Predictive Maintenance Adoption Rates: What the Surveys Show

Every vendor selling sensors, software, or analytics quotes an adoption stat. The trouble is that those stats don’t agree. Depending on which survey you pull, somewhere between 27% and 51% of plants “use predictive maintenance.” That’s a 24-point spread for what looks like the same question.

The gap isn’t sloppy math. It’s who got surveyed, what year, and what counts as predictive maintenance in the first place. A technician with a thermal camera on a monthly route checks the same box as a plant running online vibration sensors into a machine-learning model.

So this page does what the vendor decks won’t: it rates each adoption figure by how well it traces to a credible primary source, and it tells you which numbers are solid and which are folklore.

What these numbers actually measure

There are three different questions hiding under “predictive maintenance adoption,” and they produce three different sets of numbers.

The first is strategy adoption: does your plant use predictive maintenance at all? This is the headline number, and it’s the one that swings the most. The Plant Engineering Maintenance Study, a long-running survey of US industrial facilities, has tracked it for years. MaintainX surveys a different population and lands lower.

The second is maturity depth: how advanced is that program? PwC and Mainnovation’s maturity model is a useful published framework here, running from periodic visual inspection (level 1) up to data-driven predictive maintenance with machine learning (level 4, what they call PdM 4.0). This is where the optimistic adoption numbers collapse.

The third is the per-technology split: of plants doing predictive maintenance, how many use vibration analysis versus infrared thermography versus oil analysis versus ultrasound? This is the data vendors most want. The most recent source (ARC’s, below) charts the technologies but doesn’t publish exact, labeled per-technology usage rates, and the precise percentages that circulate online trace to old vendor surveys.

The Reliable Confidence Score

The Big Takeaway

Predictive maintenance is widely discussed and unevenly practiced. The “we use it” figure lands anywhere from 27% to 51% depending on the survey, and the spread is mostly about who got asked and when.

The number that tells you something real is PwC’s: about 11% of surveyed companies placed themselves at the top maturity level, where predictive maintenance runs on machine learning and large datasets. Two-thirds were still at the bottom two levels, relying on visual and instrument inspections. That’s a long way from the AI-driven future the vendor decks imply. (That 11% is European, from 2017, and based on self-assessment.)

Adoption sits somewhere between 27% and 51% depending on who’s asked, which tells you the question matters as much as the answer.

Why the numbers disagree

Who got surveyed. Plant Engineering polls US industrial facilities, broadly. PwC and Mainnovation surveyed 280 firms in Belgium, Germany, and the Netherlands. MaintainX surveyed 1,320 maintenance professionals across the US and Canada. ARC surveyed 511 North American practitioners with a vendor sponsoring the work. Different samples, different answers.

How the question was worded. “Do you use predictive maintenance?” and “Have you reached data-driven predictive maintenance?” are different questions. The first gets a yes from anyone with a thermal camera. The second is the 11% figure. Vendors quote the first and imply the second.

What counts as adoption. A plant that took two vibration readings last quarter counts. So does a plant with continuous condition monitoring feeding a predictive model. Same checkbox, very different reality.

Timing. Adoption is moving, so a 2018 figure and a 2025 figure aren’t directly comparable. Much of the precise per-technology data still in circulation predates the cheap-sensor era entirely.

How to use these numbers safely

Lead with the maturity figure when you want something defensible. “Around 11% of surveyed firms placed themselves at the top predictive-maintenance maturity level, per PwC and Mainnovation” is a claim you can stand behind, as long as you note it’s European and from 2017. “Half of plants use predictive maintenance” needs an immediate qualifier about who was surveyed.

Always name the source and the year, and separate “uses any predictive maintenance” from “runs data-driven predictive maintenance.” They differ by a wide margin.

For the technology mix, the most current source is ARC’s March 2026 survey, but it’s client-sponsored and doesn’t give exact, labeled per-technology usage rates. If you need a split between individual technologies, say plainly that the circulating numbers come from older vendor surveys and haven’t been replicated independently.

Where teams go wrong

Quoting precise per-technology percentages (vibration at exactly this, oil at exactly that) as if someone measured them last year. Those exact figures trace to old vendor surveys with no recent independent replacement. The most current source, ARC’s March 2026 survey, charts the technologies but doesn’t publish exact, labeled per-technology usage rates, and it’s vendor-sponsored.

