue of $1.33 billion (€1.18 billion) by 2030. Meanwhile, 64% of businesses believe that artificial intelligence will help increase their overall productivity.
Within the automotive sector, AI is already embedded in manufacturing and quality control, such as BMW’s ‘Factory Genius’ assistant. It is also being used to improve connected car experiences. Volvo Cars is using AI to enhance advanced driver-assistance systems (ADAS).
How would the technology work in the world of used-car retail? It could give customers a more personal and efficient experience. But how does this translate into realistic sales and revenue growth for dealerships?
AI and disruption
Answering this question means stepping back to look at the AI industry and the anticipated changes just around the corner.
‘Now we see what we believe to be also a highly disruptive change coming up with artificial intelligence,’ stated McKinsey & Company partner Peter Cholewinski at the Used Vehicle Retail Summit.
‘The topic is not new. AI has been around for many years. However, with the introduction of ChatGPT, this has arrived in our daily lives and in the lives of companies. The speed of progress is just amazing,’ he added.
ChatGPT is an example of a generative large language learning model (LLM). This means it can create content such as text and images in response to a person’s prompt or request.
To do this, it relies on using machine frameworks known as deep learning models. These algorithms simulate the human brain’s learning and decision-making processes.
Cholewinski showed the growing number of LLM releases. In 2024, 122 new models entered the market. This was up from the 109 LLMs launched in 2023 and a significant increase from 29 releases in 2022.
From left to right: Peter Cholewinski, McKinsey & Company partner. Dr Lisa Schrewentigges, McKinsey & Company project manager.
‘In 2025, you have many models out there, and those models are becoming smarter. We are now not talking about large language models, but about reasoning models. Additional tools are also coming out, like deep research. The machine can go on its own onto the internet and figure a lot of information out by itself,’ Cholewinski explained.
Agentic AI can capture value
While generative AI LLMs depend on users’ prompts and requests, agentic AI LLM models are designed to autonomously make decisions and act, with the ability to pursue complex goals with limited supervision, IBM wrote. This combines the flexibility of LLMs with the accuracy of traditional programming.
‘This year, everybody is talking about agentic AI. When you take those models with reasoning capabilities, they can plan and think about what they need to do to achieve a goal. You can also have several of them working together, exchanging basic information, reviewing each other, and trying to solve a problem on their own.
‘So, it is not only about one chatbot that you talk to, but end-to-end processes and how several agents can achieve something useful and valuable.
‘Agentic machines can tap into different workflow steps and coordinate across those workers. This means we have more automation possibilities across workflows. This is where most of the value will be captured, especially as they become smaller,’ commented Cholewinski.
The first fully autonomous agentic LLM model, Manus, was released in March 2025, as written by Forbes.
AI transformation troubles
‘Everybody is trying it out, but only a very small number can say we invested something, and we actually captured something. This is because it is very difficult,’ said Cholewinski.
‘You need to have the technology, but you also need to have the right talent to understand how to use that technology and an operating model that will drive the change management to scale and adopt this technology,’ he added.
Cholewinski showed that 88% of companies attempt a digital and AI transformation. However, just 25% meaningfully progress in their digital and AI transformation. Furthermore, just 10% of enterprises have AI at scale, and under 5% of scale use-cases deployed are active across full workflows.
‘In the cases where we are seeing value being captured, they are thinking about several use cases together and in an agentic fashion,’ he highlighted.
AI assists dealership leads
So, what real-world use cases are already being implemented in the automotive retail sector, and what impact is this having?
One example is a generative AI-based tool that can tailor and personalise messages for customers and online leads. The unnamed product was built for one of the largest German dealer groups. This means covering 200 different dealerships and a database of over 500,000 existing customers from vehicle sales.
‘What they struggled with is looking into the lead management and how to have a very structured approach in contacting existing customers in a very fast way, which is also very tailored,’ explained McKinsey & Company project manager Dr Lisa Schrewentigges.
In her presentation, she showed that the dealer group previously spent around five to 10 minutes on every customer outreach. They also struggled with how to respond to incoming website leads and how to personalise this interaction.
Fast development times
‘What we have done together with them is, within six weeks, develop a generative AI tool, which allowed them to identify the most promising leads. Secondly, tailor the messages towards those leads and be fast in answering those leads,’ she outlined.
