UpKeep Research
State of Maintenance Report 2026
Data from 214 maintenance and reliability professionals on why the industry is stuck, and what separates the teams that break out.
Letter from the CEO
Letter from Ryan Chan
Maintenance is entering 2026 with more attention than ever, and with less certainty than most teams can tolerate.
The data this year is blunt, but it's not telling us "maintenance teams don't know what to do." It's telling us "maintenance teams can't consistently do what they already know."
That distinction matters because it changes the strategy. If the problem is knowledge, you fix the training content. If the problem is execution, you fix incentives, governance, workflow design, and measurement.
Consider the paradoxes hiding in this year's data:
- 90% recognize the value of preventive maintenance, yet 74.6% remain reactive-dominated or "balanced."
- 75% of manufacturers expect AI to drive operating margins, yet 71% rate their data readiness as inadequate.
- 73% prioritize workforce training over AI investment (a 3-to-1 ratio), yet 21% make no effort to engage younger workers.
- Last year, 67% said they were "turning to contractors," yet this year 74.5% report unchanged contractor usage.
This is why I believe maintenance is becoming one of the clearest sources of competitive advantage inside physical operations. In uncertain environments, the best operators don't win by spending more. They win by executing better.
The organizations that win will do 4 things, in order:
- Build measurement leadership trusts
- Protect planned work
- Invest in workforce capability and knowledge transfer
- Then use technology and AI to scale execution, not to create more dashboards
The report below explains why the industry is stuck, and what separates the teams that break out.
Methodology
Who We Surveyed
Our 2026 State of Maintenance Report captured responses from 214 maintenance and reliability professionals across a diverse range of industries, company sizes, and roles.
Company Size
- 42% Small (1–200 employees)
- 31% Mid-sized (201–1,000)
- 18% Large (1,001–5,000)
- 9% Enterprise (5,001+)
This distribution matters because maintenance challenges manifest differently at different scales. Small organizations often lack dedicated reliability engineers or formal systems. Mid-sized companies find themselves in a "messy middle," too complex for informal systems but without enterprise-grade resources. Large organizations face coordination challenges across multiple sites that smaller teams don't encounter.
Maintenance Team Size
The estimated average is approximately 7–8 technicians per location. A team of 7–8 is a small team with no slack. When one person is on vacation, one is in training, and one gets pulled into an emergency, you've lost 37% of your capacity for the day. Every non-value-add step, every extra form field, every unclear priority, every parts delay, is felt immediately as lost wrench time.
Annual Maintenance Budget
28% don't know their own maintenance budget. This isn't a trivial data quality issue; it signals a fundamental disconnect between maintenance and finance functions. When you don't know your budget, you can't manage to it, demonstrate ROI, or make informed trade-off decisions. Without budget visibility, leadership treats maintenance as a cost center to be minimized rather than a performance driver to be invested in.
Number of Locations Managed
50% of respondents manage 6+ locations, and 26% manage 21 or more. Multi-site operations don't just add complexity; they multiply it. Each site tends to evolve its own practices, asset naming conventions, and documentation habits. When organizations try to benchmark performance or transfer best practices, these local variations become major obstacles. Many multi-site operators report feeling like they have "10 maintenance teams, not 1 system."
Industries Represented
The diversity validates that the challenges identified are not industry-specific. Whether you're maintaining production equipment, HVAC systems, medical devices, or delivery vehicles, the fundamental dynamics show consistent patterns: the reactive trap, workforce challenges, technology adoption barriers, and the execution gap are universal. (54% of respondents hold managerial or supervisory roles, with technicians making up 11% — balancing strategic and ground-level perspectives.)
Geographic Distribution
The strong North American concentration (82% US and Canada combined) reflects UpKeep's primary market, but international representation validates that maintenance challenges cross borders.
The operating context
The 2026 Reality
The reality on the ground is a widening mismatch between what organizations expect from maintenance and what maintenance teams have the capacity to deliver. Three dynamics are compounding.
Capacity is constrained.
The average site runs 7–8 technicians in an environment where equipment complexity is increasing, compliance requirements are growing, and the installed base continues to grow. Most teams aren't sitting around waiting for work, they're stretched thin, handling a workload that has grown faster than headcount.
Complexity is rising.
Half of respondents manage 6+ locations. Each site has evolved its own practices over time, creating fragmentation that undermines benchmarking, knowledge transfer, and central visibility. Many multi-site operators feel like they have 10 maintenance teams, not 1 system.
