In this article, you'll find a collection of useful stats and insights about preventive maintenance, predictive analytics, artificial intelligence, and other trends that impact maintenance teams. You'll also find out what this data says about the year ahead and how you and your team can optimize your maintenance operation.
Key takeaways
- While downtime incidents are decreasing, downtime costs are rising: Maintenance teams are seeing rising cost pressures despite making progress to eliminate reactive maintenance and unplanned downtime, mostly due to aging equipment and parts costs.
- Preventive maintenance still dominates maintenance strategies…in theory: A majority of maintenance professionals say preventive maintenance is their primary strategy, yet most facilities spend most of their time on unplanned maintenance.
- Predictive maintenance adoption is rising steadily, but still faces obstacles: While predictive maintenance (PdM) adoption is on the rise, its growth is still stalling at many facilities due to costs and internal skills gaps.
- The use of sensors and IIoT devices is growing: More than a third of maintenance professionals say they use sensors extensively and more are testing them, creating a need for processes, people, and technology that can act on the asset data collected.
- AI is crossing the chasm: More than two-thirds of maintenance teams say they will adopt AI by the end of 2026 despite budget, skill, and security barriers.
Maintenance by the numbers: Stats you need to know for 2026
Maintenance strategy and adoption
- Preventive maintenance is the top maintenance strategy used by maintenance teams, with 71% of maintenance professionals saying they use it. This is followed by reaction/run to failure (38%), predictive maintenance (27%), condition-based maintenance (18%), and reliability-centered maintenance (16%). The 2025 State of Industrial Maintenance
- 58% of facilities spend less than half their time on scheduled maintenance while less than 35% spend a majority on preventive maintenance tasks. The 2025 State of Industrial Maintenance
- Predictive maintenance adoption decreased slightly, going from 30% in 2024 to 27% in 2025. The 2025 State of Industrial Maintenance
- Predictive maintenance can reduce maintenance costs up to 25% and increase uptime by 10% to 20%. Deloitte
Downtime and maintenance costs
- 74% of maintenance leads reported less or the same amount of unscheduled downtime in 2025. The 2025 State of Industrial Maintenance
- 31% of maintenance and operations managers said downtime costs increased in 2025 while 20% said they decreased. The 2025 State of Industrial Maintenance
- 55% of maintenance professionals said that paying more for parts was the main reason for higher downtime costs. The 2025 State of Industrial Maintenance
- Unplanned equipment downtime alone costs the average Fortune 500 company $2.8 billion every year, which is about 11% of revenue. The True Cost of Downtime 2024
- The average large manufacturing plant loses $253 million per year due to unplanned downtime with the average per-hour cost of unplanned equipment downtime roughly doubling between 2019 and 2024. The True Cost of Downtime 2024
- The average manufacturing facility experiences 25 unplanned downtime incidents per month, which add up to 326 hours of downtime per year. The True Cost of Downtime 2024
- The mean time to repair has increased from 49 minutes to 81 minutes on average, driven largely by skills gaps and supply chain delays. The True Cost of Downtime 2024
- The average age of industrial fixed assets is 24 years, which is the oldest average age in nearly 70 years. The 2025 State of Industrial Maintenance
Workforce, skills and labor costs
- A lack of resources is the biggest challenge cited by maintenance leaders with 45% saying it’s their primary obstacle. Aging infrastructure (33%) and a shortage of skilled labor (30%) were the other top challenges. The 2025 State of Industrial Maintenance
- 32% of maintenance managers expect their team headcount to increase over the next 12 months while 31% say they expect their budget to increase in the same time period. Only 4% expect headcount to decrease and 9% say their budget will decrease. The 2025 State of Industrial Maintenance
- 40% of the manufacturing workforce is set to retire by 2030. The 2025 State of Industrial Maintenance
- 88% of facilities outsource some maintenance work, with the average plant outsourcing 23% of tasks. Infraspeak
- 69% of maintenance professionals are 50 years or older. Plant Engineering
AI, predictive maintenance, IIoT, and data collection
- 35% of maintenance professionals say they are using sensors and IIoT devices extensively. 41% say they are testing or considering them. The 2025 State of Industrial Maintenance
- 32% of maintenance teams say they have fully or partially implemented an AI solution across maintenance processes. A further 26% of teams are piloting AI solutions or are actively evaluating options. The 2025 State of Industrial Maintenance
- 65% of maintenance teams expect to adopt AI over the next 12 months. The 2025 State of Industrial Maintenance
- The biggest barrier to AI adoption in maintenance is budget constraints (25%), followed by a lack of expertise (24%), and cybersecurity concerns (22%). The 2025 State of Industrial Maintenance
- Companies facing more downtime are over 2x as likely to be early AI adopters (40% fully implemented vs. 18% among low-downtime peers). The 2025 State of Industrial Maintenance
- 39% of maintenance leaders say they see knowledge capture and sharing as the most valuable use case for AI in maintenance, followed by reducing unexpected equipment failure (36%). The 2025 State of Industrial Maintenance
- Across industries, 58% of manufacturing leaders planned to increase AI spending in 2024, despite concerns about accuracy and value capture. Reuters
- Fortune 500 companies are estimated to save 2.1 million hours of downtime and $233 billion in maintenance costs annually with full adoption of condition monitoring and predictive maintenance. The True Cost of Downtime 2024
- 59% of facilities use a CMMS. Plant Engineering
Five trends and insights that will dominate maintenance management in 2026
1. There’s a gap between AI ambition and AI execution in maintenance
Despite the desire to embrace AI, less than one-third of maintenance and operations teams (32%) have fully or partially implemented it. This marks a transition period for maintenance teams as they move from experimenting with AI to operationalizing it.
