Inside WeBuyCars’ AI-Powered Digital Reinvention Drive

Speaking at NADA Connect 2026, the Deputy CEO of WeBuyCars offered a rare, unfiltered look into how the company rebuilt itself using data, experimentation and artificial intelligence. “This is not the right way or the wrong way,” he began. “It is simply our story.” Yet the story carried lessons for any organisation grappling with digital transformation. WeBuyCars was founded in 2001 and grew steadily for more than a decade. When private equity entered the picture in 2017, however, investors discovered a thriving business running on remarkably simple tools. “It was essentially a Google Sheet and WhatsApp,” he explained. By 2018, the newly appointed Deputy CEO was tasked with digitising the operation. At the time, the company was already moving around 2 000 vehicles every month, but had virtually no integrated systems supporting that scale. “We walked into a business doing 2 000 vehicles a month with basically no systems,” he recalled. What might have appeared to be a weakness quickly became a strategic advantage. With no legacy infrastructure to untangle, the leadership team had the freedom to design systems from scratch. “We had a blank canvas,” he said. “We could build what the business truly needed.” Early in the transformation, the company faced a pivotal choice: purchase an off-the-shelf enterprise resource planning (ERP) system or engineer its own platform. The decision was to build internally. “We did not want the business to bend around software. The software had to bend around the business,” he said. Control over data was the second major reason for this approach. “We needed ownership of our data, its quality and how it flowed.” Interestingly, one of the first hires was not a developer but a data specialist. Clean, reliable data, he stressed, is the foundation upon which every meaningful AI capability is built. Today, the company’s internal ecosystem is known as the experimentation digital business platform. Virtually every change, whether customer-facing or internal, is tested through structured A/B experiments. “We take emotion out of decision-making,” he explained. “Let the data tell us what works.” This system is supported by a culture that distinguishes between slow, high-risk decisions and rapid, reversible ones. Buying and selling vehicles falls into the latter category, allowing the company to run hundreds of micro-experiments simultaneously without jeopardising operations. Over time, the business expanded its capabilities across several AI disciplines. Its pricing models, trained on more than 370 000 vehicle trades, now rely on Bayesian statistics to generate probability distributions for every vehicle. This enables far more precise buying and selling strategies. “Every car has a price,” he said. “You just need to make sure you paid the right one.” Computer vision has also become a key component of the operation. Image models are now used to detect damage, segment vehicle components and analyse stitched undercarriage photos. “We make money when we buy the vehicle, not when we sell it,” he emphasised. “So we must know exactly what we are buying.” Customer-facing AI is equally advanced. The company’s Orange assistant on its website can explain model differences, estimate running costs and compare vehicles for potential buyers. Meanwhile, customer and social media queries are handled automatically at enormous scale. “This is not the future,” he noted. “It is live in our business today.” Looking ahead, the Deputy CEO predicted a shift toward autonomous software agents that negotiate and transact on behalf of users. In such a scenario, a customer might simply instruct ChatGPT to sell a vehicle, while an agent representing WeBuyCars manages the interaction. “The website as we know it will disappear,” he predicted. Discoverable agents, rather than webpages, could become the new gateway for customer acquisition. He closed with several guiding principles underpinning the company’s strategy. First, technology must always serve the core business. “Unit economics over technology. Do not build tech for the sake of tech.” Second, maintain vertical integration to control every customer touchpoint. Third, ensure that AI augments people rather than replaces them. “You are not being replaced. You are becoming more effective.” And finally, protect the foundations of the business itself. “Look after the goose that lays the golden eggs,” he said. “Focus on what makes the core business valuable.” His message to the audience was clear: the age of AI experimentation is already over. “Everything I spoke about today is already deployed,” he concluded. “This is where business is now.”