Every conveyor maintenance manager I’ve spoken with this year has had a vendor pitch about AI-driven predictive maintenance. Some of those pitches are now backed by real deployments. A lot of them aren’t.

The technology has matured to the point where you can have an honest conversation about where it works. Here’s what the operational reality looks like in 2026.

Where it’s working

Rotating equipment monitoring. Vibration analysis combined with thermal imaging and AI pattern recognition has become reliable enough for production use on critical fans, pumps, and gearboxes. The models flag bearing wear patterns weeks before they would have shown up in routine inspection. That’s real money saved on unplanned downtime.

Conveyor belt condition. Computer vision watching belts in real-time catches splice degradation, cover damage, and tracking issues earlier than scheduled inspection regimes. The capital cost is moderate — it’s mostly cameras and edge computing. The savings on belt replacement timing alone usually justifies the project within a year.

Haul truck component prediction. OEMs have built reasonable component-life prediction into newer generations of trucks. The models incorporate operating conditions, ground type, payload patterns, and historical maintenance data. They’re not perfect but they’re better than the previous generation of fleet maintenance scheduling.

Where it’s not yet delivering

Tailings dam monitoring with AI. A lot of vendors pitched AI-driven tailings monitoring after the major incidents of recent years. The models exist. Adoption is slow because the consequence of a false negative is so severe that operators are still relying primarily on traditional geotechnical monitoring with AI as a supplementary signal, not a primary one. That’s the right call.

Crusher and mill optimisation. The promise is dynamic adjustment of crusher and mill parameters based on incoming ore characteristics. The execution is hampered by upstream sensor data quality. If you can’t reliably characterise the feed in real time, the downstream optimisation can’t be smart. Most operations are still using fixed setpoints adjusted manually.

Predictive maintenance for rare equipment failures. AI models work well when there’s enough historical data to train on. For equipment that fails rarely, there isn’t enough data. Some vendors solve this by pooling data across operators, but that runs into competitive sensitivity issues. The result is that high-cost, low-frequency failures (the ones you’d most want to predict) are still hard.

The integration problem

The technical models aren’t usually the bottleneck. The bottleneck is integrating predictions into existing maintenance management workflows. A model that says “this bearing has 14 days of life remaining” only has value if that prediction translates into a work order, parts ordering, scheduled labour, and proper sequencing with other maintenance activities.

Operations that have built strong CMMS integration extract more value than operations running predictive models as a standalone dashboard. This is unsexy but it’s where the operational gain happens.

For miners looking at proper deployment rather than another pilot, partnering with Azure consulting services on the integration side often matters more than the choice of model itself. The model selection is becoming commoditised. The plumbing isn’t.

Data quality is still the killer

I’ve seen pilots fail because the operation didn’t have clean tag data, accurate equipment hierarchies, or reliable connections from sensors back to a central data platform. Without that foundation, no AI model is going to deliver. With it, even relatively simple models perform.

This is why the vendors who lead with “tell us about your data infrastructure” tend to deliver real outcomes. The ones who lead with model demos and ignore the data layer often produce expensive dashboards that nobody acts on.

What to look for in 2026

The interesting deployments in the next 12 months are going to be:

• Federated learning approaches that pool insights across operations without sharing raw data
• Edge AI on equipment, reducing reliance on connectivity to remote operations centres
• Better integration with shutdown planning and major maintenance windows

The fundamentals haven’t changed. Predictive maintenance works where you have good data, clear maintenance workflows, and committed operations leadership. It doesn’t work as a bolt-on technology project. That was true five years ago and it’s still true now.