AI in the mining industry works on two layers. Machine learning already runs autonomous haulage, fleet dispatch, process control and equipment failure prediction. Language models and agents add the layer that reads and writes: geology and exploration reports prepared under JORC, NI 43-101 or S-K 1300, maintenance histories for fleets and fixed plant, safety procedures and incident reports, permits, ESG and tailings disclosures, and contractor onboarding material. Because many mines sit far from reliable networks, and because reserve data and incident records are sensitive, mining companies often run these models at the site or in their own data centre.
Where AI fits in the mining industry
Most lists of AI applications in the mining industry mix two different technologies, and a buyer gets further by separating them. The useful split is by the type of data a task consumes. Numbers and images belong to the AI and ML models built and validated for them. Documents are where generative AI and agents add something new.
| Area | Document work for language models and agents | Numerical and vision work for other models |
|---|---|---|
| Exploration and geology | Searching past reports, drafting logging summaries and technical report sections | Geological modelling, geostatistics, geophysical interpretation |
| Resource and reserve reporting | Checking draft reports against code checklists, consistency of tables and text | Block models, resource estimation, pit optimisation |
| Mining operations | Shift handovers, ground control and blasting procedures | Autonomous haulage, dispatch, drill and blast optimisation |
| Maintenance | Work order triage, failure history search, job plan drafts | Condition monitoring, oil analysis trends, failure prediction |
| Processing | Plant procedures, metallurgical test reports | Process control, grade and recovery models |
| Safety and health | Incident reports, near miss analysis, training records | Proximity detection, fatigue monitoring |
| Environment and tailings | Permit conditions, monitoring reports, GISTM disclosures | Water balance and geotechnical instrument models |
The rest of this guide covers the document column and explains where the two layers meet.
Why remote and intermittently connected sites change the design
A mine is often hours from the nearest city, connected by satellite, microwave or a single fibre route. When the link is slow, a cloud assistant that needs a round trip for every question is frustrating. When it drops, the assistant is gone, and that tends to happen during the storm or outage when people most need a procedure.
Running the model locally solves this. An open-weight model on a small GPU server in the site data room, with a local index of procedures, equipment manuals and maintenance history, answers questions without leaving the site network. The site syncs new documents, logs and model updates with the corporate data centre on a schedule. Underground operations with limited network coverage can reach the same server from fixed terminals at the crib room or workshop.
Some operations go further and keep certain networks fully isolated, for example process control networks at the concentrator. AI tools should not reach into those zones at all; they read copies of data that cross an approved boundary. Our walkthrough of AI deployments on isolated networks covers offline model transfer, and the OT documentation Q&A use case shows how operators of other industrial plants make control system documentation searchable without touching the controls.
Geology, exploration reports and the technical reporting codes
Exploration generates drill logs, assay certificates, core photos, geophysical surveys, mapping notes and decades of internal and consultant reports. Acquisitions add another company’s archive, often scanned without good indexing. Geologists spend real time finding the 1990s drilling campaign on a neighbouring tenement or the metallurgical test that was done before the last owner stopped work.
A retrieval assistant over that archive, with OCR applied to scans, answers those questions with a citation to the page. It can also draft the routine parts of a report: the history of previous work, the description of sampling and assaying methods, the summary of drill hole data, and the comparison of a draft against a reporting code’s checklist.
The Competent Person or Qualified Person stays responsible
Public reporting of exploration results, mineral resources and reserves sits under codes that put a named professional in charge:
- JORC Code 2012 (Australasia). A Competent Person must be a member or fellow of the AusIMM, the Australian Institute of Geoscientists or a Recognised Professional Organisation, with at least five years of experience relevant to the style of mineralisation and the activity being reported. The JORC Committee has been preparing an updated code; its update page records a provisional code being finalised in March 2026 and a progress webinar in August 2026, with formal approval, ASX consultation and a transition period still to follow. Until then the 2012 edition applies.
- NI 43-101 (Canada). Disclosure of scientific and technical information about a mineral project must be based on information prepared by or under the supervision of a qualified person. The Canadian Securities Administrators published a proposed repeal and replacement of the instrument for comment in June 2025; the Alberta Securities Commission’s listing shows no final adoption as of October 2026.
- Regulation S-K Subpart 1300 (United States). Item 1302 requires a registrant’s disclosure of exploration results, resources and reserves to be based on information prepared by a qualified person, and requires a dated and signed technical report summary from that person for each material property.
