When it comes to mapping out a maintenance program in 2026, exploring ways to include AI is clearly a priority for many leaders. In fact, 58% of maintenance teams have either fully implemented, partially implemented, or are piloting AI tools, according to our State of Industrial Maintenance report. Meanwhile, 17% percent say they have no current plans to implement, down from 29% last year.
So how are maintenance teams using AI-powered tools today? And, more importantly, what benefits have they seen so far? Here are four reasons why teams are getting behind AI adoption.
Key takeaways
- AI is helping address the industry’s knowledge transfer challenges as more technicians retire.
- Leaders report that AI tools are helping them prevent equipment failures.
- The technology is boosting team productivity by improving scheduling and resource allocation.
- With AI-powered analysis tools, leaders are able to make data-driven decisions faster.
Key Benefits of AI in Maintenance
Knowledge Capture and Sharing
Over the next five years, the Hudson Institute reported that as much as 40% of the maintenance workforce could retire.
Maintenance leaders are finding that AI tools can help them grapple with the reality of the growing skills gap. “Knowledge capture and sharing” is cited as an advantage of AI, referenced by 27% of leaders who’ve implemented the technology.
AI tools can capture insights directly from technicians’ workflows, helping teams quickly create repositories for maintenance expertise that would otherwise be lost.
And when training inexperienced technicians, AI’s instant guidance can be a game-changer. To get the job done correctly and efficiently, technicians can lean on AI assistants for support. These tools can pull up answers from years of maintenance data, manuals, and your team’s previous work, making it easy to find specifications, troubleshoot issues, or follow safety steps.
“We’ve used AI to help with our lockout tagout procedures. For example, to change a bandsaw blade, we needed to lock out the motor. The first thing MaintainX CoPilot said was to shut off the breaker, which verified our thinking. To go through the entire manual to find that information would have been pretty hard, but CoPilot showed us exactly where to find it.” -Jeremiah Dotson, facility maintenance manager at Amfab Steel
Beating Unplanned Downtime
As the cost of downtime continues to rise, amping up failure prevention measures has become more critical than ever for maintenance teams. The good news is that emerging AI tools are already producing results on that front—28% of leaders who have brought AI into their operations report that it’s reducing unexpected equipment failures.
For example, a chemical manufacturer might use AI to stop equipment failures before they happen by setting up triggers when an industrial mixer exceeds a set temperature or vibration threshold.
And where traditional approaches have failed, maintenance leaders are putting their trust in AI technology as a new solution. Of companies that experienced more downtime than expected last year, 40% have implemented AI across multiple maintenance processes. Meanwhile, since their motivation to experiment with innovative solutions is less urgent, only 18% of leaders who experienced less downtime have fully implemented AI.
Productivity Gains
AI is also helping teams become more efficient, which is key to controlling costs. Adopting AI has improved scheduling and resource allocation, according to 30% of maintenance leaders. These tools can take the guesswork out of maintenance planning by using historical work order data—for example, a monthly preventive maintenance task for a conveyor system—to accurately forecast how long that task will take to complete.
When it comes to work order completion, 36% of maintenance leaders report better performance thanks to AI tools. AI assistants can also speed up time-consuming tasks like digital procedure creation, giving everyone access to the instructions they need to keep equipment reliable and safe.
“We’re continuing improvements on the procedures side and getting better at using the AI features, which is great at giving us a jumpstart. You can get writer's block when you're thinking about how to make a procedure, but the AI system allows you to quickly jump into it.” Ryan Bell, assistant general manager of public works, Villages Golf and Country Club
Smarter Maintenance Through Data
For 34% of leaders, implementing AI has translated to better data-driven decisions.
Many leaders use AI for cost tracking and optimization. For example, AI tools can evaluate historical parts consumption and shipping data to predict the best times to order critical spare parts. This helps teams avoid expensive downtime and emergency parts shipping costs.
When you provide maintenance data like past work orders, AI tools can analyze and reveal root causes of failures. And beyond just identifying equipment issues, the technology can recommend solutions that help you take faster, more effective action.
How to Make AI Work for Your Team
If these benefits have you curious about the next steps for AI implementation on your team, we have some recommendations.
- Clean up and digitize your data: First, keep in mind that, no matter how powerful the model, AI technology is only as useful as the information you feed it. Digitizing your day-to-day operations so your AI tools have access to a steady diet of clean data is the first step toward breakthroughs like the examples above.
- Pick a specific area for improvement: In a recent chat with our cofounder, Nick Haase, Daniel Marchant, service manager at Xylem, pointed to the power of keeping it simple when it comes to bringing AI into your workflows. “As we evolve, adopting AI is a necessity to an extent. But we need to be extremely tactical when we adopt it. We need to pick a process, a piece of equipment, or a singular problem and come up with a singular solution,” Daniel says. “Trying to implement a grand, everything-automated, Star Trek interface and maintenance plan is not going to happen unless you have infinite resources.”
In other words, rushing blindly into AI can create its own trap: investing in solutions that teams won’t trust or technology your facility isn’t prepared for. Your team’s success with AI hinges not on rapid adoption, but on being thoughtful about implementation and finding specific, practical ways to use AI technology in your organization.
Want to learn more about the challenges facing maintenance teams, and the strategies organizations are using to overcome them in 2026? Download the full State of Industrial Maintenance report.
Maintenance teams using AI FAQs
How are maintenance teams using AI to improve operations?
Maintenance teams using AI tools apply machine learning and data analysis to predict failures and surface trends in asset performance. These capabilities help teams shift from reactive repairs to planned, condition-based work.
How often should a maintenance team review its AI tool outputs?
Most teams benefit from daily reviews of AI-generated alerts and weekly reviews of trend dashboards. Regular check-ins keep alert predictions accurate and help technicians flag data quality issues before they affect results.
How does a CMMS support AI adoption for maintenance teams?
A CMMS like MaintainX provides the structured work order and asset data that AI models need. Clean, consistent records improve prediction accuracy and give teams one platform for maintenance management and AI-driven insights.
What results are maintenance teams using AI tools seeing first?
Early adopters most often report faster knowledge transfer and fewer unplanned breakdowns as the first measurable wins. These gains tend to appear within months when teams build on clean asset data and a focused adoption plan.
Senior Content Writer, MaintainX
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