Confidence is dropping.
28% don't know their annual maintenance budget. When maintenance leaders lack budget visibility, they cannot have informed conversations with leadership about trade-offs or demonstrate ROI. This creates a defensive posture where uncertainty becomes the default stance.
The Weekly Plan Collapse
These three dynamics combine to create what we call the "weekly plan collapse," the systemic trap that keeps organizations stuck in reactive mode despite their best intentions. The cycle works like this:
- The week starts with a schedule. PM tasks are assigned, work orders prioritized, resources allocated. The plan looks reasonable Monday morning.
- Reactive work interrupts. Equipment failure pulls technicians off scheduled work. It's urgent, visible, and production is waiting. The PM can wait.
- Planned work gets pushed. Monday's PM slides to Tuesday. Tuesday's work slides to Wednesday. By Friday, the week's plan is unrecognizable.
- Documentation gets skipped. With the backlog growing and more emergencies arriving, technicians rush through closeouts. Details get omitted. Fields are marked "other."
- Data quality degrades. Incomplete records mean incomplete analysis. Trends become invisible. Repeat failures aren't connected because failure modes weren't documented.
- Reporting becomes less trusted. When leadership asks for metrics, the numbers don't tell a clear story. PM completion rate looks okay, but was the work effective? Metrics create an illusion of insight without actual insight.
- Leadership confidence falls. If maintenance can't demonstrate value clearly, leadership treats it as a black box. A cost center to be minimized rather than invested in.
- Budgets become more fragile. Cost centers get squeezed when times are tight. Investment in prevention gets deferred because ROI isn't visible.
- Hiring gets harder. Budget pressure limits headcount growth. Open positions take longer to fill. Remaining team members absorb more work.
- The team becomes even more reactive. With fewer people and less investment in prevention, the reactive workload grows. More equipment fails. More emergencies consume capacity. The cycle repeats.
The data in this report shows evidence of this loop from multiple angles: 74.6% reactive or balanced, 62.2% rating measurement as fair or worse, 37.3% with strained morale, 63.6% finding hiring difficult. These aren't separate problems; they're symptoms of the same systemic trap.
The findings
The 4 Takeaways
The Industry Knows What to Do But Can't Consistently Do It
This year's data doesn't tell a story of ignorance. It tells a story of an execution trap. 90% of maintenance professionals recognize the value of preventive maintenance. The case for PM has been made and accepted. Yet when we look at actual practice:
- 43.6% Balanced (40–60% reactive)
- 30.9% Mostly reactive (>60% reactive)
- 18.2% Mostly proactive (>60% planned)
- 7.3% Fully proactive (<20% reactive)
74.6% remain reactive-dominated or "balanced." Only 25.5% are mostly or fully proactive. The gap between 90% believing in prevention and 25.5% practicing it is the defining challenge of the industry. It's not a new gap, and it's not closing.
Why This Gap Exists
The top two barriers, staffing/resources and scheduling conflicts with production, are both governance problems, not belief problems. Without protected maintenance windows, planned work competes directly with production demand in every moment. And production demand is immediate, visible, and tied to revenue. It will win that competition nearly every time.
"Most plants know what they should do, but they create self-imposed obstacles to preventive maintenance by placing short-term revenue over machine health, because they view maintenance as a cost rather than an advantage. The plants that break out of this pattern shift the conversation. They make maintenance visible as a revenue protector, not just a cost center."
The Measurement Foundation Is Missing
Only 21.8% can demonstrate ROI clearly or very clearly:
And 62.2% rate their measurement capability as fair or worse:
"Most 'wins' are vibes-based, not data-backed. We're flying blind and calling it progress. Without solid measurement we get zero accountability. The 'trust me, bro' pitch does not cut it! Track the dollars, prove the impact, own the conversation. Leadership needs to hear it, and get it, in terms they understand and care about."
What "Balanced" Really Means
43.6% of respondents describe themselves as "balanced," meaning 40–60% of their work is reactive. That sounds reasonable. It's not what it appears to be. In practice, "balanced" means reactive wins whenever production pressure shows up. And production pressure always shows up.
The standard interpretation says 25.5% are proactive, 43.6% balanced, 30.9% reactive. A more accurate interpretation: 25.5% have built systems where planned work is actually protected, and 74.6% are reactive in practice, some just haven't admitted it yet.
