Have you ever wondered why some operations are able to maintain control over the lifecycle of their assets, while others are constantly disrupted by unexpected failures at the worst possible time?
Today, we’ll explore the world of maintenance reliability — a critical pillar for large industries — and understand how <a href="https://www.ibm.com/think/topics/generative-ai">Generative Artificial Intelligence</a> is changing the rules of the game.
<h2>Maintenance reliability: from reactive models to strategic asset management</h2>
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The main goal of Reliability Management is to break the vicious cycle of reactive maintenance, often known as the “break-fix” approach. Through <a href="https://it-eam.com/pt/blog/expert-desk/reliability-centered-maintenance-rcm-achieving-efficiency-and-sustainability-in-asset-management">RCM (Reliability-Centered Maintenance)</a>, organizations move from reacting to failures to actively managing their assets.
But first, a bit of context. The concept of reliability is not new. It dates back to the 1950s in the United States, driven by military and aerospace studies. At the time, the challenge was to understand why electronic components failed during critical missions.
In simple terms, reliability is the probability that a piece of equipment will operate correctly within a given period and under specific conditions.
One important detail: talking about reliability without considering time is a common mistake. If we say a machine has 98% reliability, we must ask: 98% over how many hours of operation? Time is the key metric here.
<h2>FMEA: how to structure failure analysis</h2>
To ensure efficiency and improve availability rates, organizations rely on well-established methods such as FMEA (Failure Mode and Effects Analysis).
This is a multidisciplinary and detailed process that acts as the engine of a PDCA cycle, focused on continuous improvement of reliability: planning prevention, acting on risks, and constantly reviewing results.
Key questions to guide FMEA:
<ol>
<li>What can fail? (Identifying the failure mode)</li>
<li>What is the impact? (Analyzing the effect)</li>
<li>Why would it fail? (Identifying the root cause)</li>
<li>What will we do to prevent it? (Defining mitigation actions)</li>
</ol>
<h2>Generative AI in maintenance: what changes in practice</h2>
This is where the future takes shape. Traditionally, building an FMEA requires hours of meetings and manual analysis of extensive documentation. But what if this process could be accelerated?
With Generative AI, we’re no longer talking only about dashboards or reports — but about immediate, actionable insights. It can:
<ul>
<li>Analyze decades of maintenance history in seconds</li>
<li>Suggest probable failure causes that teams may not have identified</li>
<li>Generate tailored mitigation plans and corrective actions for each scenario</li>
</ul>
AI does not replace the reliability engineer — it augments their capabilities. It transforms raw data into actionable strategies automatically.
<h2>How to apply maintenance reliability with IBM Maximo</h2>
We are moving from the era of reactive maintenance to the era of intelligent reliability. And you may be asking: how can this be applied in practice within my operation?
The answer lies in <a href="https://it-eam.com/eam/solutions">IBM Maximo</a>, specifically in the Reliability Strategy module. Within the Maximo ecosystem, these analyses can be transformed into real operational actions. The system uses advanced algorithms to optimize maintenance strategies, balancing costs, risks, and performance.
With IBM Maximo, your FMEA is no longer a static document — it becomes a dynamic, operationally connected tool.
Want to learn how to apply Generative AI and Maximo’s reliability strategy module in your business? Get in touch with our team — we’re ready to help you reach a new level of availability and operational efficiency.
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Maintenance reliability is one of the key factors that determine operational performance in industrial environments. Discover how Generative AI is transforming this approach in practice.

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Have you ever wondered why some operations are able to maintain control over the lifecycle of their assets, while others are constantly disrupted by unexpected failures at the worst possible time? Today, we’ll explore the world of maintenance reliability — a critical pillar for large industries — and understand how Generative Artificial Intelligence is changing [&hellip;]

The new era of maintenance reliability: where engineering meets generative AI