Predictive Maintenance in 2025: Why the Conversation Shifted From Technology to Execution

Predictive maintenance entered 2025 with momentum and frustration in equal measure. Adoption is undeniably higher than it was three or five years ago, yet many reliability engineers would struggle to say that outcomes have improved at the same pace. More sensors are installed, more data is collected, and more dashboards are reviewed, but unplanned downtime, reactive work, and maintenance backlogs remain stubbornly familiar.

That tension explains why the predictive maintenance conversation changed this year. The focus moved away from whether the technology works and toward whether organisations can actually use it. For reliability leaders, 2025 has been less about algorithms and more about architecture. Not IT architecture, but the operational systems that sit between insight and action.

This part sets the context. Before looking at trends, tools, or future direction, it is worth understanding what reliability practitioners spent their time thinking about in 2025 and why predictive maintenance now lives or dies by execution rather than detection.

One important note before we begin. The perspectives shared in this article are drawn primarily from our experience (the Factory AI team) working with manufacturing sites across Oceania and the United States. That inevitably shapes how we see the world. We do our best to stay grounded in evidence and outcomes, but we do not pretend to have a monopoly on truth, nor do we get everything right. Read this as an informed point of view, not a universal one.

What Reliability Leaders Actually Focused on in 2025

Scan the reliability literature from the past year and a pattern emerges quickly. The dominant themes were not artificial intelligence, machine learning models, or the latest sensor hardware. Instead, the conversation revolved around work management, planning and scheduling, backlog control, workforce capability, and decision ownership.

There was a strong emphasis on work execution management as the foundation of reliability performance. Articles on planning effectiveness, maintenance scheduling discipline, and backlog health appeared repeatedly. The underlying message was clear. Even the best condition data has limited value if work cannot be planned, prioritised, and executed consistently.

Condition monitoring itself was discussed through the lens of maturity rather than novelty. Practitioners focused on how programmes evolve over time, where they plateau, and why many never progress beyond route-based inspections or basic alarms. The idea that condition monitoring is a capability to be built, not a tool to be installed, came up again and again.

Another recurring thread was the difficulty of turning data into action. Analysis without decisions was described as one of the most expensive failure modes in reliability. Many teams are information rich but decision poor, with alerts reviewed but not owned, investigated but not closed out, or acknowledged but never converted into work.

These themes matter because they frame the environment predictive maintenance must operate within. In 2025, reliability leaders were not asking for more data. They were asking for fewer surprises, clearer priorities, and systems that reduce cognitive and administrative load rather than add to it.

Why Predictive Maintenance Is No Longer the Hard Part

For much of the past decade, predictive maintenance struggled with credibility. Sensors were expensive, connectivity was unreliable, and advanced analytics required specialised expertise that most sites did not have. Those barriers have not disappeared entirely, but they are no longer the primary constraint.

By 2025, predictive maintenance technology is broadly proven. Wireless vibration and temperature sensors are mature. Cloud infrastructure is reliable. Machine learning models for anomaly detection are well understood. Even integration pathways into historians, PLCs, and enterprise systems are far clearer than they once were.

Yet many programmes still stall. Pilots run longer than planned. Alerts are generated but not acted on. Teams disengage after early enthusiasm fades. The issue is not that predictive maintenance fails to detect problems. It is that detection alone does not change outcomes.

The quiet realisation across the reliability community is that predictive maintenance does not fail technically as often as it fails organisationally. It exposes weaknesses in planning, decision-making, ownership, and resourcing that already existed. In that sense, predictive maintenance has become less of a solution and more of a stress test.

Predictive Maintenance and the Work Management Reality

One of the strongest signals from 2025 is that predictive maintenance cannot be evaluated in isolation. Its effectiveness is tightly coupled to work management maturity.

If a site struggles with reactive work dominating the schedule, predictive alerts become another source of interruption rather than improvement. If backlog prioritisation is unclear, alerts compete with breakdowns, inspections, and urgent production requests. If planners lack confidence in the data or authority to act, early warnings expire unused.

This is why so many reliability articles returned to fundamentals. Planning quality, scheduling discipline, and clear work ownership are not separate initiatives from predictive maintenance. They are prerequisites. Without them, predictive maintenance amplifies noise instead of value.

Reliability engineers understand this instinctively. A vibration alarm that arrives without a clear response pathway is not helpful. An anomaly detected on a Friday afternoon with no spares available and no planned window does not prevent failure. Predictive maintenance only delivers value when it fits into a system designed to absorb and act on early information.

