How to Use AI for Mining

If you run a mine, finance one, advise on one, or invest in one, you spend most of your week doing analytical work that has barely changed in twenty years. Reading technical reports. Reconciling production data against plan. Validating reserve estimates. Building NPV models. Pulling cost data into benchmarks. Running scenarios on commodity prices. Writing memos. Reviewing memos. Reading more reports.

The data is there. The analytical methods are well-established. What is broken is the connective tissue between them - the work of getting data out of one system, into another, formatted properly, reconciled against another source, summarised for someone more senior, and then re-done six weeks later when something has changed.

That connective tissue is where AI is now landing. Not as a replacement for the analytical work itself, and not as some autonomous "mining intelligence" that operates without human judgement, but as the layer that collapses the cost of asking analytical questions against your assets to nearly zero. This article walks through, in detail, what that actually means across the workflows that mining companies, mining-focused funds, and mining advisors actually run.

A mid-tier mining company with five operating assets has, on average:

• Three or more historic technical reports per asset (NI 43-101, JORC, SAMREC, internal feasibility studies). Each runs 200-400 pages. Each contains a resource and reserve statement, a mine plan, a metallurgical recovery model, capex and opex assumptions, a financial model, environmental and permitting commitments, and dozens of supporting appendices.
• Years of drilling data sitting in acQuire, Datashed, or a SQL database a contractor set up in 2017. Hundreds of thousands of assay records, lithology codes, structural readings, density measurements.
• Production data in a historian (PI, Wonderware, GE Proficy). Tonnes mined, tonnes milled, head grade, recovery, concentrate grade, payable metal, by hour, by shift, by day, by month, going back as far as the operation has been running.
• Resource models in Vulcan, Datamine, Surpac or Leapfrog. Block models with dozens of attributes. Multiple grade interpolation runs. Geomet domains. Geotechnical domains.
• Mine plans in Whittle, MineSched, Deswik. Pit-by-pit, pushback-by-pushback schedules. Stockpile strategies. Equipment fleet assignments.
• Financial models in Excel. Built by a different person every two years. The dispatch one nobody fully understands. The valuation one where the discount rate is hard-coded somewhere and a price deck change requires three hours of careful editing.
• Cost data in SAP, JD Edwards, or a custom ERP. Maintenance work orders. Spare parts inventory. Contractor invoices. Power consumption.
• ESG data in a separate system because the sustainability team needed it that way. Tailings monitoring. Water balance. GHG inventory. Community spend.
• Hundreds of unstructured documents - board memos, due diligence reports, consultant studies, geotechnical assessments, metallurgical test work, exploration target reports - sitting on SharePoint, Box, or an email folder.

Now ask a question that crosses two of these systems. "What was the actual head grade variance last quarter against the resource model expectation, and is that variance large enough to warrant a model update?" That is a question with a clear answer somewhere in the data. To produce the answer with current workflows, you need a geologist to pull the model expectations, a production engineer to pull the actuals, both of them to align on the appropriate spatial reconciliation, and someone to write it up. That is a multi-day exercise. So the question gets asked once a year, in the budget cycle, when the answer is already six months stale.

Multiply this by every cross-domain question that is worth asking, and you get a sense of how much analytical productivity is being left on the table. That is the actual unlock. AI does not replace the geologist's judgement on whether to update the resource model. It replaces the three days of data gathering, formatting and reconciliation that have to happen before that judgement can be exercised.

There are roughly twelve distinct analytical workflows that consume the bulk of mining analytical time. AI changes the economics of all of them, but the magnitude varies. Below I walk through each, what the current workflow looks like, where AI lands, and what the "after" state actually feels like.

Current state: a senior associate at a fund, an investment bank, or a consultancy is given a technical report and three weeks. They read it. They build an Excel summary tab pulling out the key parameters - tonnes, grade, recovery, mining method, strip ratio, capex, opex, sustaining capex, royalties, taxes, expected production by year. They cross-check the financial model against the report's stated NPV. They produce a memo flagging assumptions they think are aggressive or inconsistent.

Three weeks of human time, billed at consultant or banker rates, produces a document that becomes stale the moment the next technical update is published.

What AI does: the entire data extraction step is now mechanical. A modern document understanding pipeline can read a 350-page NI 43-101 and produce a structured representation of every key parameter in under an hour. Resource and reserve statements with classification, grade, tonnage, contained metal. Production schedule by year. Capex breakdown by category. Operating costs per tonne mined, per tonne milled, per ounce produced. Recovery curves. Royalty and tax structure. Mining and processing method descriptions. All of it linked back to the page and section it came from.