According to Deloitte, predictive maintenance can reduce costs by 25% and increase uptime by 10–20%. The U.S. Department of Energy has documented maintenance program ROI as high as 10:1. McKinsey estimates that predictive maintenance can reduce machine downtime by 30–50% and extend machine life by 20–40%. The barrier to capturing these benefits isn't knowledge. It's execution. And execution is a system problem, not a motivation problem.
The Contractor Escape Hatch Didn't Work
Last year's report told a clear story: 67% of organizations were turning to contractors to address staffing challenges. The narrative was compelling: outsource what you can't hire, use contractors to fill the gap. This year, we tested that assumption.
Current contractor usage: mean 16.7% of maintenance work, median 11%, range 0% to 76%. The median of 11% suggests that for most organizations, contractors are a supplement, not a replacement for in-house capability.
74.5% report unchanged contractor usage. Only 9.1% actually increased. More organizations decreased contractor usage (16.4%) than increased it. The "contractor boom" was overstated. The escape hatch didn't work as expected.
"Access to specialized skills" (74.8%) dominates. But "filling labor shortages," the use case suggested by last year's narrative, ranks third at only 29.7%. Contractors are primarily used for specialized work, not as a substitute for general maintenance headcount.
- 42.3% Inconsistent quality
- 42.3% Higher long-term costs
- 28.8% Knowledge loss / poor transfer
- 24.3% Cultural / communication issues
The top two challenges both tied at 42.3%. While contractors provide access to specialized skills, they don't provide a reliable or cost-effective long-term solution to general labor challenges.
The Root Cause: You Can't Outsource Your Way to Reliability
Reliability requires deep knowledge of specific equipment, understanding of how assets interact, accumulated wisdom about failure patterns, and consistent execution over time. These capabilities live in people and organizational systems. They take years to develop and can't be purchased from an outside provider.
Contractors can provide labor hours and specialized skills for specific tasks. But they can't provide the institutional knowledge that makes maintenance effective. Organizations that tried to substitute contractors for in-house capability discovered that quality varied unpredictably, costs were higher than expected, knowledge wasn't retained, and the underlying capability gap remained.
75% Expect AI to Drive Margins, But 71% Aren't Ready
The gap between AI expectations and AI readiness is the starkest finding in this year's data. External research from TCS and AWS shows 75% of manufacturers expect AI to become one of the top three contributors to operating margins by 2026. Our survey tells a different story about where the industry actually is:
72.7% are not using AI or only exploring. Only 6.4% report widespread integration. The gap between expectation and reality is enormous. And it's not because the industry is skeptical:
- 46.4% View AI as a useful tool
- 22.7% Neutral / wait and see
- 15.5% Excited about AI's potential
- 15.5% Concerned about job impact
61.9% are either excited or view AI as a useful tool. The industry wants AI to work. There's no cultural resistance. The barrier isn't enthusiasm; it's readiness.
The Bottleneck Is Data
70.9% rate their data readiness as fair, poor, or undeveloped. Only 6.4% consider themselves well-prepared. Notice the parallel: 6.4% are well-prepared for AI, and 6.4% have widespread AI integration. This isn't coincidence. The organizations that have achieved meaningful AI adoption are the ones that first achieved data readiness.
"I thought the hardest part would be choosing the right AI tools, but the real challenge was getting our data in shape. We had to spend far more time cleaning, standardizing, and structuring information than I expected. Without that groundwork, the AI insights were inconsistent and misleading. Start small and focus on your data first, treat data readiness like building the foundation of a house. Everything else depends on it."
The Talent Crisis Is Partly Self-Inflicted
The workforce crisis is real and well-documented. 63.6% of organizations now find talent attraction difficult, up nearly 10 percentage points from 54% in 2024.
But here's what makes part of this crisis self-inflicted: 21% of organizations make zero effort to engage younger workers. No apprenticeships, no school partnerships, no internships, no mentoring. If 64% find hiring difficult and 21% make no effort to build pipeline, some organizations are both suffering from the talent crisis and actively contributing to it through inaction.
The Compounding Crisis
37.3% report fair, poor, or very poor morale, and 30% rate knowledge transfer as ineffective. With 40% of the workforce set to retire in the next 5 years, that's institutional knowledge walking out the door with no plan to capture it. These factors create a vicious cycle: experienced workers retire or burn out, their knowledge isn't captured, remaining workers are overloaded, morale declines, more workers leave, hiring gets harder, and the crisis accelerates.
