How to evaluate Mining & natural resources AI partners
Look for a comparable deployed workflow and the team that actually built it. A client logo or polished model demo is weaker evidence than the integration, acceptance test, failure handling and support record.
| Evaluation criterion | What to ask / useful evidence | Warning sign |
|---|---|---|
| Previous industrial AI implementation | Which comparable asset signals and work-order integrations did you personally deliver?A redacted architecture, chronological evaluation and named responsibilities of the proposed team. | A generic anomaly chart presented as evidence of mining deployment. |
| Requirements before model selection | How will you distinguish equipment condition from ore, duty-cycle or connectivity changes?A data assessment with operating modes, fault-label review and estimation assumptions. | Model training is priced before signal access or maintenance labels are examined. |
| Transparent site and platform costs | Which historian licences, gateways, inference services and visits are outside your fee?An itemized deployment bill with access dependencies and expansion charges. | Proprietary connectors and mandatory cloud subscriptions emerge during installation. |
| Supportable software ownership | Can our engineers diagnose missing data and recover the advisory service?A site runbook, tested rollback, code/licence register and staff handover exercise. | All troubleshooting depends on an unavailable remote developer. |
What should you ask for in a demo or free trial?
- 01Check comparable AI work
- 02Test a representative case
- 03Compare the full proposal
- 04Agree acceptance & exit
Ask whether a no-cost demonstration, sandbox trial or sample-data evaluation is available. A free consultation does not mean free implementation; many custom AI pilots are paid. Confirm any trial limit, data handling, ownership of outputs and charges before supplying material.
Make the demonstration relevant: Prioritize equipment inspections
- Bring this evidence
- Timestamped signals, operating modes, maintenance history and confirmed component outcomes.
- Ask to see this result
- An asset-level review queue explaining observations and data gaps.
- Introduce this failure
- Loaded, idle and maintenance states are scored as equivalent behavior.
- Discuss acceptance
- Useful warning time, faults missed and inspections with no finding.
- One workflow, a representative sample and a named reviewer.
- What is free, what is paid, usage limits and when billing begins.
- Access permissions, retention, deletion and permission to use sample data.
- The acceptance evidence, deliverables, ownership and the exit path.
Your Mining & natural resources AI hiring checklist
Use these evidence checks during supplier discussions. Download the topic-specific PDF for a review meeting or to keep a separate copy for each candidate.
- 01Choose a mining AI use case, asset class and engineering owner.
- 02Verify prior AI deployments with comparable signals and work-order integration.
- 03Audit clocks, fault labels, historian access and connectivity gaps.
- 04Document advisory outputs and explicitly approved equipment-command boundaries.
- 05Itemize gateways, API licences, inference, labeling and site support.
- 06Check accelerator rights, custom code and new-asset revalidation charges.
- 07Train engineers to review alerts, missing data and model rollback.
- 08Accept integration documents, acceptance tests and a locally usable maintenance runbook.
Your topic-specific checklist & buyer pack
AI scope, integration, cost questions, evaluation evidence, training and handover.
What should the proposal commit to?
| Milestone | Evidence required for acceptance |
|---|---|
| Audit one asset class | Clocks, asset mapping and an inspection baseline are accepted. |
| Replay historical decisions | Useful warning is distinguished from detection after failure was already obvious. |
| Run advisory shadow mode | The site can quantify additional work as well as confirmed faults. |
| Release to controlled operation | No vehicle or plant command is added outside the approved engineering process. |
Separate fixed scope, change requests, external subscriptions, support coverage and warranty fixes. Confirm source-code and data ownership, access to accounts, documentation, training, acceptance responsibility and an exit handover.
Staff training, documentation & handover
Name the people who will operate the AI workflow after launch. Put the following deliverables into the statement of work, with an acceptance owner and a separately identified training or handover allowance.
Staff training & adoption
- Train reliability engineers to challenge an AI alert using duty-cycle and inspection evidence.
- Rehearse lost connectivity and rollback without treating the AI as a protective system.
Documents & technical assets
- Signal catalogue, asset mappings, protocol permissions and model-input definitions.
- Chronological evaluation report with missed faults, warning time and review burden.
- Edge deployment, rollback, escalation and new-asset validation instructions.
Support & maintenance
- Name owners for historian changes, missing signals and model-performance investigation.
- Agree maintenance windows, remote access and charges for validating additional equipment.
AI partner evaluation FAQs
How can we verify a mining AI partner’s relevant experience?
Request an asset-specific implementation walkthrough with chronological evaluation, site-system integration and inspection findings. Ask the named engineers to explain their work and a failed deployment assumption. Useful evidence includes a redacted architecture, chronological evaluation and named responsibilities of the proposed team. Ask the proposed team which deployment and support responsibilities they personally held.
How should mining AI handover be accepted?
Have local staff interpret an alert, diagnose a missing signal and restore the approved service version. Receive architecture, mappings, test evidence, access inventory and escalation documentation. Train reliability engineers to challenge an AI alert using duty-cycle and inspection evidence. Agree maintenance windows, remote access and charges for validating additional equipment.
What should we ask a mining AI developer to show when an asset signal disappears?
Remove or delay a representative historian signal during the demonstration. The AI workflow should expose the gap, distinguish it from an equipment anomaly and preserve the last valid context without presenting stale output as fresh. Ask the engineers to trace an alert to the asset and inspection record, then show the engineer's review and recovery procedure. Verify who owns gateway access, missed-signal monitoring and site support.
Sources behind the hiring guidance
Technical references inform the workflow context. The hiring questions, checklist and acceptance guidance are RankGlobal editorial analysis. Verify the proposed team’s actual implementation role, references, evidence and contractual responsibilities before appointing a supplier.
Open the technical references (4)
- NIOSH: Automation and Emerging Technologies Partnership (opens in a new tab)
- NIOSH: Programmable Electronic Mining Systems—System Safety Guidance (opens in a new tab)
- AWS: Generative-AI agents for predictive maintenance (opens in a new tab)
- Mining machinery TinyML inference research (opens in a new tab)