“If you look at history, innovation doesn’t come just from giving people incentives; it comes from creating environments where their ideas can connect.”

—Steven Johnson, Author

AI adoption keeps running into the same issues, and none of them are surprising to anyone in retail auto.

Data quality problems. Disconnected systems. Delays between insight and action. Those barriers continue to show up as the biggest factors slowing real-world progress.

In dealerships, they’re already part of the daily workflow. Sales, service, and marketing data don’t always line up, which means decisions take longer and automation has less to work with.

As AI becomes more embedded in day-to-day operations, those gaps matter more. Tasks are triggering automatically. Systems are acting without waiting for a human nudge. That shift puts pressure on how clean the data is and how tightly systems are connected.

Security and access play a bigger role here as well. Risk doesn’t suddenly appear at the end of the process. It builds as data is collected, shared, trained on, and permissioned. When access rules are unclear or ownership is fuzzy, trust erodes quickly.

1. Start with the barriers you already feel.
The top blockers in the report—data quality, fragmented systems, and real-time gaps—are the same issues slowing dealerships down today. If your teams spend time reconciling numbers before acting, AI will only move as fast as that cleanup.

2. Fix data flow before adding automation.
AI depends on consistent inputs. If RO status, customer history, or lead activity live in different systems without alignment, automation creates confusion instead of efficiency.

3. Pay attention to where security risk actually enters.
The AI pipeline graphic makes it clear that risk builds early. Data collection, training sources, and system permissions matter more than the final output. Dealers should know exactly what data is being pulled, where it’s stored, and who can change the rules.

4. Lock down access as automation expands.
As AI starts triggering actions automatically, loose permissions become operational risk. Every workflow needs clear guardrails and a named owner.

5. Use barriers as prioritization tools.
If a process touches sensitive data and relies on messy inputs, it’s not the right place to start. Begin where data is clean, risk is low, and speed matters.

As AI moves deeper into day-to-day workflows, operational weak points are becoming more visible. This prompt is designed to help leaders identify where automation would accelerate value and where it would amplify risk.

How to use the output:

Run this before buying tools.
Run it before expanding automation.
Run it again in six months.

Managing 76 dealerships and 11% of Toyota’s U.S. sales forces clarity around how AI fits into real operations.

In this Auto Collabs conversation, Marcus Williams, GM of Toyota’s Los Angeles Region, shares how AI is already shaping service workflows and customer communication across a massive dealer network.

The discussion centers on practical applications in fixed ops, including appointment handling, follow-up, and faster access to accurate information for today’s highly informed customers. Marcus explains how dealers are approaching AI adoption with intention, focusing on consistency, responsiveness, and trust throughout the ownership journey.

For dealers looking for a grounded, large-scale view of how AI is showing up in daily operations, this replay offers real perspective.

Thanks for reading along, Friend! We know living in an AI world can be overwhelming sometimes. Hang in there.