Predictive Maintenance Adoption Doubled: What the Maximo-Driven Industries Are Actually Doing Differently

For more than a decade, predictive maintenance has been the technology the industrial world loved to discuss and avoided deploying. Every conference had a keynote about sensor-driven failure prediction, every vendor deck showed a hypothetical bearing failure caught three weeks early, and every operations leader nodded and went back to running their plants on reactive work orders and calendar-based PMs. The surveys told the same story year after year: single-digit adoption, stalled pilots, and a widening gap between what the industry talked about and what it actually shipped. That story just changed. New survey data from Fluke, drawing on more than 600 maintenance professionals across the United States, the United Kingdom, and Germany, shows predictive maintenance adoption rising from roughly 9 percent to 18 percent year over year. Reactive maintenance, which many expected technology to steadily erode, remains flat at about 36 percent. Meanwhile, when those same professionals were asked where their organizations plan to invest next, generative AI led the list at 36 percent, followed closely by industrial AI at 35 percent. Those are not the numbers of a niche technology. They are the numbers of a mainstream shift finally reaching escape velocity. For organizations running Maximo, this shift matters for a specific reason: independent research from Verdantix suggests Maximo users are already operating at a materially higher maintenance maturity level than their industry peers, with roughly 40 percent applying predictive techniques on critical assets versus about 15 percent market-wide. Adoption is doubling everywhere, but adoption is not evenly distributed, and the difference between the leaders and everyone else is less about sensors and more about the systems and discipline wrapped around them. This article looks across the major asset-intensive industries, asks what the doubling cohort is actually doing differently, and examines why some EAM environments turn predictive data into fewer surprises while others turn it into another dashboard nobody watches.

The Numbers Behind the Doubling, and Why They Matter More Than the Headline

The headline statistic is simple enough: predictive maintenance adoption among surveyed maintenance professionals doubled from 9 percent to 18 percent in a single year. But the surrounding numbers tell a more interesting story, and anyone planning an industrial reliability program should read the full picture rather than the topline.

Reactive maintenance sits flat at roughly 36 percent. That flatness is the most underrated finding in the dataset. It means the predictive adopters are not primarily converting reactive shops into predictive ones in a clean substitution. Instead, organizations are layering predictive capability onto a stubborn base of reactive work while squeezing the middle: the calendar-based preventive maintenance that consumes enormous PM labor hours and generates massive volumes of work orders regardless of actual asset condition. The efficiency story of predictive maintenance is not only about catching failures early. It is about finally having a defensible basis for doing less unnecessary PM work, and every hour reclaimed from low-value scheduled work funds the condition-monitoring program that replaces it.

The investment intention numbers reveal the second half of the story. Thirty-six percent of respondents rank generative AI among their top investment priorities, and 35 percent say the same for industrial AI more broadly. Those two figures, sitting alongside the adoption doubling, indicate that organizations expect the barrier to predictive maintenance to keep dropping. Historically, the barrier was expertise: building an accurate failure model required data scientists who understood both vibration signatures and degradation physics. The bet across the industry is that AI-assisted modeling, automated anomaly detection, and increasingly prescriptive tooling will compress that expertise requirement. In the Maximo ecosystem, this is exactly the territory that products like Maximo Application Suite, Maximo Predict, and the new agentic capabilities arriving with MAS 9.2 are designed to occupy.

It is also worth being honest about what the doubling does not mean. Eighteen percent adoption means 82 percent of the surveyed maintenance population is still not running predictive maintenance in any systematic way, and the flat reactive number proves the technology alone has not dissolved the habits and pressures that make reactive work the default. Doubling from a small base is a trend, not a victory. The organizations that read this data as permission to delay will find that the maturity gap compounds: the Verdantix research referenced above suggests this gap is already wide, and every year of accumulated condition data, failure history quality, and analytical practice makes catching up harder, not easier.

Manufacturing: The Volume Play, Where Predictive Scales Through Standardization

Manufacturing remains the natural first home for predictive maintenance because the asset portfolio is repeatable. When a plant runs twenty identical CNC machines or bottling lines spanning six sites, a model built on one asset can be validated and rolled out across its siblings with modest incremental cost. That repeatability is why manufacturing organizations consistently appear on the leading edge of the adoption curve the Fluke data describes.

What distinguishes the mature manufacturing deployments is standardization of the work management layer underneath the prediction. A predictive model that flags a degraded spindle is only useful if the subsequent work order flows through the same lifecycle every time: detection routes to notification, notification converts to a scheduled corrective job, the job carries the right job plan and crafts, and the failure class on the closing record feeds back into the model's training data. Organizations running Maximo Manage in this cohort tend to treat their failure codes, job plans, and asset classifications as enterprise standards rather than site-level preferences, precisely because the analytical layer amplifies whatever data quality exists underneath. Garbage in, garbage predicted.

