• Exploring how AI can support increasingly autonomous, electrified and renewable-powered mining operations while managing greater complexity safely and reliably 

• Sharing Fortescue’s experience in building AI capability across mining, energy and engineering and applying it to high-value business challenges 

• Examining how AI can reshape workflows, decision-making and the way work is designed and delivered across the mining value chain 

• Understanding how mining companies can combine AI’s analytical power with human experience, judgement and accountability to deliver sustainable operational value 

• Breaking down data silos across mining operations 

• Improving data quality to enable trusted AI 

• Transforming operational data into real-time insights 

• Building scalable data platforms for future growth 

• Transforming data into measurable business outcomes 

Moderator: Ed Lewis, Head of Data, AI & Automation, Unearthed 

• Aligning AI investments with long-term business strategy 

• Creating an organisational culture that embraces innovation 

• Balancing operational performance with digital transformation 

• Developing the leadership capabilities required for an AI-driven future 

• Lessons from enterprise-wide transformation programmes 

Moderator: Jason Crusan, CEO, AROSE

• Explore real-world applications of machine learning and AI to enhance grade control, mineral estimation, and operational decision-making across mining operations.

• Discover practical approaches to integrating AI into QA/QC workflows, including the use of predictive models for material properties such as moisture content to improve accuracy, consistency, and confidence in operational outcomes.

• Learn how interpretable AI models can support business-critical decisions, drive stakeholder trust, and demonstrate measurable value in production environments.

• Gain insights from firsthand experience deploying AI solutions in mining operations, including lessons learned around data quality, model validation, change management, and the transition from proof of concept to operational adoption.

• Understand the challenges, successes, and key considerations when scaling machine learning initiatives from experimentation to practical, day-to-day use within mining organisations.

1. The Infrastructure of Intelligence - Building trusted foundations for AI in mining 

• What if the biggest barrier to AI in mining isn’t the AI? 

• Why can a valuable insight still arrive too late to make a difference? 

• What does it take to turn information from a remote operation into a decision people trust? 

• Explore what must work behind the scenes for AI to deliver on its promise. 

Speaker: Graham Allan, Head of Solutions Australia & New Zealand, Orange Business

2. Mining Ontologies: Connecting Data for Smarter Operations 

• Exploring how a shared operational language can connect data across dispatch systems, processing plants, geological models and other mining systems 

• Understanding the cost and complexity of maintaining bespoke data integrations across multiple technology platforms 

• Examining how connected data and a common operational language can improve automation, planning and decision-making 

• Identifying practical starting points for implementing a mining ontology without overhauling the existing technology stack 

Speaker: Harry Finn, Part Owner & General Manager, Sales and Marketing, Information Alignment 

• Turning unlabelled rig sensor data into actionable intelligence – using AI to extract meaningful patterns from raw IoT signals without the need for costly, time-consuming labelling projects.  

• From sensor signals to crew-activity models – combining domain expertise with AI and coding agents to identify and understand activity patterns across drilling operations.  

• Keeping AI honest without labelled data – exploring a human-in-the-loop autoresearch approach that uses engineering knowledge to guide research, validate emerging patterns and avoid misleading machine-learning outputs.  

• Making future labelling faster and cheaper – using AI-generated clusters to structure the data and create a foundation for targeted labelling and more scalable machine learning.  

• Unlocking new operational insights from the rig – demonstrating how unlabelled sensor data can evolve into crew-activity intelligence and support better understanding of drilling performance. 

Understanding what differentiates AI agents from today's chatbots and copilots, including planning, tool use, reasoning and the ability to execute multi-step workflows  

• Identifying where AI agents can deliver the greatest near-term value across maintenance, procurement, reporting and operational decision support  

• Designing governance, human oversight and accountability into AI agents from the outset to enable safe and responsible adoption  

• Building a practical roadmap from individual productivity tools to enterprise-wide AI-enabled workflows  

• Looking beyond AI agents towards digital colleagues and the next generation of autonomous enterprise systems 

• What differentiates agentic AI from today's copilots and predictive AI

• Early use cases in mine planning, maintenance, geology and procurement

• Governance, oversight and operational risk

• Where mining companies should experiment over the next 2–5 years

From Process Monitoring to Operational Intelligence 

• Moving beyond monitoring to operational intelligence that enables real-time decision-making 

• Turning process and asset data into actionable insights that improve productivity, stability and asset performance 

• Leveraging AI, advanced analytics and digital twins to optimise mineral processing operations 

• Embedding digital capabilities alongside metallurgical expertise to accelerate operational excellence 

• Building the organisational capabilities needed to scale operational intelligence across the processing value chain 

• Defining the next generation of mining operations through connected data, intelligent systems and continuous optimisation 

• Using digital twins to optimise mine planning
• Integrating real-time operational data
• Supporting predictive and scenario-based decision-making
• Improving collaboration across functions
• Scaling digital twins across mining operations

• Building the foundations for effective remote mining operations beyond traditional SCADA.
• Overcoming the "keyhole effect" through real-time data platforms, interoperability, and operational context.
• Enabling Decision Intelligence with Industrial AI, digital twins, and progressive automation.
• Integrating maintenance, operations, and business data for faster, smarter decision-making.
• Extending optimisation from individual plants to the entire pit-to-port value chain.
• Redefining the operator's role from monitoring and control to orchestration and continuous improvement.

