Predictive maintenance (PdM) is no longer an experiment. Across sectors—facilities, healthcare, fleets, networks, and critical infrastructure—you’re connecting equipment, collecting condition data, and using models to flag issues before something fails. 

In part 2, we looked under the hood at the gears and rotors that make that possible: sensors, machine learning, and the connectivity layer that keeps data moving. 

But once the tech engine is in place, a different problem shows up: the workload doesn’t always change. Alerts increase, but decisions don’t get easier. Schedules stay time-based. Scaling beyond a pilot feels heavy. 

That’s also where ROI gets foggy. If insights don’t reliably translate into planning, action, and measurable outcomes, predictive maintenance stays stuck in “pilot purgatory” territory. 

One practical way to think about the next step is intelligent maintenance (prescriptive maintenance)—not as a new buzzword, but as the point where PdM becomes repeatable at scale, with clearer decisions and clearer impact. 

Here are five signs you’re ready to make that shift—and where to focus so the tech you’ve built actually pays off. 

1) Predictive maintenance is working, but planning hasn’t changed 

Early on, it’s common to treat predictive maintenance as an add-on. You keep preventive schedules and layer predictions on top. 

Over time, you may notice: 

• Assets still come down “just in case,” even when condition data looks fine 
• Predicted issues trigger extra inspections, but planned work doesn’t change much 
• You’re still doing a lot of miles-on-the-clock maintenance because changing the plan feels risky 

In healthcare, imaging systems or lab analyzers might be monitored for early signs of failure, but the calendar still dictates when equipment goes offline—regardless of actual condition or patient demand. 

If predictive maintenance isn’t reshaping when you perform work and what you prioritize, you’re leaving value on the table. 

Intelligent maintenance treats predictive signals as planning inputs. Service intervals stretch or contract based on condition. Some tasks disappear. Others move earlier. 

2) OT and IT data are still miles apart 

Most teams rely on a mix of OT systems (SCADA, historians, PLCs, IoT platforms, building management systems, device logs) and IT systems (EAM/CMMS, ERP, ticketing, inventory—plus EHR-adjacent systems in healthcare). 

If getting a complete asset picture means exporting files from several systems and stitching together a spreadsheet, you’ve reached another limit. 

Typical symptoms: 

• Model input data lives in one place; work history lives somewhere else 
• Root cause analysis takes days because the right logs or trends are hard to find 
• Integrations are one-offs that break when systems are upgraded 

Intelligent maintenance assumes OT and IT data can move reliably and at the right speed. That doesn’t require perfection, but it does require: 

• A clear home for maintenance-relevant data 
• Basic standards (naming, units, time alignment) 
• A way to expose data without custom plumbing every time 

If your team spends more time chasing data than using it, you’re ready for the next step. 

3) Technicians see more alerts, but not clearer actions 

A common early win in predictive maintenance is better detection. You spot problems earlier and more often. 

The risk is you end up with: 

• Dashboards full of red and amber indicators 
• Alerts that all look equally urgent 
• Messages that say “anomaly detected” without explaining impact or next steps 

When that happens, technicians and planners have to translate every signal into a decision: 

• Can this wait until the next planned window? 
• Do we act now? 
• What part, procedure, or skill set is needed? 

In healthcare and diagnostics, that interpretation step can also carry clinical impact: equipment downtime doesn’t just affect productivity—it can delay scans or test results. 

Intelligent maintenance focuses on guidance, not just detection. For each signal, the system aims to answer: 

• How serious is this, given asset criticality and service/process impact? 
• What action makes sense next? 
• Where should it sit in today’s queue? 

If the team is spending more time interpreting alerts than acting on them, you’ve outgrown phase one. 

4) You can’t easily show the impact of your maintenance program 

Most teams feel the difference when predictive maintenance starts working. But when finance or leadership asks, “What did this save us this year?” the answer often involves a manual deck and rough estimates. 

If you can’t measure uptime gains, cost reductions, or resource efficiency in a consistent way, it’s hard to defend expansion—even when teams feel the improvement day to day 

Intelligent maintenance treats value tracking as part of the work: 

• Agree on a small set of metrics (unplanned downtime/outages, MTBF, maintenance cost per asset, first-time fix rate) 
• Capture those metrics in the same systems used to manage work 
• Use simple views that connect changes in those metrics to specific actions and decisions 

In healthcare, you might also track operational knock-ons like delayed procedures, rescheduling volume, or reliance on backup equipment. 

If the best proof you have is “things feel better,” you’re ready for a more structured approach. 

5) Scaling beyond a pilot feels harder than starting it 

A pilot often focuses on a handful of high-value assets. You connect data, train a model, and prove it works. 

Then you try to scale and discover: 

• Each new asset requires custom data work and custom models 
• There’s no consistent way to reuse patterns across sites 
• You don’t have enough people who understand both the equipment and the analytics 
• Governance questions keep popping up: who owns the model, who approves changes, who signs off on automation? 

If rolling out to more assets, sites, hospitals, or locations feels like starting over each time, you need to treat scale as a design requirement—not an outcome you hope happens later. 

What changes when you move toward intelligent maintenance 

Moving from predictive to intelligent maintenance doesn’t mean throwing away what you’ve built. It means tightening the loop from data to decision. 

Day to day, that looks like: 

• Cleaner access to relevant data: people can pull up history, logs, and trends without hunting across systems 
• Analytics that fit existing workflows: recommendations show up where work is scheduled and tracked 
• Clear ownership: everyone knows who maintains data, models, and response workflows—and how feedback improves them 
• Incremental automation: start small (low-risk inspections, smarter prioritization) and increase automation only as confidence grows 

It’s less about “smarter tools” and more about better coordination between data, people, and processes. 

Simple ways to start moving in that direction 

If some of these signs sound familiar, you don’t need a reset. You can move toward intelligent maintenance with targeted changes: 

1. Close the loop on one high-value asset. Map the path from signal → decision → action → recorded outcome. Fix the weak spots. 
2. Standardize one OT–IT handoff. Pick a recurring data pain point (like event logs into CMMS) and make that flow reliable.
3. Change one planning rule based on condition. Adjust an interval or trigger based on actual condition and track what happens. 
4. Track two metrics rigorously. Start with downtime/outages and maintenance cost per asset (or the equivalents that matter in your environment). 
5. Collect technician feedback on alerts. Use it to refine thresholds, wording, and recommended actions. 

Small improvements compound—especially when they reduce interpretation time and make planning easier. 

Want a deeper roadmap? 

Download the Intelligent Maintenance whitepaper to see how teams are structuring the shift from predictive to intelligent maintenance—across data foundations, workflows, and scale. 

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