From Activity to Architecture

A notable shift in 2025 was the language reliability leaders used. There was less focus on activities and more on architecture. Instead of asking how many inspections were completed or how many alerts were generated, the questions became more structural.

Who owns this asset’s health decision.
Who decides whether an alert becomes work.
What happens if the alert is ignored.
How is feedback captured and used to improve future decisions.

These questions signal a move toward reliability as a system, not a collection of tasks. Predictive maintenance fits naturally into this way of thinking, but only if it is treated as part of the architecture rather than an overlay.

This also explains why discussions around data governance, asset information quality, and naming conventions gained attention. Predictive maintenance depends on clarity. Ambiguous asset hierarchies, inconsistent failure codes, and poor historical records make it harder to trust insights and slower to act on them.

In 2025, many teams realised that improving predictive maintenance outcomes often starts with unglamorous work. Cleaning up asset registers. Defining criticality consistently. Agreeing on response standards. None of these are advanced analytics problems, but all of them shape whether analytics deliver value.

Why “More Data” Is No Longer the Answer

Another theme that surfaced repeatedly is scepticism toward the idea that more data automatically leads to better decisions. Reliability engineers have lived through enough initiatives to know that volume does not equal value.

In practice, too much data without context increases the burden on already stretched teams. Alerts arrive without confidence levels. Trends appear without operational explanation. Engineers spend time interpreting charts instead of planning work.

This is where predictive maintenance programmes often stall. The system detects something unusual, but the human cost of figuring out what to do next is too high. When faced with competing priorities, teams default to the familiar. Preventive maintenance continues as scheduled. Reactive work takes precedence. Predictive insights are deferred.

The lesson from 2025 is not that data quality does not matter. It does. But decision clarity matters more. Predictive maintenance that simplifies choices, reduces ambiguity, and fits into existing workflows is far more likely to succeed than systems that deliver technically impressive but operationally heavy outputs.

The Emerging Consensus Among Practitioners

Taken together, the reliability conversation in 2025 points to a clear consensus. Predictive maintenance is no longer judged by how advanced it looks, but by how quietly it improves day-to-day reliability.

The most respected programmes are not the ones with the most sensors or the most complex models. They are the ones that help teams intervene earlier with less effort, remove unnecessary work, and learn systematically from failures and near misses.

This mindset sets the stage for the trends that matter. It explains why decision support is gaining traction, why integration with work management is critical, and why pilot design has become such a sensitive topic. Predictive maintenance has matured to the point where its success depends less on innovation and more on discipline.

In the next part, we will look at the specific predictive maintenance trends that emerged in 2025 and separate those that genuinely changed outcomes from those that mostly changed marketing language.

Predictive Maintenance in 2025: The Trends That Actually Changed Outcomes (And the Ones That Didn’t)

By 2025, most reliability engineers had developed a healthy scepticism toward “trend” articles. Too often, trends arrive with big claims, glossy diagrams, and very little impact on Monday morning. This year felt different. Not because the technology suddenly became magical, but because the pressure to show real operational value finally caught up with the hype.

Some trends genuinely changed how predictive maintenance delivers value. Others mostly changed the language on vendor websites. The difference matters, especially for teams who have already been through one or two PdM initiatives and are understandably wary of the next “breakthrough”.

Let’s separate the two.

Trend 1: Predictive Maintenance Is Shifting From Detection to Decisions

For years, anomaly detection was treated as the finish line. If the system could detect something unusual, the job was considered done. In practice, that is where the hard work actually begins.

In 2025, leading programmes started to move past detection and focus on decision support. Reliability teams no longer want to be told that vibration is high. They want to know why it might be high, how confident the system is, and what typically fixes this issue on similar assets.

This shift sounds subtle, but it changes everything. A raw alert creates work. A well-framed recommendation reduces work. One adds cognitive load. The other removes it.

There is also a quiet acknowledgement behind this trend. Most sites do not have the luxury of specialist analysts reviewing every alert. The system has to do more of the thinking up front, or the insight simply will not be used. Decision support is not about replacing engineers. It is about respecting their time.

And yes, there is humour in the reality. No one ever said, “Thank goodness, another alert with no suggested action. I was worried my day might be too quiet.”

Trend 2: Combining Vibration With Process Context Is Becoming Non-Negotiable

One of the most important technical shifts in 2025 is also one of the least flashy. Predictive maintenance programmes are increasingly combining vibration data with process and operational context.

