Technology & Innovation

•  AI and ML are a reality. While AI/ML still somewhat accessory to resource modelling, is traditional geostatistics is being challenged? This panel explores how innovation is transforming workflows and whether automation and AI can (or should) replace traditional methods.
• We’ll also ask: how do we balance speed and sophistication with geological rigour, trust, and transparency?
• Who will be responsible for developing and implementing impactful techniques?
• Uncertainty quantification and risk management is overdue: how to finally break through?
• Integration between platforms and inclusion of different subjects into a model. 
• The role of cloud (maybe quantum?) computing and remote collaboration
• Are we ready for real-time systems & digital twins?

People

• While tools and techniques evolve, the human side of resource modelling faces a talent crisis. Fewer young professionals are entering geoscience and mining, creating a skills gap just as modelling becomes more complex.
• What does the future workforce needs? More field experience, coding, critical thinking, mentorship and cross-disciplinary collaboration?
• How can industry, academia, and government work together to attract, train, and retain the next generation of modellers?
• How can we empower academic and technological institutes to keep research flowing?
• For the existing workforce, how to upskill geologists and keep systems up and running? How to avoid the loss of knowledge?