AI + Human Expertise: The Ultimate Weapon Against Unplanned Downtime

Predictive maintenance (PdM) has long promised to revolutionize manufacturing by using sensors, data, and AI-driven insights to eliminate unplanned downtime. Yet for many organizations, the reality falls short. Complexity, limited vendor support, and the challenges of turning raw data into answers often turn PdM from a strategic advantage into a daily frustration. 

PdM success can be achieved by pairing AI with dedicated human expertise. When experienced analysts work alongside AI systems, they bring context, prioritization, and guidance that transform data into decisions. Today, that expertise doesn’t have to live within the plant—some solutions now include dedicated support from condition monitoring specialists who serve as an extension of the maintenance team. This balance between technology and human insight restores trust, reduces downtime, and turns PdM into a true competitive advantage. 

When Predictive Maintenance Fails to Deliver 

Some PdM platforms overwhelm teams with data while providing little guidance on what truly matters. Dashboards can be filled with charts and alerts that lack context or prioritization, and when internal experts are stretched thin or unavailable, maintenance teams are left to make sense of the information on their own. 

As a result, facilities often experience: 

  • Data overload with little operational context 

  • Uncertainty about which issues deserve attention first 

  • Limited access to knowledgeable support when questions arise 

  • Alert fatigue that overwhelms lean teams 

When PdM programs add more complexity than value, trust in the system begins to erode. Teams start to ignore alerts, reverting to reactive maintenance simply because it feels more manageable. Instead of driving continuous improvement, PdM becomes another system that overpromises and underdelivers, leaving maintenance teams burnt out and no closer to preventing unplanned downtime. 

Alert Fatigue: Draining Your Team and Your ROI 

Alert fatigue is one of the most common reasons why people struggle to see results from their PdM program. When teams are flooded with notifications (many of them being false alarms or low-priority issues), they quickly become desensitized. Over time, even legitimate warnings start to lose their urgency. 

When maintenance teams are constantly interrupted by noncritical notifications, important signals get buried in the noise. Critical issues slip through the cracks, and early intervention windows are missed. The result is more downtime, higher repair costs, and a growing distrust in the system that was meant to prevent both. 

How Alert Fatigue Happens

AI System Generates Alerts → 
Too Many Alerts (False Positives, Low Priority) → 
Teams Overwhelmed by Volume → 
Alerts Start Being Ignored → 
Critical Warnings Missed → 
Unplanned Downtime → 
Loss of Trust in PdM System 

Beyond lost efficiency, alert fatigue takes a personal toll. Maintenance professionals end up spending long hours reacting to problems that may not exist. The constant state of firefighting increases stress, lowers morale, and reduces overall productivity. At this point, PdM isn’t failing because the technology doesn’t work—it’s failing because the people using it are overwhelmed and undersupported. 

What AI Can and Can’t Do by Itself 

Artificial intelligence has become the cornerstone of modern PdM. It can process vast amounts of machine data, uncover complex patterns that traditional monitoring can’t detect, and continuously learn from billions of operating hours. For many facilities, AI has been a game changer in detecting early signs of wear, imbalance, or lubrication issues that once went unnoticed.  

Yet even with all its strengths, AI has its limits. Data alone cannot tell the full story of an asset’s health or the nuances of its operating environment. Even the most advanced algorithms can misinterpret signals, generate false alarms, or overlook developing problems. That’s why successful PdM programs don’t rely on AI in isolation. AI is expertly trained and guided, with condition monitoring engineers validating insights, helping prioritize actions, and ensuring the technology delivers real value on the plant floor. 

Where AI Excels 

Where It Falls Short 

Assists with 24/7 monitoring of assets 

Lacks context of operations and environment 

Flags potential risks early 

Cannot interpret production schedules or asset history 

Refines models over time to improve accuracy 

May misclassify temporary anomalies as serious issues 

Detects subtle correlations humans might miss 

Can overlook warning signs that experts would recognize 

Helps maintenance teams stay ahead of unexpected failures 

Generates unnecessary work orders or misses critical failures without proper oversight 

PdM can’t thrive on algorithms alone—it needs people who understand the machines, the environment, and the story behind the data. 

Human Expertise: The Heart of Predictive Maintenance 

The real value of PdM is realized when advanced AI insights are guided by human expertise. A dedicated Condition Monitoring Engineer (CME) serves as an extension of the reliability team, combining knowledge of both technology and the realities of plant operations. By continuously monitoring live machine data, the CME validates AI-generated alerts, filters out false positives, and highlights the issues that truly matter. Beyond validation, they interpret data in context—considering machine history, operating environment, and asset criticality—to deliver prescriptive recommendations that guide maintenance teams from diagnosis to resolution. 

This collaboration bridges the gap between insight and action, enabling faster adoption, more predictable work schedules, and fewer unnecessary interventions. As teams gain confidence and control, they naturally shift from constant reaction to continuous improvement, driving higher uptime, smoother production flow, and a stronger reliability culture. 

A group of men wearing headsetsDescription automatically generated

Example of collaboration between maintenance staff and CME.  

For Arbec, the expertise and partnership from their condition monitoring engineer (CME) at AssetWatch has been critical to success. 

 

“Our CME is a partner, not just support. Kevin is quick to respond, and his analysis is essential. Without the right action on the right equipment at the right time, you’re just wasting effort.” 

- Stéphane Roberge, VP of Asset Management and Continuous Improvement, Arbec 

 

Thanks to support from their CME, Arbec’s maintenance team quickly shifted from reactive maintenance to strategic foresight. In just six months, they prevented 15 equipment failures and unlocked new levels of confidence, efficiency, and cost savings across their operation.  

Technology enables reliability, but people make it possible. Even the most advanced PdM system depends on skilled professionals who understand the equipment, the process, and the pressures of production.  

Turning Predictive Maintenance Into a Value Driver 

When PdM is powered by AI alone, it often creates more questions than answers. But when AI and human expertise work hand in hand, PdM becomes a catalyst for cultural change, operational excellence, and lasting reliability. By combining automated data analysis with expert interpretation, maintenance teams act earlier, plan smarter, and prevent failures that once seemed inevitable. PdM was never meant to replace people—it was meant to empower them.  

Learn how pairing AI with dedicated human expertise can help your team eliminate unplanned downtime and turn PdM into a true competitive advantage without the heavy lifting. Schedule your consultation with AssetWatch.