None of these codes forbids software. All of them require a person who understood the data and stands behind the estimate. A model that drafts sections is a tool in that person’s hands, and the audit trail should show what the model produced and what the Competent Person changed.
Maintenance knowledge for fleets and fixed plant
Mobile fleets and fixed plant produce long maintenance histories: work orders, failure codes, oil analysis, component change-outs, OEM service bulletins and technician comments. The structured fields are often incomplete and the free text holds the knowledge, such as which repair finally stopped a recurring hydraulic leak or why a crusher liner change took longer than planned.
Three tasks suit language models:
- Work order triage. Read each new request, compare it with the asset’s history and similar past orders, suggest a priority and failure code, and draft the job plan with parts and isolation steps for a planner to approve.
- Troubleshooting from history. Answer “what fixed this fault code on this truck model last time” across all sites, with links to the work orders.
- Shutdown planning. Summarise lessons from previous shutdowns of the same mill or kiln into a checklist for the planning team.
Condition monitoring and failure prediction stay with sensor models and reliability engineers. The predictive maintenance support use case shows how the two connect: the sensor model raises the alert, and the agent explains it with history and drafts the notification. For geographically spread assets, the field and engineering knowledge use case covers the same pattern for crews working away from the office.
Safety procedures, incident reports and MSHA requirements
Safety documentation in mining is heavily regulated, which makes it suitable for drafting support and unsuitable for automation without review. The US examples below come from Title 30 of the Code of Federal Regulations, administered by the Mine Safety and Health Administration; other jurisdictions have their own equivalents.
Reporting deadlines
Under 30 CFR 50.10, an operator must contact MSHA immediately, and within 15 minutes, once it knows or should know of an accident involving a death, an injury or entrapment with reasonable potential to cause death, or another listed accident. Under 50.20, each accident, occupational injury and occupational illness is reported on Form 7000-1 within ten working days.
An agent cannot make the 15-minute call; a person does. It can help with the slower work: assembling the shift log, permits, equipment records and witness statements into a timeline, drafting the 7000-1 narrative for review, and grouping near miss reports by hazard so trends show up before an accident.
Recent MSHA rulemaking to track
- Surface mobile equipment. A final rule published in December 2023 and effective on 19 January 2024 requires operators to develop and update a written safety program for surface mobile equipment, with input from miners and their representatives.
- Respirable crystalline silica. MSHA’s April 2024 rule set compliance dates of 12 months for coal and 24 months for metal and nonmetal mines. Following a court-ordered stay of the compliance deadlines, MSHA published a notice on 6 April 2026 delaying the metal and nonmetal conforming amendments indefinitely, pending judicial review.
- Ongoing proposals. In 2026 MSHA has proposed changes on ventilation and roof control plan approval criteria and on powered air-purifying respirators in underground mines.
Procedures change with each of these. A procedure-writing agent can draft the revised program or work instruction from the new rule text and the existing procedure, and mark every change for the safety manager. The procedure and playbook authoring use case describes that review workflow.
Contractor onboarding and training records
Mines rely heavily on contractors for shutdowns, construction and specialist services, and every contractor needs site-specific training before starting work. In the US, Part 48 sets minimum training: at least 40 hours for new underground miners and 24 hours for new surface miners under 48.5 and 48.25, 8 hours of annual refresher training under 48.8, and hazard training before work begins for certain other workers under 48.31, with certificates kept for inspection.
An assistant can generate site induction material from the current procedures, answer a contractor’s questions about site rules in plain language, and check that each worker’s training records match the tasks on the work permit. A human trainer still delivers and signs off the training; the model keeps the material current and flags gaps.
Permits, ESG reporting and tailings
Permit conditions and environmental reports
A mining permit can carry hundreds of conditions on water discharge, dust, noise, blasting, rehabilitation and reporting. Monitoring data arrives continuously, and quarterly and annual reports must show compliance with each condition. A retrieval assistant can map every condition to the monitoring data and report section that addresses it, flag conditions with no evidence, and draft the narrative for the environmental team to check against the source data. Sustainability reports draw on the same material.
The Global Industry Standard on Tailings Management
ICMM members committed on 5 August 2020 to implement the Global Industry Standard on Tailings Management, known as GISTM. It has six topic areas, 15 principles and 77 auditable requirements, provides for an accountable executive, an engineer of record and a responsible tailings facility engineer, and requires public disclosure of information about tailings facilities. Members committed to conform by August 2023 for facilities classed as extreme or very high consequence, and by August 2025 for all others. ICMM’s progress report of 4 November 2025 covered 836 member facilities and found 67 per cent in full conformance and 33 per cent in partial conformance.
