A concentrator is designed around an ore profile that rarely arrives exactly as expected. Its flowsheet is based on hardness, grade and mineralogy estimates from drill core and metallurgical testwork. In deposits that testwork covers only about 5% of the ore body. The rest is estimated, then averaged in the pit and again on the run-of-mine (ROM) pad before the ore reaches the mill. At operations more than 80% of production decisions are still made largely on experience rather than real-time information. That is not a criticism of the people running the plant. It reflects how little reliable information about the ore reaches them in time to influence a decision.
In 2026 some of the immediate returns from AI in mining come from closing this information gap. Autonomous fleets and exploration models attract plenty of attention. Both can be investments. For producing mines though a more immediate opportunity sits between the pit and the plant. Information about the ore gets lost at each handoff often before the mill can use it.
Where AI Is Being Applied Across the Mining Value Chain
AI applications now appear across much of the mining value chain. Their payback periods, capital requirements and day-to-day owners can be very different.
During exploration machine learning combines drill logs, geochemistry, geophysics and satellite imagery to build prospectivity maps that help teams decide where to drill. AI for mineral exploration can influence which targets receive attention. The return takes time. A promising target still needs drilling, modeling and permitting before it can contribute to production.
Autonomous drills and driverless haul trucks are already operating at open-pit mines. Their main benefit is repeatability since equipment can keep moving through shift changes with cycle times that're less affected by handovers. Introducing this capability across a site however calls for capital supporting infrastructure and a change program that can take years.
Predictive maintenance and computer vision sit between exploration and full autonomy. Data such as vibration, oil condition, temperature and equipment load can point to a developing fault in a truck, crusher or mill before it causes a breakdown. Cameras are also used to monitor PPE and exclusion zones assess blast fragmentation and track material on conveyors.
Planning and processing tools often start with information a mine already collects. They can estimate what feed is on its way and suggest operating targets for the circuit. Because they use existing records of new equipment their cost may fit within an operating budget. Generative AI is also used to summarize reports and shift handover notes.
Why Ore Knowledge Degrades Between the Pit and the Plant
Each part of a mine may have information for the task it handles. The difficulty emerges at the handoffs, when material and its associated data move from one team or stage to another.
A block model might capture grade and hardness at a scale of a metres. After blasting, loading and hauling that same ore is often tracked by truckload or shift. Once loads from faces are combined on the ROM pad the records may show only an average for the pile. By the time the material reaches the crusher the plant may have a grade estimate but little detail about metallurgical response. Valuable information has been averaged out along the way even when each team has followed its process.
The effects show up in processing. When hardness is uncertain operators may choose grind targets. Reagent doses are set for the expected average than the ore actually moving through the circuit. Recovery can fall when a different ore domain arrives. The reason may not become clear until month-end reconciliation. By then the block model, stockpile records and mill data can tell stories about the same material.
That makes the mine-to-mill chain a practical place to focus. The information needed to improve plant performance often exists somewhere on site; the challenge is connecting it to the person or system making the decision at the time it matters.
Where the Mine-to-Mill Gap Costs the Most
Long-Term Stockpiles That No Longer Match the Books
Stockpiles can build up over years layer by layer as mining moves between pits and phases. Yet many piles are still represented by one grade masking both their value and their risk. NTWISTs OreMax uses survey, assay and dispatch records to rebuild stockpiles as resource-grade block models. Across deployments the model has reduced block-level grade variability by an average of 30% to 50% while ore misplacement has fallen by much as 98% depending on the site.
At one gold operation the stockpile records suggested that the remaining material was all grade. A block-level model instead identified around 0.1 million tonnes of medium- to high-grade ore that had not been accounted for with an estimated value of US$11 million. Geology teams using OreMax also report saving 20 to 80 hours each week previously spent reconciling records. A gold producer in Peru that moved from open-pit mining to reliance on stockpiled ore used NTWISTs GPS-based stockpile modeling and simulation to keep mill performance steady through the transition.