The Investment Priority Data
Workforce training is prioritized 3-to-1 over AI investment (73% vs. 24.3%). Despite all the AI hype, maintenance leaders are saying: "Our constraint is people capability, not technology." That's the right instinct, but instinct isn't enough when 21% are making no effort to build the pipeline.
"Building a resilient workforce is a decade-long investment, not a quick fix. When organizations commit to attracting young talent and developing people, they don't just fill roles, they create a culture where knowledge is shared, morale is high, and employees stay. In the end, it's not technology but people, well-trained, engaged, and valued, who become the true competitive advantage for your business."
A practical playbook
What Winners Do Differently
The takeaways above describe where the industry is stuck. Winners don't start with technology. They start with operating discipline, and then they use technology to scale what already works.
Build a Measurement Spine Leadership Trusts
Choose 3–5 KPIs that connect directly to business outcomes. Standardize definitions so metrics mean the same thing across sites, shifts, and time periods. Review weekly, not quarterly. Never accept "we're improving" without "we improved by this much, measured this way."
The outcome: Maintenance becomes a measured business function with defensible ROI rather than a cost center with anecdotal value claims.
Create a Planned Work Engine
Negotiate protected maintenance windows with operations. Run weekly scheduling meetings that include both maintenance and operations stakeholders. Track schedule breaks explicitly: what was scheduled, what happened instead, why. Measure and report reactive-to-planned ratio as a leadership KPI.
The outcome: Planned work actually happens rather than existing only on paper.
Design Workflows Around Technician Reality
Simplify closeout to 5 essential fields rather than 15 fields that create burden. Make mobile field execution the default: fast, works offline, doesn't require returning to a desktop. Design parts workflows that minimize trips and waiting. Test workflows with actual technicians in actual field conditions, not in conference rooms.
The outcome: Adoption becomes natural because workflows reduce friction. Data quality improves because good data is the easy path.
Industrialize Training and Knowledge Transfer
Build job plans that capture procedural knowledge in structured, reusable formats. Create lightweight knowledge capture that happens as part of work, not after work. Establish mentoring pathways with defined expectations for both mentors and mentees. Develop training curricula regularly updated based on actual failure patterns.
The outcome: Institutional knowledge becomes organizational knowledge that persists beyond individual tenure. New workers ramp faster.
Standardize the Core System Across Sites
Enforce consistent asset hierarchy and naming conventions across all sites. Use standardized PM templates that can be adapted to site-specific equipment. Require consistent closeout discipline so data means the same thing everywhere. Allow local flexibility in scheduling and resource allocation, but not in data structure.
The outcome: Multi-site organizations function as one system rather than many independent teams. Scale becomes an advantage rather than a source of complexity.
"The biggest hurdle wasn't the schedule, it was the mindset shift. Though there was initial leadership commitment, reduced fire drills quickly changed the culture. The real breakthrough came when we reframed maintenance as preventing tomorrow's outages, not just 'nice-to-have' work. Protecting the maintenance window became one of the biggest drivers of long-term reliability."
Implications
What This Means for AI
AI will matter in maintenance, but not as magic prediction. The near-term value of AI is operational leverage: reducing friction in execution, improving consistency, and upgrading workflows so good data happens as a side effect.
A Better Approach
The strategy is not "go deploy AI." The strategy is "make one workflow AI-ready, prove value and scale."
- Pick one workflow where success is measurable. A specific process (work order triage, PM scheduling, parts forecasting) where you can define and measure success.
- Fix the inputs. Before deploying AI, fix the data quality issues in that specific workflow. Standardize the taxonomy. Clarify the cause codes. Clean up the asset hierarchy for assets involved in this workflow.
- Define what "better" means. Set measurable outcomes: time-to-assignment reduced by X%, first-time fix rate improved by Y%, schedule adherence improved by Z%.
- Deploy and measure. Implement AI in the specific workflow. Measure against defined success criteria. Give it time to generate meaningful data, typically 3–6 months.
- Scale to the next workflow. Once one workflow is demonstrably improved, apply the same approach to the next. Build momentum through demonstrated wins.