The second differentiator in manufacturing is measurement discipline. Mature programs track avoided failures in hours of downtime and dollars of lost production, then compare that to the program's cost of sensors, software, and labor. The immature programs, which are far more common, either never establish a baseline or measure success as "the model detected something," which is a vanity metric. A model that detects 200 anomalies per month is not twice as good as one that detects 100; it may be four times worse if it is wrong about most of them and crews stop trusting it. The manufacturing leaders in the predictive cohort treat alert precision as the primary program KPI, and they tune aggressively, often retiring sensor classes that generate noise rather than doubling down on coverage.

The gen-AI investment priority from the Fluke data shows up here too, though in an early and uneven way. Manufacturing organizations are experimenting with AI-generated first-draft work instructions, summarization of equipment history into technician-readable briefs, and natural language interfaces to their CMMS queries. None of that is predictive maintenance in the strict sense, but it lowers the friction of acting on predictions, and friction reduction is increasingly recognized as the binding constraint. A perfect prediction that takes an engineer thirty minutes to translate into a work order will always lose to a decent prediction that becomes a scheduled job in thirty seconds.

Utilities and Power Generation: Where the Consequences Justify the Depth

If manufacturing is the volume play, utilities are the depth play. An outage on a nuclear cooling pump, a forced derate on a combustion turbine, or a distribution transformer failure is not measured in shift productivity but in regulatory exposure, safety consequence, and sometimes millions of dollars per event. That consequence profile changes the calculus: utilities will invest in deep condition monitoring, redundant measurement, and mature analytical processes for a small number of critical asset classes that a manufacturer would never justify across a general fleet.

This is also why the Verdantix maturity findings, which put Maximo-equipped organizations at roughly 40 percent predictive or RCM adoption on critical assets versus 15 percent industry-wide, concentrate in sectors like power and water. In these industries, Reliability Centered Maintenance was not a buzzword but a regulatory and engineering discipline decades before modern AI arrived, and the organizations that built strong failure mode libraries and criticality rankings in that era now have exactly the structured data that predictive analytics needs. The AI conversation in utilities is less about discovering which assets matter and more about applying modern detection to a criticality ranking that already exists.

Consequently, the utilities cohort has a different failure mode in their predictive programs. Where manufacturers struggle with scale and standardization, utilities struggle with the last mile between insight and execution. Condition-based and predictive findings collide with long-outage planning horizons, union work rules, complex permitting, and compliance-driven PM schedules that cannot be reduced even when condition data says the component is healthy. The organizations that make predictive maintenance economically real here are the ones that configure conditional task groups and flexible PM schedules so that condition data can actually modulate the work, rather than running predictions in parallel with a PM program that steamrolls over them. Work management configuration, not analytics sophistication, is the deciding factor in this cohort.

There is also a workforce dimension that utilities surface more honestly than other industries. Their experienced engineers, the ones who could hear a problem in a pump by sound, are retiring in large numbers, and predictive programs are increasingly framed around capturing and operationalizing that expertise before it walks out the door. AI-assisted diagnostics in this context function less like a replacement for experts and more like a way to scale a retiring expert's judgment across a fleet, with human validation retained on any high-consequence call.

Oil, Gas, and Process Industries: The Rotation From Compliance to Value

The process industries have always been on the predictive-maintenance poster because rotating equipment in continuous operations offers a clean technical fit: vibration, thermography, and oil analysis correlate tightly with pump, compressor, and turbine failure modes. What has changed in the current adoption cycle is the economic framing. A decade ago, the pitch was loss avoidance, an insurance argument that struggled to survive annual budget reviews. Today, with energy margins volatile and capital discipline harsh, the pitch that is landing is throughput: fewer unplanned unit trips, less scheduled downtime, extended run lengths between turnarounds, all of it priced directly against production targets.

The 18 percent adopter cohort in oil and gas tends to share a specific structural feature: their condition monitoring and their EAM live in one closed loop. Detection feeds directly into work management, work history feeds back into detection thresholds, and both are reconciled against production data. Where the loop is open, with monitoring tools in one system and Maximo in another and analysts hand-copying between them, programs stall. The integration cost of closing the loop is real, which is part of why the MAS architecture, with its suite-level data layer and integrated applications, resonates in this sector: the historical friction of stitching point solutions together is precisely the friction these organizations are trying to eliminate.

