As dealers face rising complexity in inventory management, pricing, and profitability, especially with the 25% tariff on imported vehicles, machine-driven technologies are stepping in as vital tools — not just to handle data, but to make better, faster business decisions.

From fluctuating vehicle values and evolving consumer trends to aging inventory and regional market differences, Large language models (LLMs), agentic AI, and vertical AI solutions are stepping in to support dealership staff in interpreting data, automating analysis, and taking action. Unlike traditional business intelligence dashboards or manual pricing spreadsheets, these machine-powered platforms provide real-time insights built specifically for dealership needs and adoption is growing fast.

The Lotlinx Machine-Driven Technologies Survey shows that nearly 30% of dealers are already using machine learning, and another 30% are utilizing predictive modeling—up from just 21% in late 2024. When it comes to vehicle pricing decisions, approximately 40% of dealers are leveraging machine-driven technologies for both new and used vehicles and a strong majority (over 60%) of those dealers say these tools have improved their ability to optimize pricing strategies.

This means machines aren’t just augmenting dealer decision-making, they’re outperforming traditional approaches.

Intelligent chatbots as sales support tools

With dealers generating a staggering amount of operational data — VIN specs, sales histories, regional demand trends, and more — they historically have leaned on business intelligence (BI) platforms to process this data, but those tools still require human time and analysis. The result? Slower decisions, missed insights, and less profit.

Today’s generation of vertical AI and LLM-powered tools change that. These systems are trained specifically on dealership data and designed to function as internal assistants — providing not just analysis, but automated, actionable recommendations in real time.

For example, a dealer might need to understand which vehicles are currently overpriced relative to the local market. Instead of running reports and manually comparing listings, they can ask a machine-powered assistant that question directly — and receive a smart, context-specific answer. The system might then recommend lowering the price on certain trims, offer alternative stocking suggestions, or flag similar inventory in the region that’s moving faster. Additional example questions dealers could ask include “which new SUVs on my lot are priced above market by more than 5%?” or “what was the average turn rate for mid-size trucks over the last 90 days?”

These internal-use technologies act as virtual sales and operations managers, identifying trends, flagging issues, and even triggering automated workflows across departments. Unlike generic AI tools, these are built from the ground up to understand the intricacies of dealership operations—making them invaluable for streamlining decision-making across pricing, inventory, and forecasting.

Powering inventory decisions that drive real profit

Among the top use cases for machine-powered platforms is inventory management. According to recent data, 50% of dealers are now using machine learning for inventory decisioning, and a staggering 80% use it daily or weekly to make decisions about carryover inventory. This frequency of use underscores just how embedded these tools are becoming in dealership operations1.

The reason is simple: inventory has always been one of the most sensitive levers for dealership profitability. Holding the wrong vehicles too long, missing fast-moving trends, or failing to price inventory in line with local demand can quickly erode margins. Machine-driven platforms ingest real-time data and historical trends to solve these problems before they happen.

Rather than wait for vehicles to age out of profitability, these systems and digital assistants automatically flag underperforming units and suggest actions — like markdowns, incentives, or reallocation. They can even analyze micro-trends, like an uptick in electric vehicle interest in a specific ZIP code, and advise dealers to shift inventory accordingly. And they do this in minutes, not days.

The results speak for themselves: among dealers using machine-powered systems, many report significant improvements in turn rate, gross per vehicle, and sales velocity, all while reducing the time spent on manual analysis and guesswork.

A competitive necessity, not a nice-to-have

LLMs, agentic AI, and vertical AI are no longer experimental technologies or future-facing trends — they’re tools that leading dealers are using right now to make more profitable decisions, faster. As complexity increases in retail automotive, the dealers who lean into machine-driven insights will not only weather the storm but outperform their peers in efficiency, agility, and bottom-line results.

Yet, the Lotlinx survey data also suggests room for growth: 1 in 10 dealers still report not using any machine-driven tools at all. For those lagging behind, the message is clear—AI isn’t replacing people, it’s empowering them. And in today’s retail environment, that’s not just an advantage — it’s a necessity.

As machine-driven solutions continue to evolve, their role will only deepen across every facet of dealership operations. Those who invest today aren’t just upgrading their tech stack—they’re future-proofing their businesses for an increasingly data-driven industry.

In the end, the message is clear: AI is not the future of retail automotive—it’s the present. The dealers that understand this, embrace it, and act on it will be the ones leading the market in performance, efficiency, and profitability in the years to come.

Lance Schafer is the general manager of product and technology for Lotlinx, which offers an inventory platform that can enable dealers to automatically adapt to market dynamics, mitigating inventory risk through VIN-specific strategies. For more information, visit www.lotlinx.com.