AI IN MININGAI in mining: fewer missed tenement deadlines, fewer unplanned breakdowns

Where does AI pay off in mining?

Six places, in roughly the order we would look at them. Each has documents or data you already hold, and a number you can measure before and after.

The common thread: the AI reads, sorts, checks and drafts. A tenement manager, maintenance planner or environmental officer approves. Nothing is lodged or changed without a person.

What makes mining in Australia different?

The stakes are national. Australia’s resource and energy export earnings were $403 billion in 2025–26 and are forecast at about $422 billion in 2026–27, according to the Office of the Chief Economist’s September 2026 quarterly. The same report flags rising diesel prices as a cost risk for mining and transport. [2] A few points of cost or time matter at that scale.

The big miners have already automated the haul road. Australia had 1,024 autonomous haul trucks in July 2025, up from 927 a year earlier, second only to China. [3] That is mostly the work of the majors and the equipment makers. For mid-tier miners and contractors, the larger gap is usually in the office: obligations, approvals, maintenance history and reports still spread across spreadsheets, inboxes and personal devices.

Safety is regulated state by state. The Commonwealth does not regulate work health and safety in mining; each state and territory does. [12] In 2025, 7 mining workers died from work-related injuries, a rate of 2.3 per 100,000 workers against 1.5 across all industries. [4] Safe Work Australia also found fatality rates rise with remoteness, with workers in Very Remote Australia around 8 times more likely to die from a work-related injury than the national average. [4]

Reporting keeps growing. Companies above the thresholds report emissions and energy under the National Greenhouse and Energy Reporting (NGER) scheme by 31 October each year. [10] Mandatory sustainability reports now apply to the first group of entities for financial years from 1 January 2025, the second from 1 July 2026 and the third from 1 July 2027, and an NGER reporting obligation is one of the triggers. [11]

How does AI help tenement and land management?

Tenement management is a calendar of obligations hidden inside documents: grant conditions, expenditure commitments, rent and rates, heritage and access agreements, and reports with fixed due dates.

In Western Australia, TENGRAPH Web maps mining tenements and petroleum titles and links to Mineral Titles Online, mineral exploration reports (WAMEX) and the MINEDEX mines database. [5] The data is public. Pulling it together for your own holdings, and acting on it in time, is the work.

What AI adds:

• Reads every grant and condition and turns them into a register: obligation, tenement, due date, owner, and the page it came from.
• Watches expenditure against commitments and flags a shortfall months before the anniversary, not after it.
• Drafts the report, such as a Form 5, from your records, for a tenement manager to check and lodge.
• Answers questions in plain English: “Which exploration licences are under their expenditure commitment this year?”

Proof: the PX4 tenement management platform

For PX4 Software Resources, Quantana built a tenement management platform for the mining industry. It brings complex, multi-stakeholder workflows and approvals into one system, with GIS integration to ESRI and TENGRAPH, task and data management with full traceability, reminders and automated workflow triggers, and centralised, secure data storage. [6]

The case study reports that the platform supports 60+ regulatory jurisdictions across 6 continents, is used by mining companies including Glencore, Fortescue and Aurelia Metals, and has delivered 35% cost savings in mining project planning. [6]

To be clear about what that proves: PX4 is a platform, not an AI project. But it is exactly the foundation AI needs. One system of record, every obligation traced, every document in one place. That is what lets an AI read, remind and draft safely. If you are searching for PX4 tenement or land management, read the PX4 case study.

Can AI speed up document-heavy approvals?

Approvals produce conditions, and conditions produce work. Environmental management plans, mining proposals, clearing permits and heritage agreements each carry commitments that someone must track for years.

AI helps in two ways. Before lodgement, it checks a submission against the regulator’s checklist and your own past submissions, and lists what is missing. After approval, it turns the conditions into a register, links each one to the page it came from, and reminds the owner before each due date. When a regulator asks a question, your team finds the answer in minutes, with the source attached.

What it does not do: decide what to commit to, or talk to the regulator. Those stay with your people.

How does AI cut unplanned maintenance?

Most sites already hold the data: years of work orders, failure codes, operator notes and the condition data their fleet logs. AI reads it and finds the repeat failures and the warning signs that came before them.

The economics are well studied. A long-running US federal guide to operations and maintenance estimates that a properly functioning predictive maintenance program saves 8% to 12% over preventive maintenance alone, and that sites relying mainly on reactive maintenance can find savings above 30% to 40%. [7] Those are building and plant figures, not mining ones, so treat them as a direction, not a promise.

Adjacent proof. We worked with Facillizence on its retrofit facility platform, which combines QR tags, sensors, cloud and AI to measure and monitor operations in real time. The case study reports savings of up to 20% of operations and maintenance cost per square foot. [8]

For AISIN (Toyota Group), we designed an edge computing solution installed in government vehicles that detects and reports road issues in real time, now operational in Japan and Europe. [9] We have not deployed it on a mine. But the pattern, a device on a vehicle spotting surface problems as it drives, is the same one haul road condition monitoring needs.

Where does AI help with safety and haulage?

Safety. Incident and near-miss reports are written in free text, so patterns hide. Nationally, vehicle incidents caused 41% of work-related deaths in 2025, more than any other mechanism. [4] AI can read every report, group them by cause, equipment and location, and show a supervisor the pattern before the next serious incident. It can also check that every corrective action has an owner and a date.

Haulage. Full autonomy is the equipment makers’ territory, and the majors already run it at scale. [3] For everyone else, AI is useful one level up: reading dispatch, payload and cycle-time data, and flagging the trucks, routes and shifts that lose the most time to queues and delays. With diesel a rising cost risk, [2] idle time at the shovel is money.

Can AI help with NGER and sustainability reporting?

Yes, with the collection and checking, which is where most of the time goes. Fuel dockets, electricity bills, production records and contractor data arrive in different formats from different sites.

AI reads them, maps each line to the right category, flags gaps and outliers, and keeps a trail from every number to its source document. That trail is what auditors ask for. The calculation methods, the judgement calls and the sign-off stay with your reporting team. NGER reports are due by 31 October each year, [10] so the payoff is a calmer September.

General information, not legal advice. As at 10 Nov 2026. Check your own obligations with your advisers.