Mining and resources organisations are investing in artificial intelligence at an accelerating rate — driven by the twin pressures of safety performance requirements and the productivity imperative of extracting more value from existing assets in an environment of rising input costs. But the mining AI landscape is at least as noisy as any other industrial sector, with technology vendors presenting capabilities that range from genuinely transformative to marketing-level rebranding of existing analytics tools.

The TeckNexus AI Use Case Prioritiser for Mining cuts through this noise by applying a structured prioritisation framework to the specific operational context, asset profile, and data environment of mining operations — generating a ranked action plan that reflects what is both valuable and achievable for a given organisation.

Mining AI: The Maturity Spectrum

Mining AI use cases exist across a wide maturity spectrum. At one end, autonomous haul truck systems and autonomous drilling equipment are proven, commercially deployed technologies with documented performance benchmarks from operators including Rio Tinto, Fortescue Metals, and BHP. At the other end, AI-driven ore body modelling and real-time grade control optimisation are emerging capabilities where the data requirements and implementation complexity are still being established at scale.

The AI Use Case Prioritiser for Mining tool applies this maturity distinction explicitly — distinguishing between proven use cases where the primary question is implementation readiness, and emerging use cases where the question is whether the data and integration foundations exist to make deployment viable.

Priority AI Use Cases for Mining Operations

• Autonomous Haulage: Autonomous and remote operation of haul trucks is the most commercially mature AI use case in mining. The productivity, safety, and operational consistency benefits are well documented. The primary implementation constraint is network infrastructure — autonomous haulage requires the low-latency, high-reliability connectivity that private cellular provides.
• Predictive Maintenance: Predictive maintenance for mining plant and mobile equipment reduces unplanned downtime events that can shut down entire production streams. The ROI is highest for high-utilisation, high-replacement-cost assets: primary and secondary crushers, SAG mills, rope shovels, draglines. Sensor data quality is the key feasibility constraint.
• Drill and Blast AI: AI-driven drill and blast optimisation — using machine learning on blast fragmentation data, drill performance logs, and downstream processing performance to optimise drilling patterns and explosive loading. Strong ROI in operations where blast fragmentation quality is a material determinant of mill throughput.
• Grade Control: Real-time ore grade sensing and AI-driven blending optimisation. Reduces dilution, improves mill feed consistency, and increases resource utilisation. Depends on sensor infrastructure at the mine face or processing plant feed point.
• Safety AI: Safety AI applications including fatigue detection for equipment operators, proximity detection in high-traffic areas, and AI-assisted gas detection and emergency response. The safety ROI is complemented by regulatory compliance benefit in jurisdictions with mandatory safety performance reporting.

Open-Cut vs Underground: Different AI Priorities

The Prioritiser handles open-cut and underground mining environments separately, because the AI use case mix and feasibility profile are substantially different. Open-cut operations have a stronger near-term AI case for autonomous equipment and productivity optimisation, with clearer data infrastructure and network coverage. Underground operations have a stronger safety AI case and a more complex connectivity environment that affects both use case feasibility and implementation cost.