It’s likely that thousands of companies will go through this transition over the coming months, as 65% of maintenance teams say they plan to use AI by the end of 2026. Those that emerge from this period as leaders will be the ones that can use AI to deliver tangible value.
2. Manufacturing leaders are investing in maintenance as a competitive advantage
Maintenance is no longer treated only as a cost center. Many leaders plan to protect or grow teams and budgets, signaling a shift toward asset management as a strategic lever. In the latest data, 88% of teams expected their headcount to increase or remain the same, while 73% expect their budget to increase or stay the same.
Manufacturing leaders are signalling a rising value in maintenance through a larger, more stable financial investment. They see maintenance as essential to operational excellence and margin protection. But along with this investment comes greater expectations. Maintenance teams will need to improve the way they track and communicate the value of their return.
3. Finding data will not be a challenge—using it will
Plants already generate plenty of data. Thirty-five percent use sensors extensively and another 41% are testing or considering them. The challenge will not be in getting more data, it will be in making sure this data is clean, organized, and connected so they can make decisions with it.
Manufacturers who want to gain an edge in 2026 will focus on standardizing data, integrating the right systems, and ensuring that the data flowing into their maintenance systems trigger actions that help to increase production efficiency through reduced unplanned downtime.
4. Focus is shifting from frequency to impact
Many facilities report stable or fewer unplanned events, yet downtime costs keep rising. The primary divers of these rising costs are aging equipment and inflation on parts and shipping. Leaders are responding by prioritizing critical assets and lines where a single hour of lost production hurts most. In short, frequency is not the whole story; severity is the budget killer. Set targets by asset and shift resources to the highest-impact risks first.
5. The skills gap raises the stakes for knowledge capture
Leaders cite lack of resources, aging infrastructure, and a skilled labor shortage among their top challenges. As experienced technicians retire, the risk is losing the know-how that keeps assets running. Notably, the top reported AI benefit is knowledge capture and sharing, ahead of even failure reduction. In 2026, winning teams will codify procedures, capture tribal knowledge in the CMMS, and use AI to surface it at the point of work.
Three ways to use these insights to create a maintenance plan in 2026
1. Use data to optimize the impact of your maintenance program
Having a data-based mindset will be more important than ever for maintenance teams. The best way to do this in 2026 is to strengthen your tracking and analysis of maintenance metrics, then prioritize action based on impact to the wider production targets.
One way to do this is to use maintenance statistics from the last year to identify bottlenecks in maintenance operations where machine downtime creates lost revenue. Build an asset management roadmap that targets the highest impact equipment first.
Measure results in asset availability, unplanned downtime, and maintenance cost per production hour so you can prove value quickly.
2. Build a data foundation for predictive maintenance
Clean, standardized, and connected data is the underpinning of effective predictive maintenance. Give a spark to your maintenance program in 2026 by investing in data quality first, then using it to roll out predictive maintenance initiatives.
Start data collection with condition monitoring signals, like vibration or temperature measures, on a few high impact machines. Use connected devices to stream to your historian or business intelligence layer. Integrate with your CMMS so real-time insights trigger work orders, parts picks, and scheduled maintenance windows, closing the loop from alert to repair work.
Prioritize data quality and governance so predictive analytics and machine learning models have the necessary data to predict failures and guide maintenance decisions.
3. Close the skills and labor gap with knowledge capture and AI
Aging equipment and a maturing workforce raise risk. Train maintenance technicians, machinery maintenance workers, and facility managers to use analytical tools and a data driven approach.
Capture tribal knowledge in the CMMS, standardize job plans, and use artificial intelligence to draft procedures, suggest time estimates, and surface troubleshooting steps at the point of work.
Align internal staff and any external partners to the same codes and closeout rules to cut MTTR, reduce labor costs, and improve health management.
Final thought: 2026 will be all about moving from experimentation to execution for maintenance teams
If 2025 was about proving that digital tools can move the needle, 2026 is about operationalizing them. Start where lost revenue is highest, move from collection to action with your data, and make your CMMS the place where the loop is closed. When maintenance teams have real-time insights and time to execute, equipment failures become planned maintenance events—and your plant’s productive capacity goes up.
