Autowash changed payment systems, but Erin Dreeszen, cofounder and chief of staff at Autowash, told Fortune she knew they had to find a way to “up” everything. They started with maintenance, which is crucial to operations in the car wash world. Each bay involves upwards of 20 different pieces of equipment that work together, and if there’s an issue with any single part, the whole thing goes down. 

“The worst thing that can happen is someone shows up and the car wash is down for maintenance,” Dreeszen told Fortune. And now, with 26 locations and 150 car wash bays across them, that’s a lot of potential for something to go wrong—as well as a lot of repeatable problems begging for a more streamlined approach. 

Working with MaintainX, Autowash adopted an AI-powered maintenance system that ended up laying the groundwork for the company to reimagine its entire operations layer around data and AI. The results are tangible: Repair times dropped 74% across locations, and labor productivity jumped as AI enabled workers to better do their jobs. Institutional knowledge that was previously stuck in employees’ heads is now systematized and searchable.

From repairs to everything else

Each day, Autowash maintenance technicians have to inspect every piece of equipment, which they used to do with an old-fashioned clipboard, pen, and paper checklist. Now it’s a procedure in the operations software system. When a technician flags an issue, the system automatically starts creating a maintenance ticket.

Autowash had originally launched this software system in 2023, but it didn’t immediately catch on. The company’s mobile maintenance technicians who service the different locations were used to doing things a certain way, and they took pride in holding the required knowledge. It wasn’t until MaintainX introduced an AI-powered copilot feature that automatically starts guiding technicians on the right steps to take, using information from the equipment user guides and the company’s own repair procedures, that they really started seeing the value in it. Then came the flywheel: The more they used it, adding notes to tickets and building up a database with even more information and knowledge for the system to use, the value became undeniable and adoption grew even faster. 

“The guys were saying, ‘Man, it’s giving me a summary of what I need to do before I even get started,’” Dreeszen said. This has helped experienced technicians understand new equipment the company incorporates, as well as enabled new technicians to jump in and learn faster than ever before.

This has also allowed the company to embrace more proactive maintenance; now they know when the last oil change was, when they last serviced each piece of equipment, and all sorts of other niche metrics. They’re starting to track even deeper data too, like how many times a motor can turn over or how many times an actuator can open before breaking down. 

The benefits don’t end with maintenance, however. The improved repair data informed inventory control. Then Autowash integrated the system with its warehouse, streamlining how the company schedules deliveries. All this went on to improve how the accounting team creates purchase orders.

“It’s kind of become the backbone of operations in general for what we do,” said Dreeszen. 

Combing learnings

Nick Haase, cofounder and head of go-to-market for MaintainX, told Fortune he’s long seen software in their category be used like a “digital filing cabinet.” Users historically logged what broke and who fixed it, mostly for the purpose of compliance, audit, and financial audits, but it offered little more than that. 

Now that AI enables the system to answer back—sharing summaries upfront, surfacing information about what worked last time, pinpointing the exact part that’s needed, and improving as it gains more data—it’s like having a second all-knowing colleague by your side, he said. 

This is exactly what’s enabling Autowash to combine learnings and have the various locations work together as a uniform team rather than siloed businesses, which has been one of the biggest unlocks for the company. 

“If other technicians at another site across the region have solved that recently, then you don’t need to waste time solving the same problems over and over again. We can just surface it and say, ‘Here’s how Joe fixed it over there last week,’” Haase said.

This leads to his advice for navigating AI transformation: knowing how to translate the benefits of change to the various people in your workforce. A frontline technician likely doesn’t care about the efficiency gains or cost savings that make a difference to leadership or the front office, but they would care about something that makes their job easier. 

Overall, Dreeszen said she’s learned the importance of understanding what your problem truly is. Often, the symptoms of the problem are what’s front and center, but you have to drill to the root of it and think in a more systematic way. 

“I don’t think that software like this is necessarily a solution as much as a new way of thinking,” she said. “You can’t just turn it on and get it to work for you. You have to integrate it into the system to make it function.”