• Five emerging technology companies presenting breakthrough mining solutions. 

• Practical applications of AI, robotics, machine vision and digital twins. 

• Early deployment results and customer success stories. 

• How startups are solving long-standing operational challenges. 

• Connecting miners with the next generation of technology innovators. 

Start-up Pitch Moderator: Ed Lewis, Head of Data, AI & Automation, Unearthed 

1. Federated AI for Biological Mineral Recovery: Fragmented and commercially sensitive research data limits collaboration and the development of predictive models for biological mineral recovery. This roundtable will explore how federated AI can enable companies and research groups to train shared models while protecting their data and intellectual property, reducing duplicated experiments and accelerating the development and evaluation of biological solutions across different ore conditions. 

Speaker: Marltan Wilson, Founder, Omics Archive Pty Ltd 

2. Session to be Outlined 

Speaker: Vishnu Saran, CEO & Co-Founder, InvigiloAI 

Confirmed Start-Ups:  

• Unchartered AI 

• Invigilo AI 

• Omics Archive Pty Ltd 

• Accelerating innovation through industry collaboration

• Supporting AI adoption across the sector

• Developing the future digital workforce

• Strengthening Australia's mining technology leadership

• Creating an ecosystem that accelerates innovation

• Examine how mining organisations can accelerate AI adoption while managing the unique cybersecurity and digital risks associated with increasingly connected mining operations.

• Identify the key security considerations when deploying AI across operational technology (OT) and IT environments, including protecting critical infrastructure, managing third-party risks and maintaining workforce trust.

• Understand how practical governance and security frameworks can enable AI adoption while balancing innovation, operational resilience and regulatory obligations.

• Consider the strategies organisations are adopting to strengthen cyber resilience, secure AI-enabled operations and prepare for the next generation of intelligent mining.

• Capturing tacit knowledge with AI – turning the experience, rules and judgement held by maintenance planners into structured, usable planning constraints. 

• Rapid creation of shutdown schedules, using optimisation algorithms to produce critical path schedules early, before options to make changes disappear. 

• Building better, faster and more explainable shutdown schedules – demonstrating how AI and optimisation can augment planners, improve scenario analysis and support more robust maintenance decisions. Helping new staff produce quality plans faster. 

• Flag SimOps risks early! 

• Moving beyond technology development to focus on the operational problems and decisions that digitalisation needs to improve. 

• Exploring the decision-making layer connecting process data, predictive models and operational action. 

• Applying process knowledge, predictive models and AI to mineral processing decisions under uncertainty and changing operating conditions. 

• Introducing the emerging Grinding Intelligence approach being developed at JKMRC. 

• Evaluating decision-making effectiveness through decision quality, timeliness, consistency and adaptability, and their impact on operational outcomes. 

• Aligning AI investments with long-term business strategy. 

• Creating an organisational culture that embraces innovation. 

• Balancing operational performance with digital transformation. 

• Developing the leadership capabilities required for an AI-driven future. 

• Lessons from enterprise-wide transformation programmes. 

• From experimentation to enterprise adoption – understanding how the rapid evolution of AI is changing the way mining companies manage, access and use data. 

• Beyond content generation and coding – exploring the shift towards autonomous reasoning and more advanced AI capabilities, and what this means for mining operations. 

• Building the foundations for AI at scale – examining how data quality, governance and accessibility need to evolve to support enterprise-wide AI adoption. 

• Turning AI into measurable business value – identifying where mining companies can move beyond pilots and experimentation to deliver practical, scalable outcomes across the organisation. 

Participants can join the round table of their choice. Each roundtable leader will spend the first 10 minutes on a case study, lessons learned, or industry update. This will be followed by a 30-minute open discussion with all participants, moderated by the leader. The final 5 minutes will be used to identify 3-5 industry recommendations resulting from the group discussion. Key recommendations will be shared with the entire audience, and all roundtable recommendations will be included in the post-event report.  

