Predictive maintenance (PdM) promises to transform reactive firefighting into proactive intervention, but many enterprise facility teams struggle to move beyond pilot paralysis, the months spent debating which assets to monitor, endless discussions about sensor integration, and teams questioning whether prediction accuracy will justify implementation costs before launching a single pilot.

The organizations getting PdM right start small with focused pilots designed to deliver measurable results quickly. They select assets with clear failure patterns using centralized data, deploy targeted monitoring through flexible integrations, and establish success criteria that prove business value before scaling.

Key takeaways

  • Start with centralized asset data to identify the right pilot candidates: Use unified platforms to quickly segment assets by failure history, business impact, and criticality instead of manually analyzing years of spreadsheets
  • Track business outcomes that leadership understands: Target measurable downtime reduction and maintenance cost savings through metrics like prevented downtime hours, emergency repair cost reduction, and improved asset uptime
  • Plan for portfolio-wide scaling from day one: Choose pilot assets with large installed bases across your facilities. Success with one chiller means you can replicate the approach across all your HVAC systems

Most pilots fail not from technical limitations but from poor execution fundamentals. Understanding what separates successful pilots from stalled initiatives starts with recognizing why teams struggle in the first place.

Why some predictive maintenance pilots can fail

Many facility teams approach PdM pilots with enthusiasm and budget but without the operational framework that separates successful programs from expensive disappointments.

The most common failure patterns include:

  • Selecting too many diverse assets across multiple locations
  • Deploying sensors without clear integration into work management workflows
  • Defining success as “proving the technology works” instead of measurable business impact

Another important point is remembering that digital transformation only succeeds when teams understand that technology alone isn’t enough.

“You can invest a lot of money in digital transformation initiatives, but if you don’t bring people along on that journey… you’re setting yourself up for failure,” explains Kelly Kinghorn, Senior Partner and Digital Technology Practice Lead at ReVisionz, Inc., on a recent episode of the Asset Champion podcast.

Successful teams leverage unified asset management platforms to identify a single asset class with painful failure patterns at one representative location, integrate targeted monitoring that detects those specific failure modes, and run focused pilots with clear financial success metrics tracked through real-time dashboards.

Only after proving value at scale do they expand to additional asset types and sites.

How to select the right assets for your first predictive maintenance pilot using centralized data

Asset selection drives everything else in your pilot design. The right choice creates compelling proof points that accelerate scaling. Wrong choices create skepticism that stalls programs regardless of technical success.

Unified platforms that consolidate asset history, criticality rankings, and performance metrics dramatically simplify this identification process.

Failure frequency and business impact

Look for equipment that fails often enough to generate meaningful data during your pilot timeframe but not so frequently that replacement makes more sense than prediction. Assets with quarterly failures creating operational disruptions or safety concerns provide sufficient signal for validation while justifying the monitoring investment.

Platforms with centralized asset repositories let you quickly segment assets by failure history and criticality rather than manually searching through years of work orders across multiple systems.

Detectable failure modes with lead time

The asset must show measurable changes before catastrophic failure. Pumps developing cavitation, motors showing bearing degradation, compressors experiencing valve wear—these failure modes produce vibration, temperature, or pressure signals days or weeks before complete breakdown.

The P-F curve, a foundational concept in reliability engineering, illustrates this detection window between when potential failure becomes detectable and when functional failure occurs. Avoid assets with sudden, unpredictable failures, for example certain electrical components and certain control systems, that offer minimal warning regardless of monitoring sophistication.

High consequence of failure

Focus on equipment where failures create safety risks, stop critical operations, or generate expensive emergency repairs. A failed chiller serving your data center creates a different level of urgency than a failed bathroom exhaust fan.

Quantify the hourly cost of downtime, typical emergency repair expenses, and any safety or compliance implications using historical work order cost data.

Maintenance history and institutional knowledge

Your team already knows which assets cause the most problems. Interview technicians who respond to breakdowns, review recent failure reports in your maintenance management system, and identify equipment mentioned repeatedly in shift handoff notes.

Institutional knowledge often reveals patterns that formal data analysis misses, particularly around partial failures, intermittent issues, or degrading performance that hasn’t yet triggered complete breakdown.

Sufficient installed base for scaling

While your pilot might monitor one asset, consider whether you have 20, 50, or 100 similar units across your portfolio. Proving PdM value on unique equipment doesn’t support scaling arguments. Focus on asset classes you can replicate across multiple sites once pilot success is demonstrated through unified reporting systems.

