That last point is where this post spends most of its time. The big miners have shown what AI can do. The open question for a mid-sized operator is how to get there from a weighbridge slip and an Excel sheet.
What AI does in the mining industry today
The money is real, but the estimates vary a lot. Meticulous Research puts the AI in mining market at $3.95 billion in 2026, rising to $12.45 billion by 2036 at 12.2% a year. Other firms report figures several times higher, mostly because they count autonomous truck hardware too. Treat any single market number as an indication, not a fact.
The adoption data is firmer. According to GlobalData, autonomous haul trucks in operation worldwide rose from 2,080 to 3,832 between July 2024 and July 2025, an 84% jump. China went from 562 to 2,090. BHP runs 360 and Rio Tinto 305. That is the clearest sign that AI in mining has left the pilot stage, at least for the largest operators.
8 AI use cases in mining, by stage of the mine
| Stage | Use case | What the AI does | Maturity in 2026 |
|---|---|---|---|
| Exploration | AI mineral exploration | Combines geophysics, geochemistry and historical drill data to rank where to drill next | Proven at a few players; KoBold Metals’ Mingomba copper find in Zambia is the best-known case |
| Planning | Drone surveys and 3D site models | Turns drone footage into orthomosaics, point clouds and terrain models; measures stockpiles and cut-and-fill | Mature capture, weak access: outputs often stay locked in desktop GIS and CAD files |
| Planning | Ore grade estimation | Predicts grade between drill holes to improve block models | Growing, needs clean assay data |
| Extraction | Autonomous haulage | Driverless trucks run continuously, stopping only for fuel and maintenance | Scaled at the largest surface mines (3,832 trucks worldwide) |
| Extraction | Drill and blast optimisation | Tunes blast patterns from rock and fragmentation data | Growing |
| Maintenance | Predictive maintenance | Reads vibration, temperature and engine data to flag equipment before it fails | Widely piloted, scaled at majors |
| Operations | Fuel and production anomaly detection | Reconciles fuel issued, trips run and tonnes moved to flag theft and underperformance | Early, and only possible once the data is digital |
| Safety | Safety and compliance monitoring | Computer vision for PPE and proximity, fatigue tracking, automated statutory registers | Growing, driven in India by DGMS rules |
Where AI projects in mining stall
Most mines can’t start with the glamorous rows of that table. The pattern we see is simple: the AI use case is clear, but the data it needs doesn’t exist in a usable form.
1. Operations run on spreadsheets around an ERP
At one open-cast coal mine we work with, SAP handled finance and roughly 75% of operations lived on spreadsheets and paper. You can’t predict fuel theft from data nobody captures.
2. Survey data is locked in specialist files
Drone outputs come out of Pix4D, ArcGIS and CAD as large proprietary files. Only one team can open them, so every question waits on that team.
3. Three versions of production
Survey volumes, GPS trip counts and manual slips rarely agree. A model trained on any one of them inherits its errors.
4. Connectivity at the pit is poor
Anything that needs a live connection to work fails in the place it matters most.
5. Compliance is bolted on last
In India, DGMS Circular 08/2020 pushes mines towards digital, auditable records: digital Form-A entries for every worker, a digital history sheet for every machine, and reporting in non-editable form. Systems that ignore it get rebuilt.
The fix is not a bigger model. It is a system of record that captures operations as they happen, and then an intelligence layer on top. We cover the general version of this problem in Why AI Pilots Stall Before Production.
Two mining systems we’ve put into production
Both are built for MineGate and Sainik Mining, which run drone surveying and open-cast coal operations in India.
The GIS Portal: a drone survey the whole mine can open
MineGate’s drone captures were good. Everything after the capture was the bottleneck. The GIS Portal turns GeoTIFF, LAS and PDF survey outputs into interactive 3D models in any browser. Planners measure stockpile volumes and cut-and-fill without opening CAD. Haul roads are flagged automatically where they run narrower or steeper than DGMS limits. Each mining client logs in and sees only its own sites. It has been live since June 2026.
“Our planners could open a web browser, access the GIS portal, pull up any mining site, and get a cut-fill volume or a haul-road safety check on the spot. No CAD, no waiting on the GIS team, no file exchange.”
Himalay Sharma, Operations Head, MineGate
MineOps: running an entire coal mine on one platform
MineOps is a Mining Execution System that brings 13 functions into one system of record: production, fleet, fuel, explosives, safety, dispatch, contractors, workforce, stores and DGMS compliance. It gives nine roles, from weighbridge operator to Mine ED, their own view. Production is captured three ways (survey, GPS trips and OCR on manual slips) and reconciled. Critical forms work offline at the pit. It is in delivery, with go-live planned for FY 2027–28, and targets a 3–5% cut in diesel cost leakage and 50%+ less manual statutory reporting.
