Artificial intelligence is transforming the automotive industry at breakneck speed. From chatbots that handle customer inquiries to predictive analytics that identify sales opportunities, AI promises to revolutionize how dealerships operate. But there's a critical prerequisite that most dealers are overlooking: clean, organized data.
"Cleaning your data should be the number one job of your dealership or group today," - Todd Smith, CEO of QoreAI
"If you have any ambition to start to leverage AI,” explains Todd, “And the data is not clean and organized, the AI will start saying all kinds of things that are incorrect, wrong, and create terrible experiences, and it'll accelerate bad performance."
The AI Promise vs.
The Data Reality
The potential of AI in automotive is enormous. Dealerships are already experimenting with conversational AI on websites, AI-powered scheduling in service departments, and AI-driven lead communication in BDCs. But AI's effectiveness depends entirely on the quality of data it's trained on and processes.
Here's the harsh reality: most dealership data is a mess.
Vehicle ownership unveil some shocking realities:
- 40%+ of customers no longer own their listed vehicles
- Email addresses decay at 2.5% monthly.
When this dirty data feeds AI systems, the results can be catastrophic for customer relationships and dealership reputation.
The Hidden Danger:
AI Amplifies Bad Data
Poor data quality has always been expensive, but AI amplifies the problem exponentially. Where a human might catch an obvious error, AI systems can perpetuate and scale mistakes across thousands of customer interactions.
April Simmons shared a real-life example of this she experienced: "I get a text message that says, 'you need to bring your XYZ car in for service.' And I'm like, it was in yesterday, right? Like, literally, it was in yesterday. What are you talking about?"
This confusion occurs because dealership systems often create multiple customer records for a single vehicle. The original buyer gets one record, but when their spouse brings the car in for service, a new record is created under the spouse's name. AI systems then treat these as separate customers with separate vehicles, leading to duplicate communications and confused customers.
The Household Problem:
When AI Misses the Big Picture
One of the most complex challenges in automotive data is what experts call "householding"—understanding that multiple people in a family may interact with a dealership around the same vehicle.
"Wife or husband brings in the [other's] car, and now we have two people getting the same marketing," Smith explained. "With technology today, you can easily understand that's a household. I don't need to send it to all three. We send it to one."
Modern AI can solve this problem elegantly, but only if the underlying data is clean and properly structured. AI can analyze patterns to determine that if a car was purchased by a husband and wife together, but the wife consistently brings it in for service, she should be the primary contact for that vehicle.
"Technology can help us really not only understand our customers, but more importantly, we learn about our customers by understanding the data that lives below," - Todd Smith
When Clean Data Meets
Smart Technology
When data is clean and AI is properly implemented, the results can be remarkable. Smith shared a compelling example of AI catching a costly data entry error:
"We trained our AI to spot issues in data, and AI ultimately went and changed a vehicle price. Someone had priced a Honda Pilot at $5.38 million instead of $53,000. Not uncommon, right? They just missed the decimal point. So the system caught it."
The most impressive part? "We did not train it to look at the inventory field price. It figured it out itself, knowing that the average Pilot is in this range. That's probably just added two zeros."
This demonstrates AI's potential when working with clean, well-structured data—it can identify patterns and anomalies that humans miss, preventing costly errors before they impact customers.
Protecting Data in the AI Era
As dealerships rush to implement AI solutions, a critical security issue is emerging. Many AI tools require data to be uploaded to external platforms, creating massive compliance risks.
"I would never ever suggest uploading any data you have into ChatGPT, Claude, Gemini, or any other system.” - Todd Smith
“None of them are secure. Even if you toggle it that says do not share it, your data is not secure," Smith warned. "A research study just came out of Cornell that they blew holes in it. Even if the data was vectored, it is still available."
The compliance implications can be costly. Simmons added, "I highly, highly recommend you get a new addendum to your employee handbook that explains that they're not allowed to do that and have every employee sign off that they understand that they cannot take any customer's PII and put it into any type of system."
This isn't just about employee training—vendors are also using open AI systems in ways that expose customer data.
"Vendors are using AI, and they're writing emails out of your CRM into an open AI structure. PII is being exposed every time." - Todd Smith
Preparing Data for AI
Clean data is just the starting point for AI readiness. Dealerships also need to structure their data so AI can effectively use it.
"You also have to clean the data, and then you have to structure the data so that AI can leverage it. So, vectoring the data, putting it on a graph, some things have to happen mechanically for your data to become available that AI can use," Smith explained.
This process involves:
- Data hygiene: Cleaning, validating, and standardizing customer information
- Data enhancement: Adding behavioral and demographic insights to transaction records
- Data organization: Structuring information so AI can identify patterns and relationships
- Continuous monitoring: Implementing real-time data quality controls
The Competitive Advantage:
Winning As An AI-Ready Dealership
Dealerships that invest in proper data foundation now will have a massive competitive advantage as AI capabilities expand.
They'll be able to:
- Implement AI solutions faster because their data is already clean and structured
- Achieve better AI performance because the systems have quality data to work with
- Avoid compliance risks because they have proper data governance in place
- Scale AI applications across multiple departments and use cases
"This has to become a daily practice organizationally. In real time."
- Todd Smith
Building AI-Ready Data Infrastructure
The path to AI readiness starts with understanding your current data landscape.
- Document how data flows throughout your organization
Smith recommends dealers begin by "documenting how data is flowing organizationally. Here are all my vendors, who gets what data, how much, how are they getting the data right now, because every connection point is a vulnerability for attack."
- Aggregate and clean your data
Next comes aggregation and cleaning. "Aggregating all the data in one spot, that is then cleaned, organized, and ultimately, you'll want to enhance the data. Your data alone is not enough."
- Incorporate intelligence to your tech stack
Finally, dealerships need to add intelligence to their data. "You're gonna want to round that data out with behavioral psychographic data. You're gonna want to put more intelligence behind it because it's through that intelligence with the transactional data, then you can start to build things, and you can start to leverage AI."
The Future is Now
AI isn't coming to automotive—it's already here. The dealerships that will thrive are those that recognize data quality as the foundation for AI success. They'll implement proper data governance, invest in cleaning and structuring their information, and partner with vendors who understand the security and compliance requirements of the AI era.
"The world is yours, and where we're going with AI and the power of what you can do with it is right now virtually unlimited" - Todd Smith
… but only if you have the clean, organized data foundation to support it.
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