Vibration alone is powerful, but ambiguous. A spike can mean misalignment, lubrication issues, load changes, or operator behaviour. Without context, engineers are left guessing, often under time pressure.

By integrating process data such as speed, load, product changes, or temperature, teams gain clarity. The same vibration pattern can mean very different things depending on operating conditions. When context is available, false positives drop and confidence rises.

This matters operationally. Engineers trust systems that explain themselves. They ignore systems that cry wolf.

There is also a cultural benefit. When alerts are clearly linked to operating practices, conversations shift. Instead of debating whether the sensor is wrong, teams discuss how the asset is being run. Predictive maintenance becomes a learning tool, not a fault-finding exercise.

That change alone has probably prevented more quiet eye-rolls in maintenance meetings than any algorithm ever will.

Trend 3: Hardware Is a Commodity, Deployment Is Not

If you listen to marketing, you might think sensor choice is the most important decision in predictive maintenance. In reality, by 2025, sensors are rarely the limiting factor.

Most wireless vibration and temperature sensors are good enough. They are smaller, cheaper, and easier to install than they were a few years ago. That is not where programmes succeed or fail.

Deployment is where things get interesting. Asset selection, sensor placement, operating profiles, and access constraints matter far more than brand names. A perfectly accurate sensor on the wrong asset delivers zero value.

Leading teams have become far more selective. Instead of asking, “What can we monitor,” they ask, “What can we act on.” Assets are chosen based on failure modes, downtime impact, and the ability to intervene early, not just criticality scores.

There is a quiet maturity in this approach. It accepts that not everything needs to be monitored and that value density matters. Fewer sensors, better outcomes. A concept that would have been heresy a few years ago.

Trend 4: The Death of the ‘Perfect Pilot’

In 2025, pilots are still everywhere. What changed is how seriously they are taken.

The old model was familiar. Run a long pilot. Collect lots of data. Produce a compelling report. Then struggle to scale because the conditions were too controlled and the effort too high.

Leading organisations have moved on. Pilots are now smaller, faster, and intentionally imperfect. The goal is not to prove the technology works in ideal conditions. That question has largely been answered. The goal is to prove the organisation can respond to early warnings under real constraints.

This means shorter timelines, fewer assets, and a sharper focus on action taken. Success is measured in interventions completed, not charts produced.

There is also more honesty. If a pilot fails because alerts are ignored or work cannot be planned, that is not brushed aside. It is treated as valuable information. Predictive maintenance is revealing a bottleneck, not causing one.

In many cases, the pilot does its most important work by failing early and cheaply.

Trend 5: Predictive Maintenance Is Quietly Rewriting Preventive Maintenance

This trend rarely gets headline billing, but it may be the most financially impactful.

In 2025, many teams began using predictive insights to challenge long-standing preventive maintenance routines. Bearings replaced on fixed intervals were left in service longer. Lubrication frequencies were adjusted. Inspections were reduced or removed altogether.

This is not about cutting corners. It is about aligning work with actual asset condition. For sites under cost pressure, the ability to safely remove unnecessary PMs is often more valuable than preventing a rare catastrophic failure.

There is also a morale benefit. Technicians are acutely aware when they are performing work that does not add value. Reducing low-value PMs improves engagement, even if no one writes a case study about it.

Predictive maintenance earns its place when it helps teams do less work, not more.

The Trends That Mostly Changed Marketing Language

Not every trend in 2025 deserves equal attention. Some are more noise than signal.

Artificial intelligence branding continued to accelerate. Everything became “AI-powered,” whether it meaningfully changed behaviour or not. For reliability engineers, this quickly lost its novelty. The question shifted from “Is it AI” to “Does it reduce downtime or workload.”

Similarly, talk of fully autonomous maintenance remained largely theoretical. The idea that systems will automatically schedule and execute maintenance without human involvement makes for good conference slides. On the plant floor, reality is more constrained. Safety, risk, and accountability still matter.

That does not mean automation is irrelevant. It means expectations have matured. Reliability leaders are pragmatic. They are not waiting for perfection. They are looking for incremental improvements that compound over time.

What These Trends Tell Us About Maturity

Taken together, the real trends of 2025 point to a broader shift. Predictive maintenance is no longer evaluated as a standalone technology. It is judged by how well it integrates into the reliability system as a whole.

Detection is assumed. Decision quality is differentiating. Deployment discipline beats technical novelty. And success is measured in work avoided as much as work completed.

