•    Benchmarking AI adoption across major mining operations
•    Building vs. buying AI solutions - finding the right balance
•    Data governance frameworks that enable AI at scale
•    Integrating AI with existing OT/IT infrastructure
•    Skills gap solutions: Upskilling workforce for AI-powered operations
•    Regulatory compliance and AI transparency requirements

• Why the business case for mining construction differs from many other project models 

• How AI can drive better outcomes, lower risk and deliver projects on time 

• Some risks of AI (and how to avoid them) 

• AI Use-cases, what problems they solve and the positive outcomes they can deliver 

• Explore how AI can streamline mining operations and reduce operational costs. 

• Learn from a real-world case: optimizing fuel burn for an Australian trucking company. 

• See how AI acts as an enabler for smarter, more sustainable decision-making. 

• Taming Data Chaos: Learn how to unify disparate data from people, assets, and systems to unlock hidden performance and reliability in your mobile fleet. 

• Building Digital Twins: Discover how to model your mobile assets to accurately monitor performance, health, forecast energy demand, optimize power usage, and prepare for a hybrid/electric future. 

• Driving Action & Collaboration: See how to translate models into results through intuitive operator interfaces, real-time alerts, and a culture of secure data sharing across sites from maintenance and reliability to the procurement team looking at TCO. 

•    From Task to Strategic: Elevating Mining Professionals Through AI 
•    Building Digital Confidence: Closing the Skills Gap Without Leaving Anyone Behind
•    The Internal Innovation Engine: Developing Homegrown AI Talent 
•    Measuring What Matters: AI-Enhanced Performance and Wellbeing 
•    Using predictive analytics to identify training needs, optimise shift patterns, and prevent burnout in remote operations 
 

• Digital Experts to Teams of Digital Experts 

• Transforming your existing CCTV network into intelligent visual sensors that automate process control and enhance operational safety 

• Learning about real-world vision AI examples built for reliable monitoring in harsh mining environments 

• Exploring scalable deployment with portable vision AI on edge and cloud 

• Discovering fully integrated hardware package for rapid trials and production rollout 

• Seamless integration and retrofitting of vision AI across mining assets and plants. 

• Discussing if data centers are the future of AI? 

• Understanding what is driving todays AI compute and its sustainability  

• Exploring today's compute alternatives and what's coming 

• Where does this all lead:  a look at the likely near future 

• Looking at applied ML workflows to improve exploration. 

• Integrating classic geologic interpretations with geophysics to produce prospectivity maps - an example from NE Queensland for intrusion related Au exploration. 

• Generating geology maps from remote sensing and geophysical data, example from the Gascoyne, WA 

Unlock the full potential of your mining data. In this session, WSP & Bentley Systems explore the challenges faced by the industry and how digital twins will become the foundation for AI enablement. Discover practical strategies to make you next AI project successful. 

•    The Insider Threat Reality: Preparing for both internal threats and external attacks - balancing employee trust with security requirements 
•    Remote Site Vulnerabilities: Unique challenges of distributed workforces with limited on-site personnel and delayed incident response - technology solutions for isolated operations 
•    Legal Complexity of Monitoring: Navigating privacy laws across jurisdictions while maintaining security oversight - creating transparent policies that protect both company and employees 
•    Security as Innovation Enabler: Rebranding security teams from saying "no" to facilitating secure operations - cultural change management strategies that work 
 

Each round table covers a different topic, allowing attendees to select which one to join. Round table leaders will begin with a 5-minute introduction of themselves and a brief overview of the topic, which can include a case study, lessons learned log, or industry update. They will then facilitate a 40-minute discussion with the group, encouraging feedback and questions. The last 10 minutes will focus on agreeing on five industry recommendations. These recommendations will be shared with the entire audience and summarized in the post-event report.

1. GenAI in Production - Beyond ChatGPT - Tony Tang, Tangibility Consulting
2. Transforming Pain Points: AI Data Governance and Trust -    Vidya Krishnan, Cyient
3. Autonomous Mining: Promise vs. Reality - When Will We See True ROI?  Phil May, Equinix
4. From Block Models to Stock Models – extending Resource Intelligence to your Stockyards  Sean Helm, Sentient Computing
5. Industrial IoT Data Analytics for Mining Operations - Previn Pillay, Yokogawa Australia Pty Limited
6. Bridging the Data Divide: Overcoming Barriers to Digital and AI Adoption in Mining – Debbie Smith, Services Director – Solution Engineering & Services, Bentley Systems 
7. Scaling AI from Pilot to Production: Bridging the Gap between Experimentation and Operational Impact - Alyssa Le Cornu, Co-founder and VP of Sales, Aetheron and Shaun McLean, Founder and CTO, Aetheron 

