One of the easiest ways to misuse AI in engineering is to begin with the tool instead of the problem. Once people become interested in automation, there is a natural temptation to ask, “What else can we apply this to?” That curiosity is understandable. But in reliability engineering, it can quickly lead to the wrong kind of thinking.
Not every process should be automated. Not every judgement task should be accelerated. And not every repetitive activity is actually worth turning into a machine-supported workflow.
This matters because reliability work is not just about processing information. It is about understanding failure, context, consequence, uncertainty, and operational trade-offs. Some tasks benefit from speed and structure. Others depend on careful interpretation, tacit knowledge, and cross-functional judgement. If we fail to distinguish between the two, we risk creating outputs that look efficient but weaken decision quality.
A useful rule of thumb is this: AI tends to perform best when the task is high-volume, pattern-based, repetitive, and constrained. It tends to perform poorly when the task is low-volume, consequence-heavy, ambiguous, and deeply dependent on system context.
For example, classifying thousands of work-order descriptions into issue categories may be a strong candidate for AI support. The task is repetitive, language-based, and structurally similar across records. By contrast, deciding whether a specific failure history justifies a maintenance strategy change is not simply a classification problem. It requires context about production criticality, design intent, failure consequence, standby philosophy, maintainability, cost, operational tolerance, and stakeholder priorities.
The danger is not that AI will always be wrong. The danger is that it may produce a clean, confident-looking answer in places where confidence is exactly what should be questioned.
That is why I think engineers need a more selective automation mindset. Before applying AI, it helps to ask a few uncomfortable questions:
Is the current task actually painful enough to justify automation?
Is the process stable, or does it change too often?
Would an error here be easy to detect, or easy to miss?
Does the task rely on information that is visible in the data, or on context that exists mainly in people’s heads?
If this workflow worked perfectly, what exactly would it improve?
Those questions matter because a lot of waste comes not from failed automation, but from automating low-value tasks while leaving the true bottlenecks untouched.
There is also a professional risk. Over-automation can create distance between engineers and the material reality of the systems they are responsible for. If people stop reading failure descriptions carefully, stop interrogating anomalies, or stop asking whether a pattern genuinely reflects a physical issue, they may gradually lose the interpretive skill that makes reliability work valuable in the first place.
This is why I do not think the future of engineering work is “AI everywhere.”
I think it is more likely to be “AI where repetition dominates, and human judgement where consequence dominates.”
That is a less dramatic story than full automation. But it is a more useful one.
The real maturity is not in proving that AI can be applied.
It is in knowing where it should stop.