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    BHP on data and AI in mining: decision-making lessons for project engineers

    March 25, 2026|

    Reviewed by Tom Sullivan

    BHP on data and AI in mining: decision-making lessons for project engineers

    First reported on International Mining – News

    30 Second Briefing

    BHP Chief Digital Officer Mikko Tepponen argues that falling discovery rates and more complex orebodies are pushing miners towards data-centric decision-making, from exploration targeting to multi-decade capital allocation. He points to integrating geological, geophysical and drilling datasets into unified cloud platforms and using machine learning models to rank targets and optimise mine plans under multiple regulatory and ESG constraints. Tepponen stresses that value comes from linking these AI tools directly to operational decisions, such as dynamic cut-off grade strategies and real-time processing adjustments, rather than from pilots in isolated data science teams.

    Technical Brief

    • Tepponen frames uncertainty across three decision tiers: discovery, capital allocation and day-to-day operations.
    • He notes declining discovery rates are coinciding with more structurally complex, deeper and lower-grade deposits.
    • Regulatory variability across jurisdictions is cited as a core constraint on long-horizon capital deployment.
    • Multi-decade project lives are emphasised as amplifying the cost of early-stage geological or economic misjudgements.

    Our Take

    Our database shows only a handful of mining Op‑Eds tagged to AI or artificial intelligence among more than a thousand Mining stories, suggesting BHP is moving earlier than many peers in publicly framing data and AI as a core strategic lever rather than a back‑office optimisation tool.

    For a diversified group like BHP, embedding AI into planning and operations could be particularly impactful in copper and potash, where recent coverage highlights multi‑billion‑dollar, long‑life projects whose value is highly sensitive to orebody characterisation and schedule discipline.

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    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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