‘With generative AI and agentic AI, you can implement those kinds of solutions very fast because you do not need to train the AI anymore. These models are so powerful that you can actually use them off the shelf,’ noted Cholewinski.
‘This is also where the potential lies. You can think about your end-to-end processes, where there is a lot of manual work that you could improve. Then, think about the several use cases that make sense to improve productivity or sales with this technology,’ he added.
The sales agent journey
Schrewentigges walked through the typical sales agent journey. This starts with selecting a customer and thinking about which promotion to send. Then, interacting with the customer, and in the end, moving this customer towards a decision.
‘Where we helped here was bringing together the customer information that they already have on the system, matching it with third-party data and different website data,’ she said.
‘Then you have a full, enriched customer profile, identifying the most promising leads and personalising communications with a specific customer, which helps the sales agent convert them to a sale,’ Schrewentigges said.
A dashboard then enables the sales agent to see a full customer overview. It can prioritise the customer based on a lead score and suggest specific email campaigns. The dashboard also displays different customer groups, such as existing customers, website leads, and follow-ups.
She then showed the typical outcome of this personalised messaging. Various data points can be used by the generative AI to create an individualised email to the customer.
‘They were able to not only send out emails, but also very personalised phone calls based on the information that we put together. This, in the end, led to much faster reply times from website leads, because we had a very standardised approach in answering typical emails, but also it led to much more personalised communication,’ Schrewentigges said.
An instant impact?
‘We had a lot of impact regarding the speed of answers, personalised communication, but also in the end, this will ultimately sell cars much faster,’ she stated.
The dealer group recorded an increase of more than 20% in conversion rates. Each sales representative recorded an additional 15 to 25 vehicle sales annually on average.
This was made possible through a 70% to 80% efficiency gain, which meant more time to sell cars. Furthermore, 10 to 15 times more customers were approached with relevant sales campaigns. However, there were still significant challenges and concerns for the tool to overcome.
‘You always need to drive a balance between not using too much information because once you go into too many details that the AI might know, it becomes very creepy,’ commented Cholewinski.
Additionally, as AI becomes more powerful, could this put jobs in dealerships at risk in the future?
‘Even though generative AI solutions will help with emails, there will always be a personalised component in contacting the dealership, having a phone call, and visiting the car,’ said Schrewentigges.
‘I think it will, in a certain part, probably affect how vehicles will be sold, but we always need this component. People come to the dealership and want to see and feel a vehicle,’ she explained.
Virtual assistants for retailers
Elsewhere, Novaco AI provides virtual assistants that can be used on automotive retailer websites. By connecting to their data, the assistants are designed to improve dealership efficiency, automate conversations, and optimise customer interactions.
‘It is connected to inventory, virtual planning, digital work orders, but also your lead management system,’ outlined Novaco AI CEO Maarten Bekkers.
The assistant started with Google AI in 2019. After LLMs were released, the tool began utilising them. It is now beginning to use agentic AI models and is bringing its assistant to WhatsApp.
The company also provides a virtual assistant for dealership employees to increase their efficiency and find information quickly. The AI companion is also connected to pricing information.
‘So, if somebody calls and asks, “what would it cost to replace my clutch for the car with that number plate?”, you just fill in the question to the companion and it will generate the answer within a few seconds. ‘It is a real virtual employee that works for you,’ said Bekkers.
The assistants can also help dealerships with common queries, freeing up time for employees.
‘Complaint number one at dealerships is that the phone keeps on ringing with the same questions every day. The majority of people who book a service call the dealer. It is the most expensive resource of the dealer is actually booking the service, it is crazy,’ he commented.
‘So, you should turn it around. If people really want to call, they can still call. But in the near future, a virtual assistant will be on the phone, having the same conversation as a human and making a booking,’ Bekkers added.
AI’s organisational prowess
‘AI has been instrumental for our success,’ said Lizy’s chief of staff, Nicolas Daive, as he began his presentation. The company is an online B2B car leasing platform offering used vehicles to companies.
‘Used cars are more operationally complex and messy than new cars. Despite that, because you have lower asset value, lower leasing prices and longer holding periods, you can be extremely efficient. With AI, we were able to transform this messy product into a very simple operation,’ highlighted Daive.
