Start small. Measure what matters. Improve before scale.
The Academy pilot pathway is designed to test practical AI capability with a real workforce before making broad claims or committing to large-scale rollout.
Evidence before expansion.
A controlled pilot gives employers and the Academy a chance to test whether the training is useful, realistic and appropriate for the workforce it is intended to serve.
Does it fit the work?
Test whether the language, scenarios and workflows reflect trades, mining and industrial supervision rather than generic office examples.
Does it improve useful work?
Measure time, clarity, structure, confidence and practical usefulness on appropriate lower-consequence tasks.
Does it improve review behaviour?
Test whether participants are better at recognising unsupported assumptions, confidentiality issues and high-consequence AI use.
A simple six-step pathway.
Choose the cohort
Identify a small group of tradespeople, supervisors or frontline leaders with realistic workloads and a clear reason for participating.
Measure before training
Agree suitable baseline tasks, time, quality indicators and behaviour measures before the pilot begins.
Deliver practical learning
Use realistic industrial scenarios, RISE™, consequence thinking, verification and platform-aware guidance.
Use it on real tasks
Participants apply the training to approved lower-consequence tasks such as communication, planning and information organisation.
Compare outcomes
Review time, quality, judgement, confidence and practical usefulness against the agreed baseline.
Refine before scale
Use participant and employer feedback to improve the content before broader rollout.
Controlled, transparent and reputation-first.
The pilot is not designed to prove that AI is always better. It is designed to find where it adds value, where it does not, and what controls people need.
Clear scope before anything starts.
- Agreed pilot objective
- Defined participant group
- Baseline measures where practical
- Approved task and scenario boundaries
- Workplace information and confidentiality expectations
- Pre/post feedback and outcome review
- Recommendations for refinement or next-stage rollout
No inflated claims.
We will not promise guaranteed productivity gains, replace competent-person review, make safety-critical decisions, or publish performance claims without evidence.
Where the pilot can start.
Frontline supervisors
Handovers, meetings, team communication, planning and administrative workload.
Trades & maintenance
Communication, learning support, work preparation and lower-consequence documentation tasks.
Training & capability teams
Workforce AI awareness, responsible-use education and development of approved practical scenarios.
Interested in running a controlled pilot?
We welcome conversations with mining, maintenance, trades, industrial and training organisations interested in testing practical AI capability with a defined workforce group.