What the senior associate still does: judgement. Is the metallurgical recovery assumption realistic given the head grade and mineralogy? Does the strip ratio profile make sense given the pit shell? Has the consultant who signed off on the resource been involved in any of the recent class actions? Those are the questions that matter, and they are the questions you want a smart human spending their day on, not the data extraction that gets them in a position to ask the questions.

The compression is roughly 20:1. A workflow that took three weeks now takes a day or two, and the day or two is spent on the analytical questions that actually move investment decisions, not on the assembly that precedes them.

Current state: when an operator publishes an updated resource statement, or a fund is evaluating an acquisition target, somebody has to validate that the numbers are credible. This means looking at the drill spacing relative to the classification rules, checking the grade interpolation parameters, comparing the estimated grade-tonnage curve against the actual mined grade where there is operating history, and stress-testing the cut-off grade and metallurgical recovery assumptions.

In a fund context, this typically gets outsourced to a technical consultant - SRK, Wood, AMC, Mining Plus - at a cost of $50,000 to $250,000 depending on scope, taking four to twelve weeks.

What AI does: the data assembly and consistency checking is automated. The platform reads the resource report, the historic drilling data, the production reconciliation if it exists, and the metallurgical test work. It identifies inconsistencies - a Measured classification on a block with drill spacing that is wider than the classification rules say it should be, a recovery assumption that has not been updated since the last test work, a cut-off grade calculation that does not reflect current commodity prices and operating costs.

What the qualified person still does: signs off on the resource. That is a regulatory requirement and it is not changing. But the qualified person now starts from a synthesised, validated baseline rather than from raw drill logs and a spreadsheet. The compression here is 5-10:1 on the validation step, with a quality improvement because the consistency checking is exhaustive in a way humans rarely manage.

Current state: every month, the operations team produces a reconciliation between what the resource model said should have come out of a particular block, panel or stope, and what actually came out. This is the most important diagnostic in mining. A persistent positive reconciliation means the model is conservative and you are leaving value on the table in the plan. A persistent negative reconciliation means the model is optimistic and your reserves are overstated.

Most operations do this badly. They do it monthly, manually, in Excel, and they do it at a level of granularity that makes spotting structural patterns difficult. Cross-referencing reconciliation against geological domains, mining method, equipment, or grade range to find the actual driver of the variance takes another layer of analysis that rarely happens.

What AI does: the reconciliation runs continuously, at the level of granularity the data supports, and the variance gets attributed automatically to the most likely driver. Was it a grade variance, a tonnage variance, or both? Is it concentrated in a particular domain? Is it correlated with a particular operator, shift, or fleet? When did it start? Does it match anything that happened in the geological model - a re-domaining, a parameter change, a new dataset?

The geologist still decides what to do about it. The metallurgist still has to think about whether the recovery assumption needs revisiting. But the diagnostic that triggers their attention is now happening on the cadence of the data rather than on the cadence of monthly reporting.

Current state: the life-of-mine plan is produced once a year as part of the budget cycle. It is generated in Whittle or Deswik using a price deck that was set six months before the plan is published. By the time the plan is being executed, the price deck is wrong, the cost base has moved, and the plan that minimised value-at-risk three months ago is no longer optimal.

Re-running the plan is a multi-week project requiring the planning engineer, the geotechnical engineer, the metallurgist and the mining engineer to align. So it does not happen. The plan stays in place, decisions get made off-plan but not formally, and the gap between the plan and reality grows until the next budget cycle.

What AI does: it does not replace Whittle. The pit optimisation algorithms are well-established and the engineering judgement around inputs is non-trivial. What AI does is make the inputs current. It maintains an updated view of the resource model, the cost structure, the price deck, and the operational constraints, and surfaces when the deviation is large enough that the plan should be re-run. It also runs sensitivities far more quickly than the manual workflow allows - at $4.50/lb copper versus $5.50/lb copper, what is the optimal cut-off, the optimal pushback sequence, the optimal stockpile strategy?

The mining engineer still has to look at the result and make engineering judgements about whether it can actually be executed. But the questions they are answering shift from "what does the plan say" to "given how the world has changed, what should the plan now be."