• Legacy System Integration Strategies 

• Risk Management and Operational Resilience 

• Architecture, Connectivity and Governance Frameworks 

•    Internal data governance and quality
•    Phased approach to data standardization across operations
•    Change management strategies for technical and operational teams
•    Quick wins: Where to start for immediate AI benefits

•    Why traditional operator-dependent process control creates bottlenecks, inconsistencies, and limits optimization potential in modern mining operations 
•    How sensors, real-time data analytics, and intelligent control systems are replacing manual decision-making with automated process optimization 
•    Demonstrating improved recovery rates, reduced operating costs, and enhanced safety through fully automated process plant management 

•    Phased approach to data standardisation across operations
•    Change management strategies for technical and operational teams
•    Investment requirements and expected ROI timelines
•    Quick wins: Where to start for immediate AI benefits
•    Building internal data governance frameworks that enforce standards

•    Rail and Port Synchronization: The Last Mile Challenge 
•    Mixed Fleet Reality: Breaking the 25% Barrier - Overcoming OEM Vendor Lock-in to Enable Full Fleet Remote Operations
•    From Incidental to Intentional: Mining Transport's Hidden Data Gold 
•    Digital Handoffs: Solving the Modal Transition Problem

Use of generative AI is becoming widespread in day-to-day engineering and operational work. These tools accelerate workflows and shift effort from manual tasks to higher-value analysis, improving productivity while maintaining engineering rigour in the mining industry: 

• AI can support concept-design activities, including early engineering design and materials take-off (MTO) generation, reducing effort from weeks to hours. 

• Digitisation of logs converts manual operational records into structured datasets, enabling better outlier detection and higher data quality for decision-making. 

•  Understanding where AI can be applied  

• "I know I need to be doing something but I just don't know what?" building an ideas factory  

• 6 step process to building your AI strategy 

• Understanding quantum sensing and its competitive advantages 

• Exploring applications in mining 

• Diamond NV Centers: The Quantum Advantage 

• Discovering real-world impact and looking at the future outlook 

• Why human-free zones are speeding up mining’s AI adoption—and what’s finally making robots useful 
• How to apply first-principles and systems thinking to real-world operational chaos 
• The surprising behavioural patterns that emerge when humans work with AI 
• Why data quality isn’t a hygiene factor, but a strategy—it’s your rate limiter for automation at scale 

• Can AI Agents be trusted? 

• What’s the right balance between a Specialist and AI? 

• Partnering for success – Highlight the importance of strategic partnerships 

• Exploring the AI tools to enhance productivity 

• Understanding open process automation systems 

• Accessing compute and expertise for AI application development 

• Human–AI collaboration in mining: How agentic AI is evolving from analytics to advanced reasoning, real-time operational decision support, safe autonomous actions, and continuous verification across OT/IT environments. 

• Lifecycle impact: Efficiency, safety, and reliability gains from engineering through to production as industrial systems become more adaptive, governed, and context-aware. 

• Case studies that matter: Enhanced monitoring, decision support, optimisation, and workforce augmentation across critical mining processes. 

• What’s next: Personalised, explainable, and trusted operational intelligence powered by adaptive industrial agents. 

• The Procurement Puzzle: Where does Communicative Process Automation sit in procurement – OT, IT, or “decision systems”? Rethinking ROI models to value traceable, cross-functional decisions, not just production uplift. 

• From Disruption to Dialogue: Instead of “white-collar elimination,” CPA reframes AI as an augmentation layer for engineers, controllers and planners – new skills for designing, supervising and negotiating with decision agents. 

• Big vs. Agile Players: What makes CPA experiments stick in majors vs. mid-tiers? Lessons on governance, risk appetite, and local ownership when you introduce a conversational decision layer above the existing APC/MPC. 

• Beyond Tonnage Metrics: Industry fixation on throughput hides the biggest opportunity: automating the conversation work (handovers, exceptions, trade-offs, ESG constraints). How CPA Expands AI’s Scope Beyond Pure Production KPIs. 

•    Renewable energy integration and smart grid management
•    Battery-electric vs. hydrogen equipment - operational insights
•    AI-optimized energy consumption across operations
•    Carbon capture and utilization technologies in mining
•    Real-time emissions monitoring and automated reporting