The ROM Pad and Short-Term Feed Forecasts
Dispatch systems often already connect each truckload to its dig location, which means the raw information for block-level tracking may be available. NTWISTs DynaMax combines dispatch records with block models and assays to manage the pad by block than by a single pile average at resolutions from 1 by 1 metre up to 20 by 20 metres. Across the MineMax suite feed forecasts can look one to seven days ahead. Include confidence bounds.
DynaMax deployments have cut feed-predictability error by 30% to 80%. Typically lifted recovery by 0.5% to 2%. At a gold mine producing 100,000 ounces annually replacing ROM-pad averages with block-level forecasts increased recovery by 1.6% equivalent to roughly US$2 million a year without changing the plant equipment. Identifying misrouted or diluted material sooner has also helped recover value of up to US$250 per ounce that might otherwise have been lost.
Processing Circuits Running on Unmeasured Variables
Some of the important processing variables cannot be measured directly. True grind state effective hardness and mineral response may be difficult to observe while laboratory results can arrive hours after the operating decision has already been made. NTWISTs MillMax uses sensors to estimate these variables and show a confidence range for each estimate. It then recommends operating targets while the existing control system keeps the circuit stable.
At a nickel refinery using a -stage kiln circuit the approach progressed from advisory estimates to closed-loop optimization layered over the existing advanced process control system. Nickel extraction reached 91.6%, the level in the plants available history while residue was 45% below the five-year average. At a base-metals refiner operating a high-pressure acid leach circuit the model increased potential profit during 42% of operating hours and corrected off-target conditions during another 17%. The changes helped stabilize the process and reduce downtime within 90 days. Across processing deployments results include recovery gains of 0.5% to 5% reagent savings of up to 30% and energy reductions of up to 22%. Other sites have recorded a 20% improvement in grind target compliance and savings of up to $250,000 a year at a nickel concentrator through better feed predictability.
What Makes a Mining AI Model Trustworthy Enough to Act On
Operators are more likely to set a recommendation when it conflicts with what they see on the floor offers no clear explanation or gives no indication of how reliable the prediction is.
First a model should show uncertainty. A forecast with a range gives a metallurgist a sense of when to act and when to wait for an assay than a single number that looks exact. The model also needs operating boundaries. MillMax recommendations stay within limits set by the operations team than proposing a setpoint the site would not accept. Finally control should be introduced gradually. Deployments begin in advisory read- mode; the operation decides whether. When to close the loop after seeing how the recommendations perform.
Explainability matters in day-to-day use. Operators are more likely to accept a suggestion to coarsen the grind when the system connects that recommendation to the hardness of the ore.
For mining operations, trustworthy AI also depends on data quality, system reliability, explainability and cybersecurity. The International Society of Automation discusses these considerations in its guidance on safe and reliable industrial AI deployment.
The Data an Operation Already. What It Is Missing
Most mines already collect enough information to start exploring AI without installing new hardware. Typical inputs include the following.
- Block models and grade control data
- Fleet dispatch and fleet management records
- Stockpile surveys from drones or ground teams
- Laboratory assays
- Plant historian and control system data
The issue is rarely a shortage of data. It is often a question of timing, consistency and whether records from systems can be matched reliably. An assay may arrive hours after the ore has passed through the plant. A dispatch log may attribute a load to the source while a stockpile can change substantially between surveys. Industrial AI in mining needs to account for these imperfections, flag conflicting inputs and reflect them in confidence ranges of assuming every record is correct.
Connectivity can be another constraint at sites. With edge deployment models can run close to the operation. Support on-site decisions without depending on a continuous connection to a distant data center. NTWIST has worked with a semiconductor company on real-time ore-grade optimization at the edge helping reduce waste and speed up decisions.
Security is just as important here as with any technology (OT) connection. MineMax offers role-based access and single sign-on protects data in transit and at rest keeps an audit trail and supports location- data rules. It can run in the cloud in a customer-managed cloud or, on the customers equipment.
