TSLA367.810-0.35%
GM83.760-2.03%
F13.470-0.53%
RIVN16.005-0.165%
CYD37.230-0.49%
HMC31.3000.24%
TM190.970-0.48%
CVNA73.430-1.29%
PAG218.3101.45%
LAD367.720-0.98%
AN209.180-0.12%
GPI287.7206.12%
ABG212.380-0.53%
SAH76.2500.25%
TSLA367.810-0.35%
GM83.760-2.03%
F13.470-0.53%
RIVN16.005-0.165%
CYD37.230-0.49%
HMC31.3000.24%
TM190.970-0.48%
CVNA73.430-1.29%
PAG218.3101.45%
LAD367.720-0.98%
AN209.180-0.12%
GPI287.7206.12%
ABG212.380-0.53%
SAH76.2500.25%
TSLA367.810-0.35%
GM83.760-2.03%
F13.470-0.53%
RIVN16.005-0.165%
CYD37.230-0.49%
HMC31.3000.24%
TM190.970-0.48%
CVNA73.430-1.29%
PAG218.3101.45%
LAD367.720-0.98%
AN209.180-0.12%
GPI287.7206.12%
ABG212.380-0.53%
SAH76.2500.25%


AI is rewriting the rules of dealership website discovery

AI is changing how consumers make buying decisions online, with major implications for dealership websites and digital retail strategy. Robert DeGeorge, Director of Product Consulting for Dealer.com and VinSolutions Retail Solutions & Sales, says dealers must rethink how their websites are structured as AI tools increasingly shape how shoppers search for vehicles and dealerships.

DeGeorge joins us on the latest episode of Inside Automotive to discuss how AI is redefining visibility in automotive retail. 

According to DeGeorge, consumers are moving beyond traditional search engines and increasingly using AI platforms like ChatGPT and Google Gemini for direct answers. Instead of browsing multiple websites, shoppers now ask AI tools specific questions about pricing, payments, trade-ins and vehicle recommendations.

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That shift, DeGeorge contends, is compressing the research phase and pushing consumers to make decisions earlier in the process.

Visibility now depends on AI understanding

DeGeorge stated that dealership websites must now be designed for both users and AI systems that interpret and present information. If AI cannot clearly understand a dealership’s data structure, he said, that store is less likely to appear in generated recommendations.

“if AI can’t understand your website, then it can’t recommend your dealership.”

Additionally, he pointed to platform readiness and generative engine optimization (GEO) as key areas for dealers to now consider. Platform readiness ensures AI can access and interpret website data. GEO focuses on structuring content so it appears accurately in AI-generated answers. Both are required for visibility in an AI-driven search environment.

DeGeorge also outlined five foundational components:

  1. Structured data (schema) to label inventory and pricing in a machine-readable format.
  2. Clean site architecture to organize pages clearly for AI interpretation.
  3. Server-side rendering to ensure content is immediately readable by crawlers.
  4. Real-time indexing to keep inventory and pricing updates current.
  5. A unified data model to ensure consistent information across all pages.

With growing regulatory focus on pricing transparency, DeGeorge said accuracy and speed are now critical. He emphasized that real-time updates, structured data and centralized inventory feeds must work together to ensure AI surfaces current information. He also notes that visibility now depends on whether AI can understand and recommend a dealership.

Dealers that structure data correctly and maintain real-time accuracy will be more visible in AI-generated results. Whereas those that do not adapt risk losing visibility as AI becomes a primary driver of consumer discovery.


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