Featured in Lift Line Winter 2026
It's easy to think of the business of cranes and heavy lifting as a world of man, machine, and brute strength. That outlook ignores the dramatic impact that technology has had on the industry over the past several decades. Lifts are planned using computer-assisted design. Most cranes have onboard computer systems. These days, the cab of almost any crane has been transformed—the traditional steering wheel and lever have been replaced by a vast array of buttons and screens.
It makes sense that a business based on using equipment weighed in tons to move objects also weighed in tons would use every advantage to plan those lifts down to the last detail and execute them with maximum efficiency. Just as crane operators and support staff have grown accustomed to the critical role computers now play in the industry, in another decade we’ll probably view AI much the same way. For now, though, AI is still an emerging technology in the crane space.
Johnny Ashworth, director of business optimization for the ALL Family of Companies, is, among other things, charged with considering the ways artificial intelligence might benefit the organization and its customers. “My larger role within ALL is to develop creative methods of bridging any gaps between our legacy systems and new technology,” said Ashworth. “Obviously, in recent years, consideration of AI has been a part of that.”
Assisting humans, not replacing them
Ashworth cautions that ALL is not considering any AI that would act autonomously or replace the decisions of people. “The future of AI at ALL is assistive. It will be about giving our people—our experts— cleaner data and better options to use to make decisions,” said Ashworth.
AI is new to the national conversation, so it’s not widely understood that there are actually different kinds of the technology. The three main types of AI are: classification, generative, and agentic.
Classification AI is trained to recognize patterns. It can categorize new data into classes or labels based on those patterns. Generative AI is used to create data based on its prediction of what comes next, based on trained patterns. Agentive AI interacts with other entities, whether AI, non-AI or human, using generated output. (It is agentive AI that is most feared by skeptics of the technology, an autonomous artificial intelligence system that can make decisions, plan, and take actions with minimal or no human intervention. Think SkyNet from the “Terminator” movies.)
ALL’s exploration in AI falls strictly within the much more manageable classification type. “We envision training our AI systems to double-check our data, to make sure it is accurate and clean,” said Ashworth.
Introducing Meter Outlier
Case in point: ALL’s new meter outlier project.
Every crane is equipped with multiple meters that display information like miles driven, engine hours, and oil life. Anytime a crane is serviced by ALL’s technicians, this information is recorded into the companywide central system. It is one of the ways service departments at the various branches make sure each piece of equipment is getting attention at proper service intervals, no matter where equipment might be physically located across the national footprint.
The meter outlier, powered by AI, acts as a check on this manually recorded information. “It’s an app we created using our business intelligence software,” said Ashworth. “It reads the inputted meter information for every machine in our fleet and looks for anomalies. For instance, if the engine hours on May 7 are lower than what was recorded on May 1, it flags that for additional review. Because it knows engine hours don’t run in reverse.”
That’s a simple example, but it illustrates the kind of anomalies the meter outlier is looking for. It also demonstrates how ALL intends to use AI to help its human intelligence, not replace it.
“Our business intelligence software has dashboards that help the service teams track these intervals in real time, using the hours and miles recorded in the field.”
Ashworth anticipates that meter outlier functionality is just the beginning of using AI to enhance access to performance metrics.
“Eventually, it can scour maintenance records, provide summaries, ask natural questions,” said Ashworth. “We have so much data at our fingertips and AI will help us put it to work for us. Someday, AI will help us predict demand, optimize dispatching, and ensure that no crane sits idle when it could be earning for our customers.”
It’s about using AI to enhance efficiencies that create value for customers. “Nothing is more important than our pledge to maintain a rent-ready fleet. In the crane industry, maintenance isn’t just about uptime—it’s about trust. With Meter Outlier, we’re proving that AI isn’t about replacing expertise. It’s about accelerating it. Cranes lift tons, but data, used the right way, can move mountains.”