Starting with a First AI Project
An AI project has a chance of working if it deals with a problem that costs a lot of money and can be measured and if someone is in charge of the result. Examples are when the ore that gets to the mill is not predictable when no one is sure what is in the stockpile or when valuable minerals keep getting lost in processing.
The test should be kept small. One stockpile, one ROM pad or one part of the processing system can be enough to check how good the predictions are and whether workers find the suggestions helpful. A MineMax project usually starts with a 45-minute meeting and a look at the data thats already there. MillMax projects happen in five steps over 6 to 16 weeks. Typically return their investment in under 24 weeks with benefits appearing before the rollout is fully done.
Before the test starts the site should decide on the measures and record where they stand now. Common ones are recovery, reagent use, forecast error and reconciliation variance. Across MineMax projects NTWIST has recovered than $35 million in annualized value and the suite targets an annual uplift of roughly $1 million to $20 million per site depending on operation size. Payback usually comes within 45 days of production use and many sites reach operational clarity in about 90 days. Predictions used for planning and moving the ore have been 95% accurate. When the test shows it works the same data can be used for modules like PlanMax for constraint-aware blend planning and at more sites.
Things to Think About When Planning
AI can't fix sampling. If the samples of the ore don't show what is really there the model will learn the mistake. Still give results that look confident. Sampling quality should therefore be checked first.
A model also needs someone to take care of it. As mining moves into areas the predictions can drift if they are not checked against what actually happens. A geologist or metallurgist on site should be assigned to keep an eye on it than leaving that to the vendor.
Deciding where to start involves some choices. Models for exploration and machines that work on their own can bring a lot of value. They need a lot of money or take a long time. Projects that work from the mine to the mill are more focused use data that is already collected and can give results faster.
People who know how to run the plant are part of the solution, not the problem. A model that learns from years of operating data can help carry that knowledge from one shift to the next. It works best when the people who know the process are also checking the suggestions.
AI in Mining Trends for 2026 and Later
In the coming years more mines may link geology, planning and processing through a shared decision layer that follows the ore from the mine to the plant. Digital copies of stockpiles, ROM pads and processing circuits are moving beyond one-off studies. As they get updated closer to time planners can try out different plans before sending the ore to the mill.
AI agents are starting to take over some planning tasks. For example an agent could see a change in the forecast. Get a revised blend ready for someone to check and approve. As costs fall autonomy may expand into loading, underground work and smaller mines. The need for copper, nickel and lithium is also helping explorers use AI to check records and satellite images when looking for new deposits.
For sites receiving ore that differs from the plan the best next step is usually to match existing data with one problem. The MineMax suite of tools is made for this. To talk about an operation Book a Clarity Call with the NTWIST team.
Frequently Asked Questions About AI in Mining
What is AI in mining?
AI in mining includes machine learning, computer vision and soft sensors that use data from the rock the machines, the lab and the plant to predict what might happen next and support decisions across the mining process.
How is AI used in the mining industry today?
Current AI use cases in mining include exploration targeting, autonomous haulage, predictive maintenance, safety monitoring with computer vision, stockpile modeling, ROM pad and feed forecasting and processing optimization. NTWISTs MineMax suite focuses on the mine-to-mill chain.
Where should a mine start with AI?
Most operations see the return from a narrow pilot on a measurable mine-to-mill problem, such as stockpile uncertainty or ore variability reaching the plant.
Does artificial intelligence, in mining require sensors?
Not usually. Block models, dispatch records, stockpile surveys, assays and historian data are typically enough to begin and soft sensors can estimate variables that are not measured directly.
How does AI improve mining safety?
Autonomous equipment removes people from haulage computer vision detects PPE and exclusion zone breaches and fatigue monitoring alerts operators before an incident.
About the Author
Pramodh Chandrashekar is an industry and technology expert and Senior Researcher at NTWIST. His expertise includes industrial AI, mining and manufacturing technology, digital transformation, and operational optimization.
He focuses on applying AI and data-driven technologies to improve operational efficiency and business performance.
LinkedIn: Pramodh Chandrashekar