‘To make sure we have the best possible offering, we source vehicles all over Europe, across more than 100 suppliers. This means that we have more than 100 data formats, data types, processes, and ways of working.
‘In the past, working with this number of suppliers would have meant you needed four or five full-time employees due to the complexity it brings. With AI, we were able to do this with half a full-time employee,’ he commented.
Daive explained the process of buying cars from a supplier, with a PDF containing data. An employee then forwards the PDF to their AI agent with a few instructions. This includes scheduling a pickup time for the vehicles and pre-pricing them.
‘All that is done from the click of a button. In the past, we probably would have had a full-time employee that is doing a lot of copy and pasting, getting the right data into the right fields, and talking to a lot of departments,’ he noted.
‘Automation is nothing new. Commission is something we have been doing for almost four decades. What is new is that AI allows us to automate chaos. It can take unstructured data, structure it, then send it to the right places,’ concluded Daive.
Tools & Platforms
Relativity Scales Generative AI Availability Across Asia
RelativityOne users in five more countries will be empowered with enhanced document review and privilege identification capabilities
CHICAGO, July 7, 2025 /PRNewswire/ — Relativity, a global legal technology company, today announced that two of its generative AI solutions, Relativity aiR for Review and Relativity aiR for Privilege, will now be made available to all RelativityOne instances located in Hong Kong, India, Japan, Singapore and South Korea. Expanding on its previous availability, legal, investigation, and compliance teams in Asia will be equipped with the generative-AI powered document review solution and privilege review solution to help navigate the full spectrum of legal data challenges while reaping the benefits of better infrastructure and privacy.
“Asia’s diverse legal landscape presents unique and evolving challenges, and legal teams across the region need technology that can keep pace,” said Chris Brown, Chief Product Officer at Relativity. “Whether it be for litigation, regulatory responses, or internal investigations, Relativity aiR products provide the necessary features to manage large volumes of data more effectively. As adoption grows across the globe, and real-world use cases continue to demonstrate impact, Relativity’s customers and partners can feel confident in the power and practicality of AI in their workflows.”
Enhancing the capabilities of legal teams across Asia with intelligent tools
Customers and partners in five additional countries will now be able to leverage aiR for Review and aiR for Privilege to deliver exceptional efficiency and accuracy in document and privilege review. This regional expansion underscores Relativity’s commitment to providing innovative solutions that align with the evolving needs of legal professionals in Asia and across the globe.
“Customers in Asia are facing a perfect storm — small teams, complex and diverse data sources, multilingual review, and constant pressure from clients to cut costs,” said Stuart Hall, Principal at Control Risks. “The launch of Relativity aiR in Asia couldn’t be more timely, offering Control Risks’ customers a real opportunity to simplify and streamline cross-border investigations and disputes with smarter tools and workflows.”
The introduction of Relativity aiR products in Asia is bolstered by the region’s growing demand for secure, scalable legal technology. Built within RelativityOne, these AI tools allow firms to harness the power of automation without compromising security or performance. By operating in a cloud-native environment, legal and compliance teams can eliminate the burden of managing physical infrastructure, standardize workflows across jurisdictions and redirect resources toward strategic analysis.
In response to the growing volume of investigative matters, organizations will be able to utilize aiR for Review to support a wide range of use cases beyond litigation — including internal investigations into fraud, bribery, corruption and whistleblower complaints. Legal and compliance teams can also rely on the tool for Know Your Customer (KYC) reviews, cross-border data transfer assessments and anti-money laundering efforts. Its versatility extends even further, supporting M&A due diligence, risk assessments, trade secret theft inquiries, white-collar investigations and HR-related matters.
For organizations concerned with data protection, Relativity’s cloud-native products, including aiR, offer peace of mind with enterprise-grade security and privacy controls. Backed by the company’s in-house security team, Relativity embeds protection into every stage of its product lifecycle. This security-first approach ensures that as firms adopt cutting-edge AI tools, their information is properly safeguarded.