Safety and regulatory context shapes adoption here in ways the raw survey numbers understate. In upstream operations, a failed predictor is expensive; in a refinery, it can be catastrophic. The emerging discipline of prescriptive maintenance, where the system recommends not just a prediction but a validated response, is drawing strong interest in this sector precisely because it forces the human-in-the-loop question: who reviews, who approves, and what happens when the model and the inspector disagree. Process industry leaders are deliberately piloting these governance questions on medium-consequence assets before trusting the loop on high-consequence ones.

The competitive dimension of the Fluke dataset also lands differently in this sector. When a survey shows your peers doubling predictive adoption while your own program is still in pilot, the risk is not immediate and visible, it is an accumulating cost curve disadvantage, and boards in this industry have learned to worry about slow-moving disadvantages. Expect the pressure to spread from early-adopter operators to their contractors and service companies, who will increasingly be asked to demonstrate analytical capability as a condition of winning work.

The Common Playbook: What the Dupliated Cohort Does That Everyone Else Does Not

Strip away the sector differences and the organizations contributing to that doubled adoption figure run a remarkably similar playbook, and it is worth laying it out explicitly because none of it is exotic.

First, they started with criticality, not curiosity. Every mature program began by ranking assets by consequence of failure and targeting the expensive middle: assets critical enough that failures hurt, but numerous enough that improved availability multiplies. Nobody starts with an enterprise-wide sensor rollout, and programs that did, uniformly stalled.

Second, they standardized work management before scaling analytics. Failure codes enforced, job plans structured, asset hierarchies cleaned, and work order completion treated with the discipline of a safety-critical activity. The insight here is that predictive maintenance is downstream of work management quality, and there is no AI that compensates for a decade of inconsistent failure recording. The Verdantix finding that Maximo-heavy organizations show higher maturity is, at least in part, a measurement of this discipline: organizations that committed to a rigorous work management backbone decades ago now find their data is ready for AI in a way their competitors' data is not.

Third, they measured alert precision and technician response, not detection volume. Mature programs know their precision rate, track whether recommended work actually gets scheduled, and compare avoided downtime against program cost quarterly. This financial loop, boring as it sounds, is what keeps predictive programs funded through leadership changes and budget cycles.

Fourth, they treat AI as an accelerator of expertise rather than a substitute for it. The prescriptive-versus-predictive debate now running through the analyst community, with Gartner's 2026 EAM work fueling the argument that a prediction without a recommended and validated action is insufficient, reflects a consensus that human validation remains essential. The programs that work use AI to widen the funnel of what experts can review, then keep people in charge of high-consequence decisions.

Fifth, and perhaps most telling, they plan for the skills transition. The Fluke data on gen-AI and industrial AI investment intentions shows organizations believe the expertise barrier is falling. The adopters act on that belief by retraining planners and reliability engineers into analytical roles, not by imagining a data science department will appear fully formed from a procurement decision.

Each of these five behaviors is available to any organization at essentially any budget level. None requires advanced AI, and indeed the adoption doubling suggests the AI arrives as a consequence of the discipline rather than as a substitute for it.

Practical Implications

For organizations running Maximo today, the doubling of predictive maintenance adoption changes the strategic calculation in three concrete ways. First, the maturity gap that the Verdantix research documents will not self-correct: as adoption moves from 18 percent market-wide toward the majority, the operational cost advantages of predictive capability translate into competitive disadvantage for those who lag, particularly in industries with thin margins or volatile demand. Second, the entry path is now well documented and much less risky than the early-adopter path was; the five-part playbook above came from programs that already made the mistakes. Begin with a criticality ranking, audit your failure code and job plan discipline, and instrument a bounded set of high-value assets rather than attempting a platform-wide program. Third, the convergence of AI investment intentions with adoption reality means tooling will keep improving while your failure history keeps accumulating, which rewards organizations that start cleaning and structuring data now even if they are not ready to deploy models. In a Maximo context specifically, review how conditional task groups, flexible PM scheduling, and integration between condition data and work orders are configured, because these unglamorous settings determine whether predictions can actually change what your crews do this week.

Bottom Line

Predictive maintenance adoption doubling in a single year is the clearest signal yet that this technology has crossed from aspiration into standard practice, at least among the organizations that built the work management discipline to support it. The flat reactive rate reminds us that technology adoption remains uneven, and that the difference between the leaders and everyone else lies less in sensors and models than in data quality, governance, and the willingness to let condition data change scheduled work. For the Maximo community, the Verdantix maturity findings are encouraging but not a resting point: they reflect decisions made years ago, and the doubling cohort shows what the next few years will demand. The organizations that treat predictive maintenance as a work management program accelerated by AI, rather than an AI program hampered by work management, are the ones the next survey will show leading the market.