Why your CMMS still isn’t delivering the reliability gains you expected

29.09.2026

You invested in a Computerized Maintenance Management System (CMMS). You expected it to automatically handle work: manage work orders, schedule preventive maintenance on time, track inventory, and automatically reorder parts. You also expected unplanned downtime to drop as a result, along with lower maintenance costs.

Yet maintenance is still mostly reactive. And you're not seeing the cost savings and improved asset reliability you wanted and needed reflected in the numbers.

And it's not just about the data you started with. New information and data keep coming in continuously, on top of that. None of it gets integrated automatically, let alone in a way that helps you reach the results you expected. Sound familiar?

Market data confirms it

The MaintainX State of Industrial Maintenance 2026 survey (Opens in new tab) (n=2,234 maintenance and operations managers) shows that getting unplanned downtime under control is a widespread challenge, and that the cost of it is rising rather than falling.

64% Of respondents say they use a preventive maintenance program, expecting to spend more time on planned maintenance as a result. Yet in reality, 50% still spend less than 40% of their time on planned work. And 39% saw downtime costs actually rise over the past year.

A tool is not a miracle cure

The reason for a new CMMS was probably to gain more control and fewer surprises. That's a logical step. Did you only implement the tool? And did you not properly analyze the data going into it? Then that's probably why it isn't working well for you in practice. A tool doesn't change the quality of the data going into it. Were you working with asset information that was incomplete, outdated, or fragmented? And are you still using that information? Then the system gives you back exactly that: a clean-looking dashboard built on unreliable data. You're putting a bandage on the wound. And expect the infection to resolve itself. That's why many organizations see no reliability gains in daily practice after implementation.

Staff shortages and skills gaps get the blame

In the survey, respondents point to staff shortages (36%) and lack of the right skills (28%) as the cause of rising downtime. And I understand why they fall back on that. But I'm convinced it's not the underlying cause. Without reliable data, your people, however capable, are working with wrong or incomplete information. More people or more skills won't fix that.

A solid data foundation will.

And then there's AI

AI is seen as the next miracle cure, the solution that will finally deliver the desired result. But the same rule applies here: don't start implementing AI-driven tools if your data foundation isn't in order. They're only as good as the data they process. Without a reliable base, you get wrong conclusions faster, not the right insights.

And it's not only about getting the existing data right. The new information and data flowing in also has to be the right information: relevant to what the operation actually needs and aligned with what you want to get out of AI in the first place. Feeding an AI model more data doesn't help if it's not the data that answers your question.

Reliable data pays off across the board

With a reliable data foundation, it's not just your CMMS that gets the data it needs to provide reliable information for making the right decisions every day. You benefit in many more areas: operational excellence, easier compliance with laws and regulations, governance and risk management, interoperability, efficient daily operations, investment planning and a solid foundation for AI and advanced analytics.

My advice

Want to actually get better results from your CMMS? Stop treating symptoms and address the cause. Start with an assessment of your current asset information: where the data sits, how complete and reliable it is, and where the biggest risks arise. Only then determine the next step, whether that's cleaning up your data structure, better system integrations, or a different approach to data governance.

That step differs per organization. What doesn't differ: without that foundation, you won't get the results you want from any tool, from CMMS to AI.

About the author

Lina Bäckman

Senior Service Solutions Manager, AIM & Data

About the author

Lina Bäckman

Senior Service Solutions Manager, AIM & Data

I help asset-intensive organizations such as those in energy, process industry and utilities turn information complexity into structured, usable knowledge across the full asset lifecycle, from investment projects through operations and maintenance. At Etteplan I've built and led teams and developed service offerings. My background combines technical engineering with leadership and commercial responsibility, which means I understand both what clients need to solve and how to design solutions that work at scale. Etteplan AIM is built on the insight that asset information only creates value when it serves a clear purpose. Our team brings operational and engineering context to every information management task. We don't just store and organize; we structure information and set up the governance and processes so it's genuinely usable for the decisions and actions your teams need to take.