Looking ahead, Relativity remains focused on empowering users through innovation, delivering rich insights and addressing their most pressing needs. In the coming months, new capabilities will be introduced within aiR for Review and aiR for Privilege. One upcoming enhancement is aiR for Review’s prompt kickstarter capability, which will greatly reduce manual work related to prompt criteria development. Soon, users will be able to upload case background documents — such as review protocols or disclosure requests—and an expert prompt that drives aiR for Review will automatically be produced, allowing users to accelerate analyses. This feature produces a comprehensive matter overview, including key people, organizations, term descriptions and relevance criteria. From there, teams can refine prompts as needed, accelerating the review process and enabling practitioners to take immediate action.
Additionally, aiR for Privilege users will soon be able to find privileged content faster by automating context building that the AI uses to make decisions. Furthermore, a brand-new entity classifier will more accurately identify and classify the entities within each case. This enhancement will help better identify and define the roles of individuals and organizations in a matter, improving precision and efficiency in privilege review.
Unlocking new possibilities for innovation
To achieve their goals with greater precision and reduced overhead, more than 200 customers have embraced aiR for Review, while over 140 have chosen aiR for Privilege to support their workflows. The scalability and transparent natural language reasoning of this industry-leading technology help customers secure faster results while uncovering deeper insights from data.
KordaMentha, an independent and trusted advisory and investment firm working across industries throughout Australia and Asia Pacific, has transformed its legal discovery approach since adopting aiR for Review. The solution has surfaced insights that conventional methods would have overlooked entirely. A recent case study highlights how aiR for Review enabled a defensible and comprehensive review under a tight disclosure deadline, in total saving 25+ days and reducing costs by 85%. With subject matter experts leading the process, KordaMentha was able to uncover several unanticipated findings that drove organizational change.
“Whether as a renowned center for international arbitration, a market with extensive regulatory and investigative demands, or a source of exponential data growth, Asia is a dynamic region uniquely suited to Relativity’s aiR suite,” said Roman Barbera, Partner at KordaMentha. “Building on RelativityOne’s proven ability to navigate diverse languages and data types, aiR delivers exceptional scalability and insight. We’re excited to deploy this trusted and secure AI solution in a region where KordaMentha is already deeply embedded, and where the need for fast, intelligent and defensible data analysis continues to grow.”
In addition to the current aiR product availability, Relativity aiR for Case Strategy, a cutting-edge solution that makes it faster and simpler for litigation attorneys to extract facts, craft case narratives and prepare for depositions and trial, is currently in limited general availability and is expected to become generally available to all regions with access to aiR products later this year.
For more information about the expansion of aiR availability in Asia, please register for the webinar “Transforming Legal Work in Asia: Introducing Relativity aiR for Review and aiR for Privilege,” taking place on July 22. The webinar will offer a first-hand look at aiR for Review and aiR for Privilege through live demonstrations and real stories from early adopters who’ve already transformed their practices. Request a demo from the Relativity team here.
About Relativity
Relativity makes software to help users organize data, discover the truth and act on it. Its SaaS product, RelativityOne, manages large volumes of data and quickly identifies key issues during litigation and internal investigations. Relativity has more than 300,000 users in approximately 40 countries serving thousands of organizations globally primarily in legal, financial services and government sectors, including the U.S. Department of Justice and 198 of the Am Law 200. Please contact Relativity at [email protected] or visit www.relativity.com for more information.
Media Contact: [email protected]
Logo – https://mma.prnewswire.com/media/445801/new_Relativity_logo_Logo_v2.jpg
Tools & Platforms
Why data center, tech firms are concerned about Chile’s AI regulation
34,000+ projects in Latin America.
43,000+ global companies doing business in the region.
102,000+ key contacts related to companies and projects
Analysis, reports, news and interviews about your industry in English, Spanish and Portuguese.
Tools & Platforms
Player faith in technology shaken by storm around AI line-calling at Wimbledon | Wimbledon 2025
When the Wimbledon organisers announced last year that electronic line-calling would replace line judges for the first time at the Championships this year, plenty of criticism could have been anticipated. Some people would take issue with the more sterile landscape on court and the lack of human touch, while the cull of around 300 linesmen and women would also surely be a sore point. It is difficult, however, to imagine they were prepared for the firestorm that has followed its long-awaited implementation at this tournament.
Electronic line-calling, or ELC, which uses automated ball-tracking technology has, after all, long been used in professional tennis tournaments, starting with the Next Gen ATP Finals in 2018. It has been four years since the Australian Open became the first grand slam to utilise the technology and this year, for the first time, the men’s tour, the ATP, is using ELC at all of its events. Although all other men’s clay-court events use ELC, the French Open is now the only grand slam that still employs human line judges.
