Artificial intelligence (AI) is no longer a future consideration for car dealers. It’s a present-day competitive necessity. That is the central message from Todd Smith, CEO of QoreAI and author of The Intelligent Dealership: How AI and Data Transform Automotive Retail. Smith joins us on today’s episode of Inside Automotive to outline how dealerships can begin putting AI to work across every department.
According to Smith, treating AI like traditional software is the primary fundamental mistake dealers make when approaching AI adoption. Unlike conventional software, where users input data and receive an output, AI requires training, context, and time to understand how a dealership operates. Smith compares the onboarding process to hiring a new employee, one who starts with no instructional knowledge but, once trained, consistently outperforms any single member of the team.
“This is very compounding technology. It's not linear. You're not making a once a year update to your SaaS tool."
Smith argues that most dealerships are deploying AI in the wrong direction. Rather than immediately pointing it at customers through chatbots and outbound communications, he urges dealers to first turn AI inward. The foundation of that approach is what he calls the “dealership brain,” a centralized repository of institutional knowledge, workflows, employee processes and transactional data. Most dealerships store that knowledge loosely in outdated binders, in the habits of long-tenured employees or not at all.
Once captured and structured, AI can begin optimizing operations across sales, service, finance and marketing in ways no human team can match alone.
Data is the most valuable asset
Smith draws a sharp distinction between dealers who own their intelligence and those who rent it from vendors. Dealers who allow transactional data to flow freely into third-party systems are effectively handing over their most valuable asset.
He points to Tesla and Amazon as companies that built dominant market positions not on products, but on data. Dealership transactional data, the point at which a customer writes a check, ranks among the most coveted data on the market. Vendors who access it freely will eventually monetize it and sell AI-powered insights back to dealers as a product.
For instance, Smith recounted watching an elderly couple at a dealership photograph their deal sheet and run it through ChatGPT to evaluate whether they were getting a fair price. When he flagged it to the desk manager, the response was immediate: “Another one?”
Consumers are already using AI to compare pricing, assess inventory and evaluate dealership reputations through aggregated reviews that surface patterns no individual scan would catch. Dealers who attempt to game pricing or manipulate their digital presence will find AI dismissing them from results entirely as trust and transparency become the currency of the LLM era.
Practical applications
Smith sees immediate AI applications across all areas of dealership operations:
- In marketing, the shift moves away from broad conquest campaigns toward true one-to-one customer engagement.
- In service, AI can route vehicles to technicians based on demonstrated efficiency.
- In used-vehicle operations, rather than pricing a car based on a handful of data points, AI can evaluate tens of thousands of variables simultaneously and update recommendations in seconds.
- In HR, dealers can upload a resume and instantly generate role-specific interview questions tailored to their culture, and managers can practice difficult employee conversations with real-time compliance and tone feedback.
The ROI question
When dealers ask about return on investment (ROI), Smith reframes the question entirely. He asserts that the value of AI is not measured in immediate revenue but in compounding operational efficiency. He draws a parallel to Michael Dell building a leaner manufacturing model to outpace IBM and Compaq despite their size and resources. Dealers who invest in building AI-powered operations today will be positioned to compete well beyond their local markets as that efficiency gap widens.
Nevertheless, Smith is direct about urgency. Unlike the internet era, where dealers who waited a few years could still catch up, AI’s exponential pace eliminates that cushion. He references METR.org, which tracks autonomous AI capability, noting that the window of unassisted AI work has grown from minutes to 12 continuous hours and is accelerating. The dealers who act now will own their intelligence and continue building on it. Those who rely entirely on vendor-supplied tools will find that intelligence disappears the moment they cancel a contract.
“AI is built to build efficiency.”