Current state: a fund or operator has a valuation model on each asset they care about. It is in Excel. It was built by an analyst who has since left. The discount rate is hard-coded in three different cells. The price deck assumption is on a hidden tab. To rerun the valuation under a new commodity price scenario takes between two hours and two days depending on how brittle the model is. So nobody does it on the cadence that matters.

What AI does: it runs the valuation continuously on a model whose inputs are linked to the underlying technical and operational data. When the resource model updates, the production schedule updates. When the production schedule updates, the cash flow forecast updates. When the price deck moves, the NPV moves. When operating costs move, the breakeven moves. This is not magic - it is the financial model that should always have existed, but didn't because the cost of building and maintaining it was too high.

The analyst still validates the result. The investment committee still makes the call. But the committee now sees a current valuation rather than a snapshot from the last quarterly review.

Current state: a target asset comes onto the market. The fund has 30-60 days for first-round bidding, sometimes less. The data room contains 800-1,500 documents. Reading the data room and producing an investment memo is a two-month exercise even with a full deal team. Half of the work is just understanding what is in the data room and what is missing from it.

What AI does: it reads the data room. Within hours, you have a document inventory, an extracted parameter set for the asset, a draft valuation under your price deck, a list of identified risks, a comparison to recent comparable transactions, and a list of things that should be in the data room but are not. The deal team then spends their time on the actual investment thesis - the questions of whether the asset is mispriced, what the operating upside is, what the integration looks like - rather than on the document review that has to happen before any of those questions can be addressed.

The compression here is the most economically significant in mining M&A. Teams that have this capability built into their workflow will see opportunities and act on them at a cadence that teams without it cannot match. Over a 24-month window, in an active deal environment, this is a structural competitive advantage.

Current state: a multi-asset operator or a resource fund has a capex budget. They have to allocate it across their portfolio. The allocation gets done annually, in spreadsheets, with a heavy bias toward incumbency - assets that got capex last year tend to get capex this year, because nobody has the analytical capacity to seriously revisit the allocation.

What AI does: it runs the actual allocation problem. Given the current valuation of each asset, the marginal NPV of an additional dollar of capex by asset, the constraints (commodity exposure, geographic concentration, total capex budget, liquidity requirements), what is the allocation that maximises portfolio NPV? What is the efficient frontier between expected return and risk?

This is a problem that is solvable in closed form for some structures and via Monte Carlo for others. The reason it is not currently solved at most operators and funds is not analytical - it is data. The inputs to the optimisation cannot be assembled in time. Once they can, the optimisation is a tool the CFO uses on a Tuesday, not a project they commission once every three years.

Current state: every operator wants to know where they sit on the industry cost curve. There are commercial datasets - Wood Mackenzie, S&P Global, Skarn Associates - that publish C1 and AISC by mine, but the data is lagged and the methodology varies. Internal benchmarking against a peer set requires hand-coding each peer's reported numbers into a comparable framework, accounting for differences in by-product treatment, depreciation policy and royalty structure.

What AI does: it ingests the public reporting from peer operations - annual reports, quarterly results, technical reports, sustainability reports - and constructs a normalised view of operating costs at whatever level of granularity the data supports. It surfaces structural drivers - power cost, labour cost, strip ratio, processing complexity - rather than just headline numbers. It updates as new disclosures come in.

The cost engineer still does the interpretation. But the data assembly that used to take a quarter now takes an afternoon.

Current state: the maintenance team manages a fleet of large mobile equipment - trucks, shovels, drills - using a CMMS like SAP PM or IBM Maximo. They have telemetry from the equipment via Modular, Wenco, or similar dispatch systems. They have hundreds of thousands of work orders going back years. They cannot easily answer questions like: what is the actual MTBF on our 793F fleet versus the OEM number, has it changed since we switched to that aftermarket filter supplier, and is the change material to fleet replacement timing?

What AI does: connects the maintenance data, the operational data, and the cost data so that questions like that have answers. This is not predictive maintenance in the buzzword sense. It is making the equipment performance data usable for the kinds of questions the maintenance manager and the asset manager need to answer.

Current state: ESG data lives in a separate system because the sustainability team set it up that way. Tailings monitoring sits with the geotechnical engineer and the dam safety officer. Water data sits with the environmental team. None of it talks to operations or to finance, even though the financial implications of an ESG event can be existential.

What AI does: connects the ESG and operational data layers so that the financial and risk implications are visible. If tailings deposition is running ahead of plan and the construction schedule on the next raise is tight, that has financial implications. If water consumption is running above permit limits, that has operational implications. If a community grievance is escalating, that has portfolio implications. None of these need ML in any sophisticated sense - they need the data connected and visible.

