And what actually works in the field
Artificial intelligence has promised to transform operations for more than a decade. From predictive maintenance to intelligent scheduling, the vision has been compelling — and investment has followed.
Yet across manufacturing, field service, and asset-heavy industries, the same pattern repeats: most AI pilots never make it past experimentation.
This isn’t because operators resist technology. It’s because many early AI initiatives were built on assumptions that don’t hold up in real operational environments.
Thinking about a predictive maintenance initiative?
If you're evaluating how to move from pilot to production in your maintenance organization, we offer a focused 30-minute performance review for maintenance leaders.
→ Schedule a Maintenance Performance ReviewThe failure point for most pilots isn’t the algorithm. It’s the interface between AI and day-to-day operations.
First, many pilots assume clean, complete data.
In reality, operational data is fragmented and imperfect. Documentation lives in PDFs and manuals, historical records are inconsistent, and machines change over time. Systems that rely on pristine datasets struggle the moment they encounter the messiness of the real world — which is almost immediately.
Second, they are designed for analysts rather than operators.
A surprising number of AI tools produce dashboards instead of decisions. Insights arrive after the fact, require interpretation, or sit outside the technician’s workflow. If AI doesn’t help the person doing the work at the moment a decision is required, it rarely gets used — regardless of how sophisticated the model is.
Third, prediction is treated as the end goal.
Knowing that a failure might occur is useful, but it’s rarely sufficient. Operators need to know what to check, which procedure applies, what parts are relevant, and how similar situations were handled in the past. Many pilots stop at prediction and never bridge the gap to action.
Finally, most systems are brittle in the face of change.
Operational environments are dynamic by nature. Equipment ages, suppliers shift, processes evolve, and edge cases are the norm. Rule-based approaches and narrowly trained models tend to degrade quickly once conditions deviate from the original training set.
The AI initiatives that do succeed — and scale — take a different approach.
They start with the operator, not the model. Instead of asking what the algorithm can predict, they ask what decision someone is trying to make, under what constraints, and with what information available. AI becomes a way to reduce cognitive load and surface relevant context — not an abstract analytics layer.
They embrace imperfect data rather than waiting for ideal conditions. Effective systems combine manuals, procedures, historical cases, and live inputs, improving incrementally through use. Progress matters more than purity.
They also connect knowledge to context. What matters is not just what the system knows, but when and where that knowledge is applied. The most useful tools adapt to task and situation, presenting information in a way that supports action rather than analysis.
And critically, they evolve with the operation. Successful approaches learn from new cases, incorporate feedback from the field, and adapt as equipment and processes change. They behave less like one-off projects and more like operational capabilities. By shifting the focus from the model to the operator’s decision, we’ve seen organizations move from pilot to production in weeks rather than the typical 12-18 month "science experiment" cycle.
To ensure a roadmap actually survives the first week on the floor, the strategy has to shift:
How this applies in practice?
We work with maintenance-heavy organizations to operationalize this approach — combining technician workflows, OEM documentation, and real-time context into usable decision support.
If you're evaluating this shift in your own environment, we can walk through what a low-risk pilot would realistically look like.
→ Request Strategy ReviewAI struggled in operations for so long not because the ambition was wrong, but because the approach was. Early efforts optimized for prediction, dashboards, and perfect data — while operations require support, workflows, and resilience to reality.
That gap is now closing.
Not because the vision changed, but because expectations did. The focus has shifted from models to outcomes, from analytics to execution, and from experimental pilots to systems that earn their place on the floor and in the field.
For teams willing to start where the work actually happens, AI is no longer a science experiment. It is becoming an operating advantage.