AI Training for Mining & Metals
A mine site runs on shift handovers, work orders, inspection notes, incident chronologies, permit evidence, and supplier correspondence about as often as it runs on equipment, frequently moving between a remote site and a head office in different languages. This training shows your teams where generative AI drafts, summarizes, and organizes that text reliably, and where it has no business being: ore-body, safety, and engineering calls stay with the qualified professionals accountable for them.
- modules
- 6
- hours
- 12
Why this training
Why AI in this industry
Add the ESG narrative and permit renewal, plus the supplier email chain nobody assigned to anyone in particular. A mine site's actual output each day is ore and a stack of paperwork that has to be just as accurate: shift handovers, work orders, inspection notes, incident chronologies, permit evidence, supplier correspondence. Engineers, supervisors, and specialists write almost all of it in between running the site, which is the job they're there for.
Get a technical procedure or a safety chronology wrong and it isn't a proofreading problem. A mishandled geological or safety input can create exposure that outlasts the meeting where someone first typed it. This training doesn't teach AI to run equipment, estimate reserves, optimize a haul route, or authorize a safety call. Those decisions stay with your qualified engineers and geologists, with safety officers holding their line.
It teaches operations, maintenance, HSE, permitting, and procurement teams to draft, summarize, and organize the documentation around those decisions, with a named reviewer at every handoff. Large language models are genuinely good at this kind of text work, and most sites haven't organized it yet. That gap is where the two days go.
Over two days, split around shift patterns, teams turn field notes into production summaries, work orders into maintenance records, and incident notes into reviewable chronologies. They also work on permit and ESG material with multilingual correspondence between site and head office. Manufacturing-adjacent operations may find AI training for manufacturing covers the value chain inside formal plant quality systems. The AI training catalog follows the same rule throughout: each program adapts to approved tools for your commodity and site structure.
Shift reports don't stop when the shift does
Shift reports, work orders, inspection notes, toolbox talks, and permit evidence pile up daily, written by supervisors and engineers who'd rather be running the site than writing about it. A first AI-assisted draft clears the backlog fast, and the record still meets the same bar it always has.
Ask around and you'll find two or three people who know why
A handful of veteran operators and technicians usually know both why a procedure exists and how last month's recurring fault got fixed. AI doesn't replace that knowledge, but it turns their notes, radio logs, and one-off answers into something the next shift can search instead of asking around.
HSE files get read by people who weren't there
An incident report gets read by a regulator, an insurer, or a community group who wasn't on site, and it has to make sense to them anyway. Routine audits show up on their own schedule and expect that same level of care. Building either one under time pressure, from partial notes nobody wrote at the same time, is where gaps quietly become assumptions.
Permits and ESG paperwork don't wait for a slow month
Permit conditions, sustainability narratives, regulator updates, and community messages compete with tenders, RFQs, and supplier follow-ups for the same small team's time, often in two or three languages between site and head office.
The model leaves geology and set-points alone, including safety sign-off
Geological models and engineering set-points sit outside what a drafting tool should ever decide, as does safety authorization. Get a number wrong in a technical or safety document and it's a real risk. The line gets drawn in plain terms before day one starts. The team sees what generative AI may draft and what only a qualified reviewer signs off on.
Syllabus
Training syllabus
Telling a language model from a control system
90 minBeginner
The site already runs predictive, vision, and control systems for exploration, processing, and dispatch, and generative AI stays in its own lane alongside them. Desk and field-support work is where the model actually helps. Technical and safety content is where it starts inventing detail instead of raising a flag.
- The predictive, vision, and control systems already on site, kept apart from a language model
- Where large language models help desk and field-support work, and where they don't
- What a hallucinated fact looks like inside a technical or safety document
- Engineering and geological decisions stay with qualified staff, as do safety calls
- A documentation-first pilot
Prompting your site can trust
90 minBeginner
Site procedures, manuals, and permit conditions use a repeatable prompt shape: role, context, approved source, output format, and review, with the model required to flag uncertainty rather than offer a fast guess.
