Predictive Maintenance Isn’t Always AI and Here’s Why That Matters

AI driven predictive maintenance (PdM) (opens in new tab) 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’t offer the intelligence or automation many organizations assume they’re getting.

In this blog, we’re touching base with our subject matter experts to draw a clear line between what’s actually predictive and what’s just marketed as such.

The long history of non-AI predictive maintenance

Predictive maintenance didn’t begin with artificial intelligence. As Mike Cooper (opens in new tab) 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.”

The traditional forms Mike mentions include:

Condition-based or threshold monitoring
This is when an alert is triggered for a variable like temperature or vibration that exceeds a predefined limit.

Statistical trend analysis
This is when you are spotting anomalies by analyzing changes in equipment performance over time.

Physics-based models
An example of this is using mathematical models to simulate how and when components are likely to degrade.

OEM guidelines and lifecycle models
This is when you are estimating failure timelines based on historical data and manufacturer specs.

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 to understand.”

AI driven predictive maintenance isn’t what you think

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.

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.

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

• Identify failure patterns invisible to humans
• Improve predictions with every data point
• Recommend optimal maintenance timing based on real-world behavior and not just predefined rules

“When predictive maintenance is truly AI-enabled, it changes how teams think, work, and measure success. It doesn’t just detect problems; it helps prevent them altogether, with fewer false alarms and more confident decision-making,” says Mike.

But what form can AI-enabled PdM take? What does it look like?

“For one thing, AI-enabled means it can look at specific, measurable, machine details,” Mike adds. For example, these can be:

• Remaining useful life (RUL): Instead of guessing when an asset might fail, AI calculates how much time is left before a breakdown occurs.
• Failure rate: AI models continuously learn from operational data to estimate how often similar failures occur.
• Mean time to repair (MTTR): With earlier warnings and clearer fault diagnostics, teams can reduce the time it takes to fix issues.
• Unplanned downtime reduction: AI helps maintenance teams prioritize interventions before failures happen, cutting costly disruptions.
• Condition indicators: By analyzing data like vibration, temperature, and pressure, AI detects subtle shifts that precede failure, often weeks in advance.

Non-AI vs. AI based PdM approaches

Below is a table that outlines the non-AI and AI based predictive maintenance approaches:

Does predictive maintenance really work?

In short, yes. But it needs to be implemented correctly in order to work effectively for maintenance teams.

“Since PdM uses data from sensors, ML models, and historical trends to predict things like failures, that data needs to be accurate, and not only does it need to be accurate, but it also needs to be available,” Mike highlights.

Success for PdM really depends on the data quality and availability, the technological maturity (i.e., AI/ML models are trained and validated properly), and of course integration with operations (i.e., PdM is embedded into the workflows and decision-making process).

Why is predictive maintenance so important today?

Predictive maintenance is important because it changes maintenance from a reactive or scheduled task into a strategic, data-driven process. For industries like manufacturing, energy, and transportation, PdM can lead to thousands of dollars in savings and productivity gains.

Some benefits of PdM include:

• Minimizing unplanned downtime
• Extending asset life
• Reducing maintenance costs
• Improving safety (e.g., preventing hazardous failures in critical systems)
• Increasing operational efficiency (e.g., keeps production lines running smoothly)

Not all predictive solutions are made the same

In an industry crowded with predictive claims, it’s easy to assume all solutions offer similar value. But when predictive maintenance doesn’t use AI, you’re still doing most of the heavy lifting like manually setting thresholds, analyzing trends, and reacting after the fact.

With a true AI-powered system, your maintenance strategy becomes proactive, adaptive, and continuously improving.

If your PdM strategy still relies on old-school rules or statistical models, you’re missing out on the full potential of what AI can do, not just for assets, but for uptime, productivity, and peace of mind.

That’s where solutions like Fiix CMMS AI features stand out.

Want to see how true AI-driven PdM works in action?

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