{"id":196540,"date":"2025-08-05T17:05:11","date_gmt":"2025-08-05T21:05:11","guid":{"rendered":"https:\/\/fiixdev.wpengine.com\/?p=196540"},"modified":"2026-03-11T12:03:45","modified_gmt":"2026-03-11T16:03:45","slug":"predictive-maintenance-is-not-always-ai","status":"publish","type":"post","link":"https:\/\/fiixsoftware.com\/blog\/predictive-maintenance-is-not-always-ai\/","title":{"rendered":"Predictive Maintenance Isn\u2019t Always AI and Here\u2019s Why That Matters"},"content":{"rendered":"
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AI driven predictive maintenance (PdM)<\/a> (opens in new tab)<\/span> has become a buzzword in the maintenance world, but not all PdM is created equal. While AI-driven PdM has set a new standard for performance, many solutions on the market today still rely on traditional, non-AI methods. These techniques have value, but they don\u2019t offer the intelligence or automation many organizations assume they\u2019re getting.<\/p>\n

In this blog, we’re touching base with our subject matter experts to draw a clear line between what\u2019s actually predictive and what\u2019s just marketed as such.<\/p>\n<\/div>\n

The long history of non-AI predictive maintenance<\/h2>\n

Predictive maintenance didn\u2019t begin with artificial intelligence. As Mike Cooper<\/a> (opens in new tab)<\/span> highlights, “for decades, maintenance teams have used tried-and-true methods to anticipate failures before they happen. AI is relatively new for maintenance teams; there are a lot of traditional forms of prediction that aren’t always talked about.”<\/q><\/p>\n

The traditional forms Mike mentions include:<\/p>\n

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\"Condition<\/figure>\n<\/div>\n
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Condition-based<\/span> or threshold monitoring<\/strong>
This is when an alert is triggered for a variable like temperature or vibration that exceeds a predefined limit.<\/p>\n<\/p><\/div>\n<\/div>\n

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\"Statistical<\/figure>\n<\/div>\n
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Statistical trend analysis<\/strong>
This is when you are spotting anomalies by analyzing changes in equipment performance over time.<\/p>\n<\/p><\/div>\n<\/div>\n

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\"Physics-based<\/figure>\n<\/div>\n
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Physics-based models<\/strong>
An example of this is using mathematical models to simulate how and when components are likely to degrade.<\/p>\n<\/p><\/div>\n<\/div>\n

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\"OEM<\/figure>\n<\/div>\n
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OEM guidelines and lifecycle models<\/strong>
This is when you are estimating failure timelines based on historical data and manufacturer specs.<\/p>\n<\/p><\/div>\n<\/div>\n

Each of these approaches laid the foundation for PdM, and they’re still being used today. Mike adds that “the catch with these four approaches is that they’re not truly predictive in the ways AI can be, and that’s important for the users of a CMMS<\/a> to understand.”<\/q><\/p>\n

AI driven predictive maintenance isn’t what you think<\/h2>\n

After speaking with some of our industry experts, one thing has become abundantly clear: many CMMS providers are claiming predictive AI capabilities, but it’s mostly statistical or threshold-based logic.<\/strong><\/p>\n

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What we see in the market is the bold claim that a CMMS has AI-driven predictive maintenance capabilities, and that’s simply not true.<\/p>\n<\/blockquote>\n

Mike goes on to explain that true AI-driven PdM uses machine learning (ML) to identify certain patterns. For example, it can:<\/p>\n