This is a sign of maturity. It is also why predictive maintenance initiatives now succeed or fail faster than they used to. There is less patience for dashboards that look impressive but change nothing.

In the next part, we will look at the harder lessons 2025 reinforced. The ones that are uncomfortable, occasionally humbling, and entirely familiar to anyone who has spent time in a plant.

Predictive Maintenance in 2025: The Hard Truths Reliability Leaders Learned the Slow Way

By the time most reliability teams reach 2025, they are no longer naïve. They have seen technologies come and go. They have survived at least one initiative that promised transformation and delivered… dashboards. What remains is not cynicism, but discernment.

This part is about the lessons that refused to go away. The truths that kept resurfacing, regardless of industry, asset class, or software vendor. None of them are new. All of them matter more than ever.

And yes, most of them were learned the slow way.

Truth #1: Most Failures Were Predictable, and Still Not Prevented

This is the one that hurts a little.

In 2025, many sites quietly acknowledged what the data had been saying for years. A large proportion of failures were preceded by warning signs. Vibration drift. Temperature creep. Subtle changes that did not trigger alarms at first, but were visible in hindsight.

The technology worked. The failure still happened.

Why. Because knowing earlier does not automatically mean acting earlier. Alerts arrived during shutdowns, night shifts, holiday periods, or peak production. The team saw them. The team agreed they mattered. The team did not have the capacity to respond.

This is not negligence. It is reality.

Reliability engineers operate in a world of trade-offs. Every intervention competes with production targets, safety constraints, staffing limits, and spare parts availability. Predictive maintenance did not remove those constraints. It simply made them visible sooner.

In some cases, that visibility was uncomfortable. The system warned of a problem. Nothing was done. The failure occurred. The post-mortem felt awkwardly short.

“Yes, the alert was there.”

Silence.

Truth #2: Alert Fatigue Is Real, but Action Fatigue Is Worse

Alert fatigue gets a lot of attention. Too many notifications. Too little signal. Engineers tuning alarms until they stop firing altogether.

In 2025, a more subtle problem emerged. Action fatigue.

Even when alerts were accurate, clear, and timely, teams struggled to act consistently. Each alert demanded judgement. Is this urgent. Can it wait. Who should look at it. What if we are wrong.

That decision-making overhead adds up. Especially when resources are tight.

The most effective predictive maintenance programmes recognised this early. They did not try to eliminate alerts entirely. They worked to reduce the number of decisions required per alert.

Clear ownership helped. So did recommended actions, confidence indicators, and examples of what had worked before. Anything that shortened the gap between “something looks wrong” and “this is what we usually do” reduced fatigue.

Reliability engineers do not fear work. They fear unnecessary thinking under pressure. Systems that respect that reality get used. Systems that ignore it get muted.

Truth #3: Data Quality Matters Less Than Decision Ownership

This one surprised some people.

For years, data quality was treated as the gating factor for predictive maintenance success. Clean data. High resolution. Long histories. Perfect baselines.

In practice, many successful programmes operated with data that was merely good enough. What they had instead was clarity.

Someone owned the asset. Someone owned the alert. Someone owned the decision.

When ownership is clear, imperfect data can still drive action. When ownership is unclear, perfect data sits idle.

In 2025, reliability leaders increasingly focused on defining decision rights rather than chasing marginal data improvements. Who decides whether an alert becomes work. Who closes the loop. Who captures the outcome.

Once those questions were answered, the system improved quickly. Not because the models were smarter, but because the organisation was.

This does not excuse poor data practices. It reframes priorities. You cannot analyse your way out of unclear accountability.

Truth #4: Technology Cannot Fix Poor Design or Bad Operating Practices

Predictive maintenance has a cruel honesty about it. It will tell you when something is wrong. It will also keep telling you if the underlying cause is never addressed.

In 2025, many teams learned this lesson through repetition. The same assets triggered alerts again and again. Bearings overheated. Gearboxes vibrated. Fans loosened.

The temptation was to tune the model. Reduce sensitivity. Suppress alerts.

The better response was harder. Fix the design. Change the operating practice. Eliminate the defect.

Predictive maintenance does not replace reliability engineering fundamentals. It exposes where they are missing. Assets operated outside their design envelope will continue to degrade predictably. The system will notice. Repeatedly.

There is a certain dark humour in this. The software keeps saying, politely and persistently, “This is still a problem.” Eventually, the message sinks in.

Truth #5: Culture Is Rarely the Problem People Think It Is

When predictive maintenance struggles, culture is often blamed. “The team doesn’t trust the system.” “Maintenance is resistant to change.” “Operators don’t engage.”

