Outcomes for Applications of AI Workshop in Denver
Participants added to and commented on the content that was developed in Fall 2019 to the draft guideline and worked on more clearly defining the target audience for it. Suggestions included topics such as the explain-ability of AI, identifying whether or not AI is the right solution and clarity around IP issues.
- Comparative Table of Contents. During the workshop, the table of content was revised and there were several suggestions to restructure some of its components. Key changes are around including use cases, technical foundation and shared topics and the rest are similar concepts that could potentially be merged. The outputs will be reviewed by the Artificial Intelligence Working Group Steering Committee within the next weeks to define a final version for the table of content.
- Stakeholder Matrix. The participants mapped the multiple stakeholders around the topic, and identify four types – Star Power, Ready to Go, Future Collaborators and Skeptics Worth Convincing. Star Power are important characters within the process that can accelerate the AI adoption in any organization, and these are usually people at leadership levels and onsite champions. Ready to Go represent those stakeholders that are all-in to begin implementing AI in their operations, such as mine operators, OEMs, OTMs, relevant suppliers/services and academia. Future Collaborators are organizations that have a lot of experience in AI or are critical in the process, such as Big Techs, SMEs, HR departments, Chemical companies, etc. Finally, but not least, Skeptics Worth Convincing are those stakeholders that still struggle to understand the benefits of AI and could represent roadmaps during any implementations, such as Operational Leaders, Site Safety department, Unions, General Workforce, and others.
- Actions Items. The team identified the most critical actions to deliver within the next months, according to its level of difficulty. In the short-term actions, the Project team needs to seek for case studies examples to include in the guideline, seek for ‘Star Power’ involvement for expert input, convince the audience about the benefits of AI, find experts with mathematics and domains background and risk owners. For the mid-term actions, build multifunctional teams, deliver two whitepapers around AI maturity and how to use the data, and build a library for case studies from O&C, R&D and academia. For the long-term actions, the team aims to design a training course around the topic, get the guideline publish and approvals to deploy a prototype.
- Future State of AI in Mining. The participants of the workshop were gathered to define the future state of AI in mining by mapping today’s way of doing work and what is consider today’s innovation. The two concepts were discussed and delivered which are the residual assets that mining will continue doing that’s currently doing, and what mining will be able to do tomorrow with today’s innovation. Finally, each of the groups define strategies that will help mining get to that future state.
If you or anyone has any input on the above outcomes, please let us know in the comment area or contact