BHP’s AI and digital rollout: asset-level productivity lessons for mine engineers
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
BHP’s Chief Digital Officer Mikko Tepponen describes how AI, automation and data-led decision-making are being embedded directly into mine sites and processing hubs, with frontline crews owning tools rather than relying on centralised pilots. Examples include AI models optimising truck dispatch and shovel dig rates, predictive maintenance on critical assets such as conveyors and crushers, and digital twins for major processing circuits. Tepponen reports “significant financial value” already realised, signalling that future productivity gains will depend on scaling these asset-level solutions rather than standalone technology projects.
Technical Brief
- Tepponen stresses co-developing digital tools directly with individual BHP assets rather than off-site prototypes.
- Frontline ownership means operators configure and iterate algorithms themselves, instead of relying on central digital teams.
- Digital solutions are embedded into existing control-room workflows, avoiding separate “innovation” systems that operators must dual-run.
- BHP’s approach focuses on repeatable patterns across multiple operations, not one-off site-specific pilots.
- Tepponen frames digital work as part of standard operating practice, not a standalone technology programme.
Our Take
BHP’s push on AI and digital tools sits alongside its lower-emission steelmaking work with 13 major steelmakers, signalling that the company is treating data and technology as core levers for both productivity and decarbonisation rather than separate initiatives.
The presence of BHP Ventures in recent funding of mining tech startups in the San Francisco Bay Area suggests that some of the AI capabilities highlighted here may increasingly be sourced or co-developed through external innovators rather than built solely in-house.
BHP’s role in data- and study-heavy projects such as the Resolution Copper JV with Rio Tinto indicates that any internal AI and digital platforms will likely be tested first on complex, long-horizon copper developments where optimisation of design and permitting scenarios has outsized value.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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