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Today, we zoom in on triaging as one of the most overlooked and under-optimized processes in the maintenance value chain.
Asset-intensive organizations lose millions annually to slow or inaccurate triaging of alarms, work requests, and sensor events. To change that, two levers matter more than anything else: Speed and Precision. AI can help teams supercharge both, enabling the transition from reactive firefighting to Autonomous Maintenance
What was once a bottleneck can now become a strategic advantage, shifting triaging from a pain point to a performance multiplier. Let's break down why the industry lags today, what’s at stake, and how AI-powered triaging is helping early adopters unlock peak asset performance, reliability, and efficiency at scale.
Despite widespread adoption of IoT and connected assets, the way most operations teams handle triaging hasn’t evolved much.
Workflows are still built around manually reviewing events — often in spreadsheets or basic CMMS dashboards. Contextual data like asset hierarchy, duty cycles, and recent work orders is scattered, incomplete, or missing altogether. That leaves technicians guessing, jumping between systems, and reacting late.
Here’s what the gap between typical and world-class triage looks like in numbers:
This isn’t just a technology gap. It’s a decision-making gap. Teams are drowning in noise, starved of insight, and burning precious hours trying to separate what’s urgent from what’s not. Without better triaging tools and processes, even the best analytics and dashboards can’t deliver real impact.
AI has the potential to flip this script. But first, we need to understand why the system is stuck.
If the data is available and the stakes are high, why hasn’t triage evolved? The truth is, most operations and maintenance teams aren’t held back by intent, but by infrastructure, inertia, and fragmentation. Here are six structural barriers that continue to slow progress:
1️⃣ Data Tsunami: Sensor density has doubled in the past five years. But human bandwidth hasn’t. With hundreds or thousands of signals flooding in daily, teams simply can’t keep up, especially with manual triaging.
2️⃣ Context Deficit: Most teams don’t have complete or connected asset information models. Without context, like operational modes, historical interventions, or known quirks, rule-based systems fail, and teams are forced to dig.
3️⃣ Legacy Tools: CMMS and EAM platforms were designed for record-keeping, not real-time analytics. They don’t surface patterns, prioritize intelligently, or adapt to operational dynamics, making them poorly suited for modern triage.
4️⃣ Skill Scarcity: With nearly 30% of senior reliability engineers expected to retire by 2030, tribal knowledge is walking out the door. Newer staff often lack the experience to make confident, high-stakes calls.
5️⃣ Incentive Misalignment: Operators care about uptime. Vendors often benefit from extended billable hours. In this environment, fast triage doesn’t always align with the commercial model — so slow decisions persist.
6️⃣ Risk Aversion: IT/OT constraints, cybersecurity concerns, and siloed systems make it difficult to deploy cloud-based AI at scale, even when the business case is strong.
Bottom line? Traditional approaches can’t scale because they weren’t built for the complexity, velocity, or expectations of today’s asset environments. Triaging has outgrown the human-only model. And that’s where AI comes in.
Triage may not be the most visible part of your maintenance workflow, but it’s easily one of the most expensive when done wrong. Every delay, false alarm, or missed anomaly creates downstream consequences that quietly drain budgets and degrade performance.
Here’s how it adds up:
💸 Unplanned Downtime: For a 1 MW HVAC chiller plant, just one hour of downtime can cost up to $12,000 in comfort penalties and energy ramp-up.
⚡Energy Inefficiency: Roughly 15% of motor-related alarms point to faults that silently drive up energy consumption by 5–10% before a failure actually occurs.
🛠️ Inflated Spare Parts Costs: False positives lead to premature replacements. This drives up spares inventory and parts costs by 8–12%, year over year.
🧾 Compliance & Safety Risk: Delayed triage of critical alarms raises the risk of environmental fines, regulatory penalties, and safety incidents, especially in high-risk sites.
🧮 The Big Picture: Across the global FM industry, triage inefficiencies contribute to an estimated $80B–$120B in avoidable OPEX and lost productivity every year.
The real breakthrough in triaging isn’t just about adding automation. It’s about multiplying what teams can do through speed and precision, simultaneously. With the right AI architecture, this isn’t theoretical. It’s happening.
🚀 Speed at Scale: Modern AI systems can ingest and classify thousands of events per second. Through vectorized similarity search and LLM-based summarization, they route only high-impact anomalies for review, reducing alert queues by up to 90%.
🎯 Precision with Context: Self-learning asset knowledge graphs drastically reduce false alarms as context matures. Meanwhile, predictive models fuse sensor data, power usage, and historical work orders, transforming noisy inputs into reliable foresight.
When both speed and precision go up, triage stops being a bottleneck and becomes a performance multiplier.
If you're serious about moving toward Autonomous Maintenance, triage performance can’t remain a black box. It’s time to measure what matters. Here are four high-impact metrics every reliability or operations team should be tracking:
⏱️ Mean Time to Detect (MTTD): How quickly can your system recognize an actual issue after it begins? Lower MTTD = earlier intervention = less damage and downtime.
⚡ Alert-to-Action Time (A2A): How long does it take to move from detecting an issue to taking the first corrective step? A short A2A time reflects streamlined workflows and empowered decision-making.
🤖 Percentage of Autonomous Resolutions: What portion of issues are resolved without manual triage or decision support? This is your clearest signal that autonomy is actually working.
💰 Maintenance Cost per Runtime Hour: Instead of viewing maintenance as a fixed cost, measure it in relation to asset performance. As triaging improves, this number should drop steadily.
Triaging doesn’t need to be the weak link in your maintenance chain anymore. With AI, speed and precision are no longer trade-offs. You can have both, and organizations are already seeing results:
But change doesn’t need to start big. Begin by evaluating your current triage pipeline. Where are the delays? Where’s the noise? What’s being missed?
A focused 90-day pilot with an Autonomous Maintenance platform like Xempla can surface quick wins and prove ROI early, fuelling a more confident transition to full autonomy.
Autonomous triaging is no longer a future state. It’s a strategic lever for performance, sustainability, and competitive edge.
The window to capture that advantage is now.
Let's talk.
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