- Mine documents get the same five-part structure: role, context, source, output, review
- Querying procedures without guessing
- Asking the model to flag uncertainty instead of inventing a missing detail
- A searchable entry built from a foreman's or geotechnician's notes
- A reviewed prompt library your site keeps using after the workshop ends
Shift, production, and maintenance paperwork, handled faster
150 minIntermediate
Operations and maintenance teams use structured field notes for shift handovers plus production and maintenance records. Every workflow ends with a named reviewer before a draft reaches a shift or a work order closes out, no matter how busy that reviewer already is.
- Shift handovers and daily production summaries pulled from structured field notes
- Work-order and inspection-note synthesis for maintenance and reliability teams
- Ordering a maintenance backlog by what needs doing first
- Toolbox-talk and procedure drafts ready for a supervisor's sign-off
- What a shift supervisor checks before any draft goes out
- Consistent handovers across sites
HSE files and the operational-learning loop
120 minIntermediate
An incident timeline gets pieced together from confirmed records only, never a guess about what probably happened. Lessons-learned notes and safety communication for crews and contractors get the same discipline, with a clear route for anything the model shouldn't decide.
- Piecing together an incident timeline from confirmed records
- Drafting lessons-learned notes that don't assign causality ahead of an investigation
- Safety communication for crews and contractors during shift briefings
- Audit and compliance evidence organized for regulator and internal reviews
- Anything safety-critical and uncertain goes to a qualified reviewer
Permits, ESG, procurement, and the people on the other end of the email
150 minIntermediate
Permitting, sustainability, procurement, and community-relations teams draft permit-condition summaries, ESG narratives, tender and supplier correspondence, and multilingual updates between site and head office. Each one carries a clear sign-off step, and a regulator or a community group sees the result only after that sign-off happens.
- Permit-condition tracking and evidence summaries for renewal cycles
- ESG and sustainability narratives drafted from approved data sources
- Community and regulator communication a site can stand behind
- Writing tender and RFQ material with supplier correspondence for procurement teams
- Multilingual site-to-head-office reporting
- Naming who signs off before community- or regulator-facing text goes out
Governance and a 90-day adoption plan
90 minAdvanced
This closing module brings individual technique into day-to-day site practice. Teams classify sensitive data and keep it out of unapproved tools, then decide who reviews what before it goes anywhere. It ends by naming people across shifts and sites who keep the momentum going, plus a phased adoption plan.
- Classifying geological, safety, employee, and commercial data
- Keeping site data out of consumer AI tools: OT and cyber boundaries explained in plain terms
- A risk-mapped review matrix
- People across shifts and sites who keep the momentum going after the workshop
- A 30/60/90-day adoption plan to walk out with
Outcomes
Outcomes & audience
What you will learn
- Turn a shift handover into a record worth trusting
- A ranked maintenance backlog
- An answer with its source
- Piece together an incident timeline from confirmed records, causality left to the investigation
- Draft lessons-learned notes ahead of the formal finding
- Prepare permit and ESG drafts for community sign-off
- Write tender and RFQ material with supplier correspondence across languages
- Classify sensitive site data, then walk out with a 30/60/90-day plan
Who should attend
- Mine operations excellence and technical services leaders
- Maintenance and reliability teams in processing or plant work
- HSE managers and officers working with incident investigators
- Permitting and ESG teams responsible for community relations
- Procurement and supply-chain teams responsible for contracts
- Digital transformation leads and corporate academy or L&D leaders
Format
Training format
The full course runs two days. Sites typically break it into shorter blocks to work around shift patterns and remote-site schedules. Onsite and live-online delivery both use exercises built around your approved tools fitted to your commodity and site structure.
- Format
- Onsite or live online
- Duration
- 2 days (about 11.5 hours, can be split across shift patterns)
- Group size
- Up to 20 participants per group
- Language
- English or Turkish
- Materials
- Prompt library, document templates, review checklists, and exercise workbook
- Certificate
- Certificate of completion
About Zeo
Why Zeo
Zeo started in 2011 and now works out of San Francisco, Istanbul, Ankara, and Lisbon. We run Copilot Academy and organize Digitalzone, an international digital marketing conference. This program draws on the 10+ years of consulting and training work behind that, applied to corporate AI adoption.
- 2011founded in Istanbul
- 10+years of consulting and training experience
- 3offices: San Francisco, Istanbul, Ankara, Lisbon
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