Drawing on lessons learned from existing AI applications in mining to help operations benefit from the safety and productivity improvements that it can offer.

Many mining stakeholders have implemented various AI applications and experimented with different approaches—some have seen success while others have been disappointed. This project aims to leverage lessons learned and case study examples from these efforts to develop a guideline for applying AI. It also aims to develop a roadmap for the industry to that AI applications can be scalable.


  • Business case: defining a decision-making process, the implications of AI and more
  • Setting the approach, business model and structure, project management, KPIs, and more
  • Change management and human factors: Defining the journey and resources needed, key stakeholders’ involvement, adoption cycle for workforce, training plan and more
  • Ethics: Understanding the risk of bias and the potential implications of AI
  • Data considerations: data models, data reconciliation, leveraging data analytics methodologies, intellectual property and more
  • Business operations: risk management, safeguards considerations and decision-making
  • Defining acceptance criteria, testing frameworks, infrastructure, cybersecurity, scalability and more
  • Launched

  • Content Development

  • Editing

  • Review

  • Published


Feb 27, 2020 | Workshop in Denver, CO

During the Denver workshop on Feb 27, the project team identified the stakeholder matrix, suggested topics, defined the action items and strategized on the future of AI in mining. Click here to access the full summary.

Oct 2019 | Project Launch

Workshops were held in October to develop a table of contents and start defining content. Currently, the document is still in its early stages of development. If you are interested in becoming an active contributor, please contact any GMG staff.