Where AI Wins Early
| Application | What It Does | Why It Works |
|---|---|---|
| Work request triage | Automatically categorizes and routes incoming requests | Reduces dispatcher workload; improves consistency |
| Work order structuring | Turns unstructured technician input into standardized data | Improves data quality as a byproduct of normal work |
| Suggested troubleshooting | Surfaces relevant history and likely causes based on symptoms | Helps less experienced technicians perform like veterans |
| Schedule optimization | Adjusts PM schedules based on actual conditions and constraints | Prevents both over-maintenance and under-maintenance |
| Parts forecasting | Predicts demand to optimize inventory levels | Reduces stockouts and excess inventory |
| Documentation prompts | Nudges technicians for missing information at closeout | Improves completeness without adding friction |
These applications make existing work easier rather than requiring new behaviors. They reduce friction rather than adding it. The AI that gets used is the AI that helps technicians do their job better.
Looking ahead
What's Coming in 2027 and 2028
The data in this report points toward several shifts that will accelerate over the next two years.
Shift 1: Maintenance Will Be Judged on Financial Outcomes, Not Activity
The measurement gap (62.2% rate capability as fair or worse) is a pressure signal. Leadership will increasingly demand clarity about what maintenance spending is actually delivering. Activity metrics like PM completion % will no longer be sufficient. Organizations that can articulate ROI in business terms will maintain investment.
Shift 2: Technician Experience Will Become a Retention Weapon
With 37.3% reporting strained morale and 63.6% finding hiring difficult, retention is increasingly critical. Technician experience, how the daily work actually feels, will become a competitive factor. Organizations that reduce chaos, simplify workflows, and invest in tools technicians like will retain better.
Shift 3: Multi-Site Standardization Becomes Mandatory
With 50%+ managing multiple locations, multi-site governance will move from "nice to have" to required. Leadership will demand benchmarking capability: the ability to compare sites, identify top performers, and transfer best practices. Systems that allow variability in data structure will be replaced with systems that enforce consistency.
Shift 4: AI Shifts from Insight to Execution
The first wave of AI in maintenance focused on insight, dashboards, predictions, alerts. The next wave will focus on execution, actually making work happen differently. The winners won't be organizations that "use AI," they'll be organizations that operationalize AI inside workflows where it drives measurable improvements.
Conclusion
The 2026 data doesn't tell a story of an industry that lacks awareness. It tells a story of an industry stuck in an execution trap.
- Reactive work remains dominant, even though teams know prevention works. 90% believe in PM; 74.6% are still reactive or "balanced."
- The contractor escape hatch didn't materialize, organizations are managing the crisis in place, not outsourcing their way out. 74.5% report unchanged contractor reliance.
- AI excitement is high, but readiness is low, 75% expect AI to drive margins, but 71% aren't ready because data quality is a workflow problem, not a technology problem.
- The talent crisis is intensifying, while 21% make no effort to build pipelines and 30% have ineffective knowledge transfer. The crisis is partly self-inflicted.
The way out is not more hype and not a bigger initiative. The way out is a better operating system:
- Build measurement leadership trusts, so maintenance becomes a measured business function with defensible ROI.
- Protect planned work, so prevention actually happens instead of existing only on paper.
- Standardize workflows and knowledge transfer, so good data is the byproduct of work and knowledge doesn't walk out the door.
- Then apply technology and AI, where it reduces friction and scales execution, not where it creates more dashboards.
About
About UpKeep
UpKeep is an Asset Operations platform built for maintenance, facilities, and reliability teams. We help organizations close the execution gap by making it easier for technicians to do their best work, turning maintenance data into actionable insights, and connecting operations across sites and systems.
This report reflects our commitment to the maintenance community. We publish it annually to provide honest data and practical guidance that helps teams address the real challenges they face.
This report synthesizes findings from UpKeep's 2025–26 survey, the 2025 State of Maintenance Report, and validation from external sources including McKinsey, Deloitte, Harvard Joint Center for Housing Studies, TCS/AWS, the U.S. Congress Joint Economic Committee, The Manufacturing Institute, the National Fire Protection Association, and leading industry publications.
- Deloitte and The Manufacturing Institute, "2023 Manufacturing Industry Outlook"
- McKinsey & Company, "Building the vital skills for the future of work in operations"
- Harvard Joint Center for Housing Studies, "The State of the Nation's Housing 2023"
- U.S. Congress Joint Economic Committee, "Addressing the Skilled Trades Gap"
- National Fire Protection Association, "2023 Skilled Trades Workforce Survey"
See what closing the execution gap looks like
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