In 2025, a more nuanced understanding emerged.

Most frontline teams are not resistant to technology. They are resistant to noise, extra admin, and unclear expectations. When a system adds work without removing any, scepticism is rational.

Successful programmes earned trust by being useful early. Fewer false positives. Clear explanations. Visible wins. When an alert led to a planned intervention that avoided a breakdown, belief followed naturally.

Culture did not change because someone asked nicely. It changed because the system proved it respected the team’s time and judgement.

There is nothing mystical about that.

Truth #6: Workforce Constraints Are Now a Design Input, Not an Excuse

Labour shortages, skills gaps, and retirements were not new in 2025. What changed was how openly they were acknowledged.

Reliability leaders stopped designing systems for ideal staffing levels. They designed for reality. Fewer people. Less experience. More turnover.

Predictive maintenance that assumed deep vibration expertise on every shift struggled. Systems that embedded knowledge, context, and guidance scaled far better.

This was not about dumbing things down. It was about making expertise reusable. Capturing what experienced engineers knew and making it available to those who came next.

In a strange way, predictive maintenance became a workforce strategy as much as a technical one.

Truth #7: Analysis Without Feedback Is a Dead End

One of the quieter lessons of 2025 was the importance of feedback loops.

Alerts without outcomes do not improve systems. Recommendations without confirmation do not get smarter. Programmes without learning stagnate.

The best teams closed the loop relentlessly. Every alert that led to work was tagged. Every false positive was noted. Every avoided failure was discussed.

This did not require perfection. It required discipline.

Over time, the benefits compounded. Confidence increased. Noise decreased. The system began to reflect the site’s reality rather than an abstract model.

Predictive maintenance stopped being something that happened to the team. It became something the team actively shaped.

The Uncomfortable Pattern Behind These Truths

If there is a common thread running through all these lessons, it is this. Predictive maintenance did not fail teams in 2025. It revealed them.

It showed where decision-making was slow. Where ownership was unclear. Where work management was fragile. Where design flaws persisted.

That can feel confronting. It can also be incredibly useful.

Sites that embraced this mirror effect improved rapidly. Sites that tried to mute it learned less.

There is professional humour in that too. The software does not care about politics. It just keeps reporting what it sees.

Why These Lessons Matter Going Forward

These truths are not reasons to abandon predictive maintenance. They are reasons to approach it differently.

By 2025, the most effective reliability leaders stopped asking whether predictive maintenance was worth it. They asked whether their organisation was ready to use it properly.

That question changes everything.

In the final part, we will look forward. Not to hype-filled futures, but to practical guidance. What reliability leaders should look for. How to evaluate predictive maintenance without getting burned. And where this discipline is genuinely heading next, once the noise settles.

Because if 2025 taught us anything, it is this. Predictive maintenance is no longer a technology problem.

It is a leadership one.

Predictive Maintenance in 2025: What Reliability Leaders Should Do Next (And What to Stop Doing)

By the time reliability leaders reach Part 4 of this conversation, most are no longer asking whether predictive maintenance works. That question belongs to an earlier decade. In 2025, the more relevant question is simpler, sharper, and harder.

“What should we actually do differently now?”

Predictive maintenance has matured enough that small choices have outsized consequences. The wrong pilot design can stall momentum for years. The wrong success metric can quietly kill a programme that is technically sound. And the wrong expectations can turn a useful system into yet another dashboard that everyone politely ignores.

This final part is about practical judgement. Not theory. Not vendor promises. Just grounded guidance for reliability leaders who want predictive maintenance to survive contact with reality.

How to Evaluate Predictive Maintenance in 2025 Without Getting Burned

If there is one mistake reliability leaders still make, it is evaluating predictive maintenance as a feature set rather than a behaviour change.

In 2025, the most useful evaluation questions are operational, not technical.

How quickly does the system lead to a decision that someone is confident acting on.
How much interpretation is required before work can be planned.
How often does it reduce work, rather than create it.

Accuracy still matters, of course. But accuracy without action is just a very precise way to stay busy.

Look closely at how alerts are framed. Are they contextual. Do they include likely causes. Do they suggest next steps. Or do they simply state that “something is abnormal” and leave the hard thinking to an already stretched team.

Reliability engineers are not afraid of complexity. They are allergic to ambiguity.

Another useful test is behavioural. Ask to see unresolved alerts. Not the success stories. The awkward ones. The alerts that sat open for weeks. Why. What got in the way. The answers will tell you far more about real-world fit than any demo.