Current state: a development project has a critical path that is dominated by permitting and stakeholder consultation. The schedule lives in a Primavera P6 file. The dependencies on regulatory milestones are tracked manually by the permitting lead. When a milestone slips, the impact on the overall project economics is not re-run for weeks.

What AI does: maintains the linkage between the project schedule, the technical parameters that depend on it, and the financial model. A six-month slip on the environmental permit is not just a calendar event - it is an NPV impact, a debt drawdown timing change, and a contractor mobilisation cost. Surfacing all of that at the moment the slip is identified, rather than at the next monthly project review, is the unlock.

Current state: a public mining company has to disclose material changes to its asset base, technical parameters and reserve estimates. The process for doing so is heavy. It involves lawyers, qualified persons, the IR team, and management sign-off. The threshold of materiality is sometimes ambiguous. The disclosure is sometimes late.

What AI does: continuously surfaces the underlying technical and operational changes in a way that lets the disclosure team make better-informed materiality decisions earlier. It does not replace the legal judgement. It improves the information set the legal judgement is based on.

The change is not "we use AI to do mining things faster." The change is structural. Once the data is connected and the analytical layer above it is functional, the cadence of the entire business shifts.

Capital allocation becomes a continuous question, not an annual one. The CFO can re-run the portfolio allocation any Tuesday and the answer will reflect the world as it is, not the world as it was at last September's planning offsite. The board sees current numbers. The investment committee makes decisions on assets that were valued this morning.

M&A becomes a faster game. The team that can read a data room in a day will see opportunities that the team that takes a month to do the same work will miss. Banker-led processes with formal timelines do not change, but the work that happens between the teaser and the bid changes character entirely.

Operational decisions tighten their loop. The reconciliation that used to be a monthly exercise is now continuous. The mine plan that used to be re-run annually is now re-run when something material moves. The cost benchmarking that used to be a quarterly project is now a dashboard.

The technical work stays human. Geologists still decide what the resource model should look like. Metallurgists still interpret test work. Mining engineers still build the plan. Reserves still get signed off by qualified persons. Boards still approve capex. None of that goes away - it cannot, both for engineering and regulatory reasons.

What changes is the ratio of analytical time to assembly time. Most mining technical professionals currently spend the majority of their working week on data assembly and reporting. After this shift, they spend the majority of it on the actual technical work. That is a productivity change of roughly 3-5x on the most expensive labour in the industry.

What I have described above is not something you build in-house from a stack of cloud services and a few language models. The reason is that the value is in the integration - the connected data layer, the modular analytical engines, the ability to ask cross-domain questions and get answers traceable back to source. Building all of that, properly, takes years. Buying the foundational AI capabilities and stitching them into your existing stack does not work, because your existing stack is the problem.

This is what we built Honeycomb for. It is an intelligence platform for mining and energy asset operators, owners and advisors. The architecture is a semantic digital twin of each asset - a structured, queryable representation of everything the asset is and does - sitting on top of a unified data layer that ingests technical reports, drilling data, production records, financial models, operational data, and the rest of the mess described above. On top of that sit the modular analytical engines: resource and reserve workflows, decline curve and production forecasting, pit optimisation linkage, NPV and scenario modelling, portfolio optimisation, due diligence acceleration, benchmarking. All outputs are traceable to source documents, page-level. The interface is natural language, so the people who need answers do not have to wait for someone else to build them a spreadsheet.

The operators using Honeycomb are not buying it because it is novel. They are buying it because the alternative - continuing to run the mining analytical stack the way it has been run for twenty years - is no longer competitive. Companies whose analytical cadence matches their data cadence will outperform companies whose analytical cadence matches their reporting cycle. That is the entire game, and it is opening up now.

If you are reading this because you run mining assets, finance them, advise on them, or invest in them, the first thing to do is not to commission a digital strategy review. It is to take a single workflow you currently run more slowly than you should, and run it through Honeycomb. A field-level production reconciliation. A target asset's data room. A portfolio capex allocation. A reserve update.

Free trial at honeycomb.sirca.io. Upload one asset's data, see what the platform produces, and decide for yourself whether the cycle time looks the same as the one you currently run.

The companies that get this right over the next two years will operate differently from the ones that do not. Not at the margin - structurally. The window for getting on the right side of that gap is open now.