Pragmatic. Focused on early value, sustainable foundations, and clarity on ROI. This is not a crisis — but the opportunity cost is real and already accruing.
AI in mining is an opportunity-cost consideration — pursue it with early value in mind, alongside careful foundational thinking.
Unlike other industries, mining isn't facing an existential AI crisis. So it's fine to be careful. But the opportunity cost of not pursuing AI is substantial and should prompt immediate, staged momentum — the biggest risk today is inaction while "shadow AI" already spreads across the workforce.
Pinpoint the "no-regrets" moves and pursue them with execution expertise, not a leap of faith.
Stand up guardrails and a staged rollout of AI tooling with a careful, governed foundation.
In parallel, build the foundational DNA of an AI-enabled organisation — culture over any single tool.
You'll have many questions about how to address the AI opportunity. These are the ones that matter most.
Where is the heaviest concentration of potential AI value in our business?
What is the cost of doing nothing?
What is our exposure today from unsanctioned AI use — "shadow AI" — by our own people?
Where should AI be positioned in our business — under the CTO/CIO, or a new CAIO?
What can we get going with tomorrow, to prove value from AI in the short term?
How can we transform our extensive historical data into a legitimate competitive asset?
The question isn't whether our people use unsanctioned AI — it's how much proprietary data has already left the building. It's happening now, it's not hypothetical, and for a miner it's dangerous.
employees use AI tools their employer never approved — adoption often led by senior staff, not juniors.[1]
of organisations suspect or have evidence staff are using prohibited public GenAI tools (Gartner).[2]
of data pasted into GenAI flows through personal, unmanaged accounts that bypass every corporate control.[3]
of all data breaches now involve shadow AI, at a higher average cost per breach (IBM, 2025).[4]
Drill results, ore-body and reserve models pasted into consumer LLMs move outside our control — and could inform a rival's bid on adjacent ground.
Offtake terms, JV and acquisition positions leaked mid-negotiation destroy deal value.
Guidance, reserve or results text drafted in a public tool risks selective-disclosure and JSE/SEC breaches before official release.
+ Others: safety, ESG leaks, as well as POPIA / GDPR breaches.
Use-case options are extensive — but the ones worth chasing first are concentrated across the value chain. Follow the flow, stage by stage; ★ marks a flagship deep dive.
Cross-cutting use cases, grouped by function. Select a function to expand its full list — ★ are flagship deep dives; the $ tier is an indicative cost signal.
The flagship opportunities are concentrated and proven — not a leap of faith. Select any to explore how it works and the value at stake.
The worst decision is inaction, especially given the likely spread of shadow AI. Ideally, appoint a Chief AI Officer, negotiate terms with AI providers, track token and cost usage on accessible dashboards, and set a roadmap to sharing knowledge and upskilling from the basics.
A word from Malka · A&M Chief AI & Knowledge OfficerA&M and NTT DATA deliver pragmatic early value on sustainable foundations. The partnership stacks as a pyramid — A&M builds AI capability and delivers value at the top, NTT DATA provides the intelligence and infrastructure beneath. Select any tier of the pyramid to reveal its full detail below; click it again to collapse. The A&M capability & value layer is shown first.
Every use case from the assessment is plotted by technology maturity (vertical) against ease of implementation (horizontal, the inverse of change-management difficulty). Dot size reflects value — scaled to the investment tier ($, $$, $$$) as a proxy for the scale of the prize. The sweet spot, top-right, is where mature, high-value and easy-to-land opportunities cluster. Hover or tap any point to read the use case, its value, maturity and ease. Gold rings mark the flagship deep dives. Positions are derived from the assessment's 1–5 maturity and change-management scores; indicative, not a precise ranking.
Hover a point to see the use case, its type and where it sits.
Momentum now, discipline throughout — every phase passes a gate before the next unlocks.
Appoint an AI lead, commit seed funding, set the mandate and risk appetite. Run the strategic assessment; design phases 2–3 and target the high-priority rollout areas. Deploy the governed LLM with guardrails from day one.
Approve stage-gated funding; endorse the operating model. Stand up the delivery COE — prioritise, pilot, scale — and embed governance, host the first amplified pilots and provide domain owners with sanctioned capability.
Federate ownership to sites; hold the portfolio to ROI. Run a continuous-improvement engine that matures and scales the portfolio, hardening model governance and the value-tracking mechanism.
Agree the no-regrets path — the de-risked, proven moves that are ready to pursue now.
Mandate the team and governance to run it, with clear ownership and accountability.
Set the guardrails that make broad AI use safe — turning shadow-AI risk into sanctioned capability.