Australia’s mining sector has been deploying AI with less fanfare than other industries but often more practical success. Here’s what’s actually happening in resources AI as we close out 2025.

Where Mining AI Is Working

Predictive Maintenance

The clearest AI win in mining is predicting equipment failure before it happens. Heavy equipment (trucks, excavators, processing equipment) is expensive to repair and more expensive when failed equipment stops production.

What’s deployed: Sensor data from equipment feeds ML models that predict component failure. Maintenance happens during planned downtime rather than after unexpected failure.

Results reported: 15-25% reduction in unplanned downtime. Maintenance cost reductions of 10-15%. These are significant numbers on expensive equipment.

Why it works: Clean sensor data, clear failure definitions, measurable outcomes, high stakes. All the ingredients for AI success.

Geological Modelling

AI assists in interpreting geological data – drill core analysis, seismic interpretation, deposit modelling. This doesn’t replace geologists but helps them process more data faster.

What’s deployed: Computer vision for core analysis. ML models for deposit boundary prediction. Integration with geological modelling software.

Results reported: 20-30% faster analysis. Improved model accuracy (harder to quantify). Better drill targeting reducing exploration waste.

Why it works: Geological interpretation has always been data-heavy. AI augments what geologists already do rather than replacing their judgment.

Autonomous Operations

Mining has been a leader in autonomous vehicles. Autonomous haul trucks have operated at scale in Australian mines for years. This has expanded to include drilling, blasting, and other operations.

What’s deployed: Autonomous haul truck fleets (Rio Tinto, BHP, and others). Semi-autonomous drilling. Automated ore processing adjustment.

Results reported: Productivity improvements of 10-20% for hauling. Improved safety (removing humans from hazardous operations). 24/7 operation capability.

Why it works: Mines are controlled environments. Routes are defined. Conditions are known. This is easier than general autonomous driving.

Production Optimisation

AI optimising throughput across the value chain – extraction rates, processing parameters, logistics coordination.

What’s deployed: ML models adjusting processing parameters in real-time. Optimisation of truck dispatch and scheduling. Integration across mine-to-port value chain.

Results reported: 2-5% throughput improvements. Significant at scale – a 3% improvement on a major iron ore operation is meaningful revenue.

Why it works: Mining has extensive operational data and clear optimisation objectives. Small improvements have large dollar impact.

Mining-Specific Challenges

Remote Operations

Many Australian mines are in remote locations with limited connectivity. This affects AI deployment:

• Edge computing required for real-time applications
• Data synchronisation challenges for cloud-based systems
• Limited on-site technical support

Successful deployments account for remote operation from the start.

Harsh Conditions

Mining environments are harsh – dust, heat, vibration. Sensors and equipment face conditions consumer and office technology doesn’t.

• Ruggedised equipment required
• Sensor reliability varies
• Maintenance of AI infrastructure itself is challenging

Safety Criticality

Mining operations can be dangerous. AI systems affecting safety require additional rigour:

• Extensive testing before deployment
• Human oversight requirements
• Regulatory compliance for safety-critical systems

This slows deployment but is non-negotiable.

Long Investment Cycles

Mining projects span decades. Technology decisions must account for long-term support and evolution:

• Vendor stability matters more than in other sectors
• Technology lock-in risks are significant
• Upgrade paths must be considered at deployment

What’s Not Working (Yet)

Autonomous Everything

Despite progress, fully autonomous mines remain aspirational. Most operations use partial automation with significant human involvement.

The gap: complex tasks, edge cases, and coordination challenges that current AI can’t handle reliably.

Timeline: Continued incremental progress rather than sudden full automation.

Real-Time Geological Interpretation

While AI assists geological work, real-time interpretation that guides drilling in the moment remains limited. The uncertainty involved exceeds current AI reliability.

The gap: Geological interpretation requires judgment under uncertainty that AI can’t fully replicate.

Timeline: Continued improvement in assistance, not replacement.

Cross-Site Standardisation

Each mine has specific characteristics. AI models trained at one site often don’t transfer well to others without significant adaptation.

The gap: Transfer learning and generalisation remain challenging.

Timeline: Gradual improvement as more data and better techniques develop.

Lessons for Other Industries

Mining AI offers lessons applicable elsewhere:

Lesson 1: Start with Clear ROI

Mining’s practical approach – investing where ROI is clear and measurable – drives success. Predictive maintenance has obvious value. That’s where investment went.

Other industries could benefit from similar discipline.

Lesson 2: Operational Integration Matters

Successful mining AI is integrated with operational systems and workflows. It’s not a separate innovation project but part of how work gets done.

Lesson 3: Harsh Conditions Reveal Weaknesses

Mining’s harsh environment quickly exposes unreliable technology. The solutions that survive are genuinely robust.

Other industries can learn from this – test rigorously before deployment.

Lesson 4: Safety Constraints Drive Quality

Mining’s safety requirements force rigour that produces better systems. Constraints improve outcomes.

Lesson 5: Long-Term Thinking Matters

Mining’s long investment horizons encourage sustainable technology choices rather than chasing trends.

The Vendor Landscape

Mining AI involves several vendor categories:

Major mining software providers: Companies like AVEVA, Hexagon, and Micromine embedding AI in established mining software.

Equipment manufacturers: Caterpillar, Komatsu, and others building AI into equipment and maintenance systems.

Specialist AI providers: Companies focused specifically on mining AI applications.

System integrators: Big 4 and technology consultants implementing and integrating solutions.

Most large mining companies use multiple vendors, selecting based on specific application needs.

Looking Ahead

Mining AI will continue to evolve:

Short-term (2026): Continued scaling of proven applications. More autonomous operations. Improved integration across operations.

Medium-term (2027-2028): Better real-time optimisation. More sophisticated geological AI assistance. Progress toward more autonomous operations.

Long-term (2029+): Potential for significantly more autonomous operations, though human involvement will remain substantial.

Final Thought

Mining’s AI adoption demonstrates what practical, ROI-focused deployment looks like. Less hype, more substance. Clear use cases. Measurable outcomes.

Other industries talk more about AI transformation. Mining quietly does it. The sector’s practical approach offers a model for enterprise AI deployment more broadly.

Sometimes the boring approach is the effective approach.