Predictive Maintenance Lives or Dies in the Workflow

One of the clearest lessons of 2025 is that predictive maintenance must fit existing workflows, not attempt to replace them overnight.

If alerts live outside the systems people already use, friction increases. If acting on insights requires duplicate data entry, momentum slows. If the connection between detection and work execution is unclear, trust erodes.

The most successful programmes did not demand wholesale change. They made small, deliberate adjustments. Clear escalation paths. Simple ownership rules. Lightweight feedback loops.

Think less about transformation and more about alignment.

And yes, this is where many initiatives quietly fail. The system works. The workflow does not. The software gets blamed. The real issue stays untouched.

How to Design a Predictive Maintenance Pilot That Actually Converts

Pilots deserve their own section because they remain one of the most misunderstood parts of predictive maintenance.

In 2025, the goal of a pilot is not to prove that the technology works. That question is largely settled. The goal is to prove that your organisation can use early warnings to change outcomes.

That requires restraint.

Choose fewer assets. Assets with known failure modes. Assets where intervention is possible without heroics. Avoid the temptation to monitor everything critical at once.

Define success in behavioural terms. How many alerts led to planned work. How much unplanned work was avoided. How confident did the team feel acting on the insights.

And be honest about failure. If alerts are ignored because there is no time, that is not a technology failure. It is valuable information. Treat it as such.

A pilot that fails quickly and clearly is far cheaper than one that limps along for a year producing beautiful charts and zero change.

Stop Expecting Predictive Maintenance to Be Autonomous

This is worth saying plainly.

Fully autonomous maintenance remains more aspiration than reality. And that is fine.

In 2025, the most effective predictive maintenance systems did not try to remove humans from the loop. They tried to make the human loop tighter, calmer, and more consistent.

They supported judgement rather than replacing it. They reduced the number of decisions required. They captured outcomes so the system improved over time.

Expecting predictive maintenance to run itself is like expecting a torque wrench to install the motor. Useful tool. Still needs a hand.

Reliability leaders who embraced this mindset avoided disappointment. Those who did not often spent a year waiting for a future that never quite arrived.

Where Predictive Maintenance Is Actually Heading Next

Strip away the hype and a few clear directions emerge.

Decision support will continue to improve. Systems will get better at explaining why something matters and what usually works. Not because engineers need less intelligence, but because they need less friction.

Integration with work management will deepen. Not flashy integration. Practical integration. Alerts that naturally become work. Outcomes that feed back into models and standards.

Handling variability will improve. Assets that change speed, load, or product frequently will become easier to monitor meaningfully. This alone will unlock value in many sites that struggled with false positives in the past.

There will also be continued noise. New acronyms. New promises. New “revolutions”. Reliability leaders will need filters, not excitement.

The good news is that those filters are getting sharper.

Predictive Maintenance and the Sustainability Conversation

One subtle shift in 2025 is how predictive maintenance is being reframed through a lifecycle and sustainability lens.

Less wasted maintenance. Longer component life. Fewer emergency interventions. More stable operation.

These outcomes align naturally with sustainability goals, without needing to overstate the case. Predictive maintenance does not save the planet on its own. It does help organisations use assets more responsibly.

This framing matters because it connects reliability to broader business conversations. When done honestly, it strengthens the case for investment without resorting to buzzwords.

Engineers tend to appreciate that.

What to Stop Doing Immediately

A short but important list.

Stop evaluating predictive maintenance based on how impressive the dashboard looks.
Stop assuming more data will fix unclear ownership.
Stop designing pilots to avoid uncomfortable truths.
Stop blaming culture when the system adds noise.

None of these habits are malicious. They are understandable. They are also expensive.

The Final Test: What Predictive Maintenance Really Measures

Here is the quiet conclusion that many reliability leaders reached in 2025.

Predictive maintenance is not just a way to detect failure earlier. It is a way to measure organisational maturity.

It reveals how decisions are made. How work is prioritised. How learning happens. How comfortable a team is with acting under uncertainty.

Strong systems get stronger. Weak systems get exposed.

That is not a reason to avoid predictive maintenance. It is a reason to approach it with eyes open.

Used well, predictive maintenance reduces downtime, removes unnecessary work, and makes reliability calmer rather than more frantic. Used poorly, it produces noise, frustration, and another round of “this sounded good at the time”.

The difference is not the algorithm.

It is leadership.

And in 2025, that truth finally stopped being optional.