MineOps is the foundation for the AI use cases in the table above. Its roadmap adds fuel-theft anomaly detection, equipment breakdown prediction and a natural-language MIS assistant on top of the system of record. That order matters: data first, intelligence second.
How to implement AI in your mine: FDE as a Service
Mining AI is a last-mile problem. Someone has to sit with the fuel manager, learn how a Parchi moves from the pit to the weighbridge, and build software that survives no signal and 45-degree heat. That is the job of a forward deployed engineer: a senior engineer who works inside your operation until the system is live and used.
With FDE as a Service, KnackLabs runs that whole function for you. We recruit, train and deploy senior FDEs who are live in weeks, not after a six-month hiring search. This is how the GIS Portal and MineOps were built.
We estimate a mining engagement takes around 6–8 weeks from scoping on site to the first users working in the system.
| Step | What happens | Output |
|---|---|---|
| 1. Scope on site | FDEs walk the operation, from pit to weighbridge to MIS, and map where data is lost | A ranked list of use cases with a baseline for each |
| 2. Build the system of record | Digitise the workflows the AI will need: production, fuel, fleet, surveys, compliance | One source of truth, offline-ready |
| 3. Ship to a few users first | Five supervisors, not fifty; watch where it breaks in the field | A working system, adopted by its first users |
| 4. Add the intelligence layer | Anomaly detection, predictive maintenance, assistants on top of clean data | AI use cases measured against step 1’s baseline |
| 5. Scale or hand over | Roll out across sites, or hand back to your team with documentation | Your system, your data, your IP |
If you sell software to mines, the same model works under your brand. Our FDEs deploy your platform at your mining customers, white-labelled, so signed deals reach production.
Frequently asked questions
How is AI used in the mining industry?
AI is used across the mine life cycle: to target exploration drilling, estimate ore grade, run autonomous haul trucks, predict equipment failures, process drone surveys into 3D models, detect fuel and production anomalies, and monitor safety and compliance.
What are the main benefits of AI in mining?
Higher equipment uptime, lower fuel and maintenance costs, safer sites with fewer people in hazardous zones, faster survey-to-decision cycles, and auditable compliance records. The size of each benefit depends on how much operational data is already digital.
How big is the AI in mining market?
Meticulous Research estimates $3.95 billion in 2026, growing to $12.45 billion by 2036 at a 12.2% CAGR. Estimates from other firms are higher because they include autonomous vehicle hardware.
How many autonomous trucks are used in mining?
GlobalData counted 3,832 autonomous haul trucks worldwide in July 2025, up from 2,080 a year earlier. China has the most (2,090), followed by Australia (1,024).
Can mid-sized mines use AI, or is it only for BHP and Rio Tinto?
Yes, but the starting point is different. Mid-sized mines usually need a digital system of record for production, fuel and fleet first. AI use cases like anomaly detection and predictive maintenance are then built on that data.
How long does it take to implement AI in a mine?
We estimate around 6–8 weeks from scoping on site to the first users working in the system. A focused system, such as a browser-based survey portal, can go live within months; a full mining execution system is a multi-phase programme.
What is DGMS compliance software?
Software that keeps the digital, auditable records India’s Directorate General of Mines Safety expects under Circular 08/2020, such as Form-A registers, attendance and machinery records, with a trail that can’t be edited.
Kartik Bansal is the co-founder and CEO of KnackLabs, an AI transformation firm that embeds senior engineers inside businesses to build and run production AI. Before KnackLabs he was a senior engineer at Microsoft and co-founder and CTO of Convosight.
Book a 30-minute call and we’ll map your first AI use case.
Sources
- Meticulous Research, AI in Mining Market: Global Opportunity Analysis and Industry Forecast 2026–2036 (Feb 2026)
- GlobalData, “China emerges as global leader in autonomous surface mining trucks”, Mining Technology (8 Aug 2025)
- Bloomberg News, “AI-powered Zambian copper mine may become world’s third largest”, MINING.COM (9 Feb 2024)
- DGMS (Tech) Circular No. 08 of 2020, Recommendations of the 12th National Conference on Safety in Mines (29 Apr 2020), in DGMS circulars 2018–2024 (PDF)
- KnackLabs, GIS Portal case study: “A drone survey the whole mine can open”
- KnackLabs, MineOps case study: “Running an entire coal mine on one platform”
- KnackLabs, FDE as a Service
- Mining
- AI use cases
- Autonomous haulage
- Predictive maintenance
- DGMS compliance
- Forward deployed engineering