Confusing the Plant Engineering 52% CMMS figure with run-to-failure. Aggregator roundups do this constantly, and many also mislabel the 2020 study as 2021. On the primary source it’s the 2020 study, 52% is CMMS adoption, and run-to-failure is 51%.

Treating intent as deployment. “60% plan to adopt PdM 4.0” and “11% have adopted it” are both PwC figures, and only one of them describes what’s actually running on plant floors. The same goes for the 65% who “plan to use AI by 2026.”

Pitting a strategy number against a maturity number. Someone sees “51% use predictive maintenance” next to “11% are data-driven” and assumes one of them is wrong. They measure different things. Both can be true at once.

Methodology

We prioritized primary and strongly authoritative sources: the Plant Engineering Maintenance Study (published findings on plantengineering.com), the PwC and Mainnovation Predictive Maintenance 4.0 reports (2017 and 2018), ARC Advisory Group’s March 2026 maintenance-productivity report, and MaintainX’s 2025 State of Industrial Maintenance. Deloitte appears only for context on benefits, not adoption, since its well-known figures describe outcomes (downtime reduction, cost savings) rather than how many plants use what.

Confidence ratings reflect traceability, not how often a number gets repeated. The ARC figures are rated Medium: the report is recent and methodologically transparent (511 North American respondents, surveyed Q4 2025), but it is client-sponsored, and while it charts vibration, ultrasonic, and oil analysis by type across four adoption-status categories, it doesn’t put exact, labeled usage percentages on each. The ~40% in its text is the “currently using and expanding” category rather than total current usage, so we did not present it as a per-technology adoption rate. The precise per-individual-technology splits that circulate online are treated as a negative finding because they trace only to dated vendor surveys with no open, independent replacement, so we declined to publish specific numbers for them.

Three notes worth flagging. First, several stat roundups report the Plant Engineering 52% as a run-to-failure figure and date the study to 2021; on the primary source it’s the 2020 study, with 52% as CMMS adoption and run-to-failure at 51%. Second, the 11% maturity finding comes from PwC’s 2017 study of 280 companies; the 2018 follow-up surveyed 268, and the two are easy to mix up. Third, the often-quoted upper-bound adoption figures (above 60%) trace to surveys we could not verify against a credible primary, and at least one of them is actually an AI-adoption number rather than a predictive-maintenance one, so we capped the defensible range at 51%. SEO vendor stat pages were excluded as load-bearing citations.

The Short Version

Predictive maintenance adoption is real but shallow. Most plants run some preventive maintenance (88%), about half still let some assets run to failure, and the “we use predictive maintenance” number ranges from 27% to 51% depending on who’s surveyed. The figure that holds up: about 11% of surveyed firms placed themselves at the top maturity level, where predictive maintenance runs on machine learning, per PwC and Mainnovation (Europe, 2017).

The most recent technology data comes from ARC’s March 2026 survey, which charts vibration, ultrasonic, and oil analysis but is client-sponsored and doesn’t publish exact, labeled per-technology usage rates. The precise per-technology splits circulating online trace to old vendor surveys, so treat them as folklore until someone runs a fresh, independent count.

Sources

• Plant Engineering, 2020 Maintenance Study findings (published at plantengineering.com): https://www.plantengineering.com/the-maintenance-function-like-manufacturing-itself-is-a-rapidly-changing-environment/
• Plant Engineering, 2016 Maintenance Study (seven key findings): https://www.plantengineering.com/articles/2016-maintenance-study-seven-key-findings/
• PwC and Mainnovation, Predictive Maintenance 4.0: Predict the Unpredictable (2017): https://www.pwc.nl/en/publicaties/predictive-maintenance-40-predict-the-unpredictable.html
• PwC and Mainnovation, Predictive Maintenance 4.0 (overview and 2018 follow-up): https://www.mainnovation.com/publications/predictive-maintenance-4-0/
• ARC Advisory Group, Technology Adoption and Its Impact on Maintenance Productivity (March 2026): https://www.arcweb.com/industry-best-practices/technology-adoption-its-impact-maintenance-productivity
• MaintainX, The 2025 State of Industrial Maintenance: https://www.getmaintainx.com/newsroom/state-of-industrial-maintenance-report-2025
• Deloitte Insights, Using predictive technologies for asset maintenance (context, benefits): https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/industry-4-0/using-predictive-technologies-for-asset-maintenance.html