How to measure if your PdM pilot is working

Technical metrics matter less than business outcomes when justifying program expansion to leadership. Your pilot needs to demonstrate measurable improvements in operational performance and maintenance efficiency through dashboards that stakeholders can access and understand.

Track these financial outcomes to build your business case:

  • Prevented downtime hours: Compare actual downtime during the pilot against historical baseline for similar equipment. Most organizations target 30–50% downtime reduction, according to research from Deloitte. For critical manufacturing lines or building systems, preventing even one 8-hour unplanned outage can justify annual platform costs
  • Emergency repair cost reduction: According to Equipment Reliability Institute data, emergency repairs cost 3–5x more than scheduled work due to premium labor rates, expedited parts, and operational disruption. Track emergency work order frequency and costs against baseline periods, using real-time dashboards to communicate progress
  • Improved asset uptime and reliability: Monitor equipment availability, reduced mean time between failures, and improved overall equipment effectiveness (OEE). Manufacturing industry benchmarks show world-class facilities achieve OEE scores above 85%, with predictive maintenance as a key enabler. A gas field specialist used usage-based triggers to improve regulatory compliance while reducing unplanned inspection risks
  • Maintenance cost savings: Track total spending per asset (labor, parts, contractor costs) as budgets shift from reactive emergency work to planned interventions. The Uptime Institute reports PdM programs typically reduce overall maintenance costs by 25-30% while extending asset lifespans

These metrics transform pilot success from technical achievement into business outcomes that secure scaling budgets.

From pilot success to portfolio-wide predictive maintenance

The path from pilot to portfolio is straightforward when you start focused and prove value through measurable outcomes.

Ready to design a predictive maintenance pilot that delivers measurable results before committing to enterprise-wide deployment? Our team has helped organizations across manufacturing, healthcare, government, and commercial real estate launch successful PdM programs that scale.

Ready to discuss asset selection strategies, integration approaches, and success criteria aligned with your facility portfolio and operational priorities? Schedule a consultation with an Eptura expert.

Frequently asked questions

  • How do I choose between a frequently failing asset versus a high-cost critical asset for my pilot?

    Choose the asset that delivers the highest total cost impact—failure frequency multiplied by failure cost. A frequently failing HVAC chiller makes a better pilot than a rarely failing backup generator because frequent failures provide more validation data during your pilot timeframe and create more visible pain points that build stakeholder support. However, if you have a critical asset with very high failure costs, even annual failures justify monitoring, especially when downtime affects safety or regulatory compliance. 

  • What if we don't have historical failure data to identify pilot candidates?

    Start by interviewing your maintenance technicians and facility managers—they know which assets cause the most problems even if formal documentation is incomplete. Ask which equipment generates the most emergency calls, requires the most frequent repairs, or gets mentioned repeatedly in shift handoff notes. This institutional knowledge often reveals better pilot candidates than incomplete CMMS data. 

  • How long should I run my pilot before deciding whether to scale?

    The key milestone isn’t calendar duration but accumulating enough validated early warnings to prove your monitoring approach works and calculate reliable cost avoidance metrics. Throughout the pilot, track financial outcomes through real-time dashboards. If you’re consistently preventing failures and documenting cost savings after multiple successful interventions, you have sufficient proof points to build your scaling business case regardless of elapsed time. 

  • How do we secure leadership buy-in for PdM investment when budgets are tight?

    Build your business case around prevented downtime costs rather than monitoring system features. Calculate the annual failure costs for your proposed pilot asset using historical work order data—emergency repair expenses, overtime labor, expedited parts shipping, and operational disruption. Present PdM as insurance that costs less than a single prevented failure. Manufacturing assets with significant hourly downtime costs can justify substantial annual platform and monitoring investments by preventing even one major outage. 

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As a content creator at Eptura, Jonathan Davis covers asset management, maintenance software, and SaaS solutions, delivering thought leadership with actionable insights across industries such as fleet, manufacturing, healthcare, and hospitality. Jonathan’s writing focuses on topics to help enterprises optimize their operations, including building lifecycle management, digital twins, BIM for facility management, and preventive and predictive maintenance strategies. With a master's degree in journalism and a diverse background that includes writing textbooks, editing video game dialogue, and teaching English as a foreign language, Jonathan brings a versatile perspective to his content creation.