Aarjav Trivedi is the founder and CEO of Ridecell, a leading intelligent automation platform for fleets.
We are well into the 21st century, but deciding what to do with a used fleet vehicle—Keep it and try to get another turn out of it? Sell it?—is still a highly manual, ad hoc process for fleet management companies (FMCs). This opens a window for AI to step in and significantly transform how vehicle remarketing gets done, helping fleet and rental companies make better business decisions and increase the bottom line.
One Size Doesn’t Fit All
First things first: How exactly does a fleet company decide that a vehicle is ready to be remarketed? Historically, this has been a “one-size-fits-all” determination across the entire fleet—for example, “sell it after 3 years of service” or “once the vehicle hits 80,000 miles, auction it off.”
The thing is, not every vehicle has been driven the same way during its lifetime. Some have been put to hardcore work—maybe even been involved in a fender bender or two—while others have experienced nothing but smooth sailing and have plenty of useful life ahead of them.
Unfortunately, getting an accurate condition report for a vehicle is a lot more involved than simply checking the odometer to see how many miles the vehicle has racked up. Gaining a more nuanced understanding of the vehicle’s condition takes considerable time and effort.
At least, it does without AI.
Making Sense Of Heaps Of Data
AI is fantastic at parsing large heaps of data to spot patterns. It’s now possible for fleet owners to drive a vehicle into an inspection facility, have cameras and other imaging technologies take dozens or even hundreds of photos of the vehicle and then let AI make sense of it. Are there dings and scratches on the undercarriage? Does the frame seem ever-so-slightly bent out of shape? AI will pick up on those cues.
Likewise, AI can pore through all the telemetry data to see which vehicle is the proverbial “car that was only driven once a week on Sundays,” and which was pushed to the max. AI can even analyze a 30-second audio recording of an engine to determine overall engine health.
Keep Or Sell?
The end result of AI’s analytical efforts is highly accurate, vehicle-specific condition reports. Using those condition reports as a foundation, FMCs can further leverage AI to generate very precise repair estimates, which can bring clarity to the “keep vs. sell” question.
Maybe Vehicle A needs an entire overhaul while Vehicle B only needs a few cosmetic tweaks, in which case, the most profitable decision would be to auction off Vehicle A to a wholesaler while holding on to Vehicle B to get another lease out of it. Or, maybe Vehicle B is in good enough shape that it can quickly be marketed for sale directly to drivers—a high-margin channel when compared to wholesale auctions.
The ability to make these case-by-case distinctions between different vehicles is similar to the changes that have come to the auto insurance space in recent years. Rather than basing insurance rates on generic “buckets” like driver age or past driving experience, companies can now insure vehicles based on data-driven factors such as miles actually driven and driving style.
In the same way, AI allows FMCs to shift away from “one-size-fits-all” categorization toward a more granular approach that allows them to better maximize the value of their assets when it comes time to remarket them.
Smarter Decisions Around “When” And “Where”
Maximizing value isn’t just about which assets to keep or sell, but also when and where to sell them. For example, say an FMC has a vehicle that doesn’t need any major maintenance for the next 20,000 miles. That’s essentially “free usage”: There’s no significant cost required for the fleet company to derive revenue from it.
Hold on, though. Bringing a 20,000-mile car up to 40,000 miles is fine; the price the company could get when they sell it is similar in either scenario. But what if it has 80,000 miles? Suddenly, adding 20,000 miles makes it a 100k-miler—and nobody wants to buy a car with six digits of mileage (or at the very least, they won’t pay a premium). The smarter decision is to sell it before it hits that mark, and AI can help identify that selling opportunity.
Meanwhile, a big enough fleet owner may have multiple divisions, some of which are in growth mode and acquiring vehicles while others are experiencing flat growth and selling vehicles. AI can analyze regional sales data so that the FMC can take a holistic view of their organization and connect those two groups with each other, rather than having one division selling vehicles to a wholesaler, while another division is buying from a wholesaler. The only winner there is the wholesaler, who books a healthy profit playing middleman.
The Magic Starts With Data
Data is the lifeblood of AI, so in order to take advantage of any of these AI-assisted improvements to their remarketing processes, FMCs need to be at the point where they’re not just generating digital data around their vehicles, but bringing that data together. This can be done manually, through exports from individualized systems into a centralized spreadsheet or database, or via software that does it automatically. (Full disclosure: My company offers an AI-powered platform for this purpose, as do many others.)
Regardless of approach, what’s important is for FMCs to effectively leverage the information they already have today if they want to make better decisions tomorrow. That’s the next step forward for FMCs that want to use AI to make smarter, more profitable decisions around remarketing.
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