Automotive News has published a dealership-focused whitepaper, “AI for Dealerships: What’s working right now,” examining how artificial intelligence is being applied inside store operations. The source summary available for this post does not include adoption rates, ROI figures or performance benchmarks, and the source year is listed as not specified. That matters. This article should be read as commentary on the whitepaper’s positioning, not as a report of hard findings from a dated study.
Still, the positioning is useful for dealers because it reflects where the conversation has moved. AI is no longer being discussed only as a future-facing technology bet. It is being judged against daily dealership pain points: missed customer responses, inconsistent follow-up, service-lane bottlenecks, merchandising delays, documentation drag and manager visibility.
Read the whitepaper as a signal, not a scorecard
For a general manager, the practical takeaway is not “buy AI because the market says so.” It is more specific: identify the store processes where speed, consistency and accountability already affect gross, retention or employee workload, then evaluate whether an AI-supported tool can improve those processes without confusing staff or customers.
That is a narrower bar, and a healthier one.
Dealers have seen enough technology pitches that promise transformation and then create another login, another report and another meeting. The better AI use cases tend to sit close to revenue or capacity. They help a customer get answered faster. They help a service team protect an appointment. They help a used-car manager get inventory presented cleanly before online shoppers move on. They help managers see which follow-up tasks are slipping before the end of the day, not during next week’s review.
Where AI appears closest to store-level ROI
The data does not fully prove this yet, but I’d argue the strongest dealership AI opportunities are the ones that reduce time loss in departments already under pressure. Those areas do not require a dealer to reinvent the store. They require better execution of work the store already knows is important.
- Customer response: GMs should ask whether the tool helps the store respond faster and more consistently when shoppers raise their hands, especially after hours or during peak traffic periods.
- Service scheduling: Service directors should look for fewer missed appointment opportunities, cleaner handoffs and better customer communication around availability, reminders and status.
- Used-vehicle merchandising: Used-car managers should focus on speed to market, listing quality, photo and description consistency, and whether inventory gets retail-ready online before aging becomes the issue.
- Equity and retention follow-up: Sales and service leaders should evaluate whether the system helps the store identify customers who need timely outreach, rather than relying on a manager to remember every opportunity.
- Administrative support: Controllers and department managers should consider whether routine documentation and communication tasks can be tightened without creating compliance risk or extra review work.
None of these examples require a dealership to accept vague claims about replacing people. In most stores, the better question is whether the tool helps good employees handle more opportunities with less delay and fewer dropped balls. That is a much more believable standard than a sweeping promise about automation.
Where dealers should be more skeptical
Some AI claims are still too broad for a dealer to underwrite without proof. A vendor may be able to describe what its product is intended to do, but that is not the same as showing what happened in stores with similar franchises, traffic levels, staffing models and customer expectations.
- Be cautious with claims that promise major gross improvement without explaining which department behavior changes.
- Question any tool that creates more manager review work than it removes from the front line.
- Push back on demonstrations that look polished but do not reflect messy customer messages, incomplete vehicle information or real service-lane constraints.
- Avoid evaluating AI only at the executive level; the desk manager, BDC manager, advisor or inventory manager who will live with the tool should be part of the review.
- Treat vague references to “efficiency” as incomplete until the vendor can connect them to appointments, response time, close rate, repair-order handling, merchandising speed or labor savings.
A service director, for example, does not need a philosophical discussion about AI. She needs to know whether advisors will spend less time chasing basic updates, whether customers will be less likely to no-show, and whether the lane can protect capacity on a chaotic Monday morning. A used-car manager needs to know whether the tool helps get a fresh trade represented online while shopper interest is still hot. A GM needs to know whether the reporting is clear enough to coach from, not just attractive enough to show in a vendor demo.
Questions operators should ask vendors
If the Automotive News whitepaper is treated as a prompt for management discussion, it gives dealers a useful opening to tighten their evaluation process. Before approving another AI-related expense, managers should ask sharper questions than “Does it use AI?”
- Which department owns the result, and who is accountable for using the tool every day?
- What store-level metric should improve first: response time, appointment set rate, show rate, merchandising speed, repair-order handling, retention outreach or employee capacity?
- Can the vendor provide recent examples from dealerships that resemble our operation in size, brand mix and staffing?
- What does success look like after 30, 60 and 90 days?
- How much employee training is required before the tool becomes useful?
- What customer experience risk exists if the tool gives an incomplete, awkward or poorly timed response?
- How will managers know whether the tool is being used properly, not merely turned on?
Those questions separate useful technology from showroom theater. They also force the conversation back to the department manager who has to live with the result. AI that looks impressive in a conference room but fails at the service counter, sales desk or inventory office will not earn trust for long.
A practical way to evaluate by department
For GMs, the cleanest approach may be to build a short department-by-department review. In sales, look at lead response consistency, appointment quality and follow-up discipline. In service, look at scheduling friction, missed calls, status communication and declined-service recovery. In used vehicles, look at time to online retail readiness, listing completeness and aging exposure. In accounting or administration, look at repetitive tasks that slow employees down but still require accuracy and oversight.
Then pick one pain point, not six.
That is where many dealership technology rollouts get sideways. A store buys a broad promise, assigns no clear owner and then wonders why the result is hard to measure. A more disciplined pilot starts with a department manager, a defined process, a small set of success measures and a regular review cadence. If the tool cannot show practical value in that setting, expanding it across the store is probably premature.
The dealer takeaway
The Automotive News whitepaper appears to capture an important shift: dealership AI is being pulled out of the buzzword category and into the operations meeting. That is progress, but it also raises the standard. Dealers should not reward AI language by itself. They should reward tools that help managers protect revenue opportunities, reduce avoidable delays and make daily execution easier for employees.
The strongest store-level test is simple: if the technology disappeared tomorrow, which manager would miss it first, and why?