• Governing AI at Scale – Building the AI Control Plane for Mining: Explore how mining companies can create a secure, governed layer for managing AI models, agents and data across the enterprise. Discuss how to bring structured data, unstructured information, and third-party sources together while maintaining control, security, auditability, and accountability as AI adoption accelerates. Roundtable Leader: Kyle Evans, Enterprise Account Executive, Snowflake 

• From Remote Visibility to Decision Intelligence – What’s Next for Mining Operations? As mining companies move from connectivity and remote operations towards AI-driven decision-making and autonomy, this roundtable will explore where the industry is on the maturity curve, what barriers remain, which decisions still rely on tribal knowledge, and where AI can realistically support operations over the next 3–5 years. Roundtable Leader: Mark Chan Yan, Head of Digital Solutions, Yokogawa Australia Pty. Ltd. 

• Roundtable: Building Trusted Communications Infrastructure Foundations for the Intelligent Mine: As mining operations become increasingly connected, autonomous and AI-enabled, reliable communications infrastructure is essential. This roundtable will explore how mining companies can build secure, resilient and scalable networks to support real time data exchange, remote operations and intelligent decision-making across the mine. Roundtable Leader: Graham Allan, Head of Solutions Australia & New Zealand, Orange Business 

• From Siloed Data to Situational Awareness – Making Digital Twins Operationally Useful in Mining: Digital twins promise greater visibility across mining operations, but fragmented data and limited integration often prevent them from delivering real operational value. This roundtable will explore how mining companies can connect data across systems, improve situational awareness and use digital twins to support better decision-making, operational planning and real-time responses. Roundtable Leader: Sean Helm, Guardian & InDI Value Stream Leader, Sentient 

• From Pilot to Plant-Wide Adoption – Improving the Success Rate of Digitalisation Projects:Implementing AI, IoT and digital technologies in comminution plants often depends on data, sensors and site conditions that may not be in place. This roundtable will explore the barriers to successful implementation and how mining companies can improve data readiness, site preparation and collaboration to increase the chances of success. Roundtable Leader: Arie Araya, ME Digital Deployment Manager, ME Elecmetal 

• From Individual Tasks to End-to-End Execution – Agentic AI in Mining Business Processes: As AI agents become increasingly capable, the opportunity is shifting from automating individual tasks to executing entire business processes. This roundtable will explore how mining companies can use agentic AI to connect workflows, systems and data, where human oversight remains essential, and which business processes are ready for end-to-end execution. Roundtable Leader: Vladimir Vorotilov, Associate, AITAI (Australian Institute of Transformation and AI) 

• Bridging the Data Gap: Transforming Inspection Data into Actionable Intelligence - Why turning robotic inspection data into integrated, real-time maintenance insights is the key to safer, more reliable, and more efficient mining operations. Godelius 

Roundtable Leaders:  

• Sean Helm, Guardian & InDI Value Stream Leader, Sentient 
• Arie Araya, ME Digital Deployment Manager, ME Elecmetal 
• Vladimir Vorotilov, Associate, AITAI (Australian Institute of Transformation and AI) 
• Mark Chan Yan, Head of Digital Solutions, Yokogawa Australia Pty. Ltd. 
• Graham Allan, Head of Solutions Australia & New Zealand, Orange Business 

• Building compelling business cases for AI investments 

• Selecting KPIs that matter to executive leadership 

• The role of innovation infrastructure in scaling and demonstrating the value of physical AI across robotics and automation 

• Scaling successful initiatives while avoiding common pitfalls 

• Aligning digital investments with strategic objectives 

• Lessons from organisations delivering measurable business value 

• How an alternative perspective to mine planning can identify opportunities to eliminate repetitive tasks, reduce manual intervention, and streamline workflows. 

• Integrating existing mine planning environments to improve the transfer and reliability of data between systems. 

• Development of custom tools that automate predefined processes and improve the efficiency of day-to-day engineering tasks. 

• Quantifying the financial value of workflow optimisation through time savings, translating improvements in staff efficiency into measurable cost savings and NPV. 

• Exploring how quantum technologies are moving from research into practical applications, and their potential relevance to mining. 

• Applying quantum algorithms to complex scheduling and planning challenges, including train routing and vehicle logistics. 

• Investigating quantum imaging techniques for wave-based imaging, with potential applications in seismic data analysis and subsurface modelling. 

• Examining advances in quantum sensing, including field-demonstrated gravity and magnetic sensors for mining applications. 

• Assessing which mining challenges are best suited to quantum technologies, expected development timelines, and opportunities to complement emerging AI tools. 

• Which roles will evolve over the next decade  

• Upskilling engineers, operators and frontline supervisors  

• Leading organisational change at scale  

• Creating trust and adoption across the workforce  

• Attracting digital talent into mining 

• Coaching mid-level leaders to develop judgment, expertise, and a shared sense of purpose in increasingly AI-enabled and complex operating environments