Tailings governance runs on documents: design reports, construction records, instrument readings, inspection reports, independent review reports and the disclosure itself. An assistant that answers “when did the engineer of record last review this facility’s freeboard” with a citation saves hours. Drafting the disclosure from approved reports is a reasonable task for a model; deciding whether a facility is safe is not.
Agentic AI in the mining industry: what agents should and should not do
Agents are useful at a mine when a task has several steps across systems and a clear human decision at the end. Good first candidates are maintenance triage, incident report assembly, permit condition tracking and contractor document checks.
Set boundaries before the first pilot:
- Read first, write later. Agents start with read access to the maintenance system, document library and permit register. Writing back a work order or updating a register comes later, one action at a time, each with an approval step.
- No control actions. Agents never send commands to fleet management, autonomous haulage, process control or ventilation systems.
- Named approvers. Every agent output that leaves the team, such as a regulator report, a technical report section or a tailings disclosure, has a named human approver recorded in the log.
Autonomous haulage, computer vision and process optimisation remain the domain of their own specialised systems, validated by the vendors and the mine’s engineers. A language model can explain their outputs in plain language or search their logs, but it should not be put in charge of them.
A reference architecture for mining companies
| Layer | What sits there | AI design rule |
|---|---|---|
| Process control and fleet systems | PLCs, SCADA, autonomous haulage, fleet management | No AI connection; copies of data leave through approved boundaries |
| Site data room | Small GPU server, local index of procedures, manuals and history | Works offline, syncs on a schedule |
| Corporate data centre or private cloud | Larger models, full retrieval index, agent platform, audit log | Local models for geology, reserve, incident and permit data |
| Business systems | Maintenance system, document management, permit register, training records | Read access first; write actions need approval |
| Approved external models | Optional, for public information | Routed by policy and logged |
How VDF AI fits mining companies
VDF AI installs where a mining company already runs its systems: a corporate data centre, a private cloud, a site server room or a network with no internet route at all. Configured with local models only, it keeps geology data, reserve estimates, incident records and permit files within those walls, together with the retrieval indexes, conversation history and logs it creates. VDF AI holds no certification or regulatory approval of its own; it falls under the miner’s own security program and the reporting obligations of its Competent and Qualified Persons.
Each workflow above maps onto governed agents and multi-agent workflows. A maintenance agent drafts the job plan and waits for the planner’s approval; an incident agent builds the timeline and hands the draft to the safety superintendent. Tools are granted per role, retrieval honours per-collection access so that, for example, unreleased reserve work stays with the technical team, and the audit trail shows who asked, which model answered, which sources were used and which tools were called. VDF AI Router serves local open-weight models through Ollama, vLLM or other OpenAI-compatible endpoints and can include an approved cloud model under policy, with routing rules, budgets and per-call audit, or exclude cloud routes entirely for remote sites.
On-premises installations sign users in through Microsoft Entra ID single sign-on natively, while Okta, Keycloak and other SAML or OIDC providers go through an SSO-aware reverse proxy; role-based access control is part of every plan. Seats are sold in bundles, and a mining group can begin any bundle with a three-month platform trial at one site.
See also the critical infrastructure and OT solution page, the critical infrastructure use case hub and the overview of on-premises AI for energy and utility operators. To gauge readiness across data, site infrastructure and governance before picking a first site, use the free AI readiness assessment.
Nothing here is legal advice; check current rules with your own advisers before acting.
Sources
Verified 25 October 2026.
- JORC Code 2012 Edition (PDF)
- JORC Code update page
- Alberta Securities Commission, NI 43-101 instrument listing
- 17 CFR 229.1302, qualified person and technical report summary (S-K 1300)
- 30 CFR 50.10, immediate notification
- 30 CFR 50.20, Form 7000-1 reporting
- 30 CFR Part 48, training and retraining of miners
- MSHA final rule, Safety Program for Surface Mobile Equipment, 88 FR 87904
- MSHA respirable crystalline silica rulemaking page
- MSHA notice delaying silica conforming amendments, 91 FR 17143 (6 April 2026)
- MSHA proposed rule, Ventilation Plan Approval Criteria (August 2026)
- MSHA proposed rule, PAPRs in Underground Mines (October 2026)
- ICMM, Global Industry Standard on Tailings Management
- ICMM, report on members’ progress towards GISTM conformance (4 November 2025)