Instead of this year offering Wimbledon to step into the future, however, the All England Lawn Tennis Club (AELTC) has spent the first eight days of the tournament defending its implementation of the technology.
For the first five days of the tournament the most significant blows were the parting shots from Jack Draper and Emma Raducanu, the men’s and women’s British No 1 players, who each criticised the ELC system following their defeats. Both players believed they had been subjected to incorrect calls. “It’s kind of disappointing, the tournament here, that the calls can be so wrong, but for the most part they’ve been OK. It’s just, like, I’ve had a few in my other matches, too, that have been very wrong,” Raducanu said.
The AELTC maintained that the system was working optimally and that ELC remains considerably more accurate than the line judges it replaced. Wimbledon employs Hawk-Eye, one of numerous ELC providerswhich uses a system that incorporates 10 cameras placed around the court, and which track the bounce of the ball. Hawk-Eye states that its margin of error is 2.2mm. Wimbledon had previously used ELC only as a safety net, allowing players to challenge calls conducted by line judges.
“It’s funny, because when we did have linesmen, we were constantly asked why we didn’t have electronic line-calling because it’s more accurate,” Debbie Jevans, the chair of the AELTC, told the BBC.
Then came a disastrous series of events on Centre Court. As Anastasia Pavlyuchenkova held game point on her serve at 4-4 in the first set against Sonay Kartal on Sunday, a backhand from Kartal clearly flew long but it was not called out. After a lengthy delay, it emerged that some of the ELC cameras had not been functional on Pavlyuchenkova’s side of the court for some time during the game. The umpire Nico Helwerth opted to replay the point. Around 10 minutes later, after losing that service game, Pavyluchenkova faced a set point on Kartal’s serve.
In the end, the AELTC was fortunate with the outcome. Pavlyuchenkova, who told Helwerth the tournament had “stolen” the game from her, recovered to win both the set and the match, limiting the significance of the error. The AELTC announced in a statement on Sunday night that the ELC had been accidentally deactivated on Pavlyuchenkova’s side of the court by one of the operators running the system.
after newsletter promotion
Bright on Monday morning, the Wimbledon chief executive, Sally Bolton, fielded a contentious scheduled meeting with the media, which was almost entirely centred around ELC. Bolton asserted repeatedly that the mistake was purely down to human error, that the protocols had been changed to prevent a similar issue and that ELC has otherwise been working accurately during the tournament. At the very least, the situation with Pavlyuchenkova also underlined the importance of having contingency plans for when technology fails, including the possibility of umpires using video replay.
Since the implementation of ELC, player reaction has largely been positive as it was rolled out on hard courts, with players recognising the greater accuracy provided by the system compared to human errors. However, after numerous dramatic moments during the clay-court season, as some players were frustrated with the differences between the ball marks and the ELC’s judgments, the first week of ELC at Wimbledon has been a difficult one. It is clear that faith in its implementation on the surface has diminished and both privately and publicly, players and coaches have expressed scepticism about its accuracy. As the tournament moves into the latter stages, it remains to be seen if that faith will be restored.
-
Funding & Business7 days ago
Kayak and Expedia race to build AI travel agents that turn social posts into itineraries
-
Jobs & Careers7 days ago
Mumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Mergers & Acquisitions7 days ago
Donald Trump suggests US government review subsidies to Elon Musk’s companies
-
Funding & Business7 days ago
Rethinking Venture Capital’s Talent Pipeline
-
Jobs & Careers6 days ago
Why Agentic AI Isn’t Pure Hype (And What Skeptics Aren’t Seeing Yet)
-
Funding & Business4 days ago
Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30%
-
Jobs & Careers6 days ago
Telangana Launches TGDeX—India’s First State‑Led AI Public Infrastructure
-
Funding & Business1 week ago
From chatbots to collaborators: How AI agents are reshaping enterprise work
-
Jobs & Careers6 days ago
Astrophel Aerospace Raises ₹6.84 Crore to Build Reusable Launch Vehicle
-
Funding & Business7 days ago
Europe’s Most Ambitious Startups Aren’t Becoming Global; They’re Starting That Way