Exhibition floor open and Brella Networking

• How data, automation, and artificial intelligence are reshaping mining operations to improve safety, productivity, and operational visibility across complex sites.
• The role of digital technologies in advancing more sustainable mining, from optimizing energy and water usage to supporting decarbonization goals.
• Creating a strong digital foundation through cloud platforms, integrated data environments, and robust cybersecurity to enable scalable innovation.
• Empowering people to work alongside advanced technologies by building digital capabilities, fostering collaboration, and driving cultural transformation across the organization.

• Understanding the main barriers slowing digital adoption in mining, including legacy systems, integration challenges, workforce readiness, and change management.
• Exploring the balance between standardizing digital platforms across operations and allowing site-level customization to address unique geological and operational realities.
• Lessons learned from early digital initiatives, including what mining companies would approach differently if starting their transformation today.
• Identifying practical steps to move from pilot projects to scalable digital and AI solutions that deliver measurable value across North American mining operations.

• Applying AI and digital technologies across mining operations to unlock measurable improvements in productivity, safety, and operational performance. 

• Scaling AI from pilots to enterprise-wide deployment through strong data foundations, governance, and cross-functional collaboration. 

• Managing the risks of AI adoption, including data quality, model transparency, cybersecurity, and operational reliability. 

• Embedding responsible AI principles, governance frameworks, and workforce engagement to ensure safe and trusted deployment across mining operations. 

• Building a connected digital ecosystem across exploration, mining, and processing operations 

• Leveraging data platforms, automation, and advanced analytics to improve operational visibility and decision-making 

• Integrating safety, production, and asset performance data to drive real-time operational improvements 

• Lessons learned from deploying digital technologies across large, geographically distributed mining assets 

• Explore how connected data, processes, people and AI are enabling mining organizations to move from reactive decision-making towards more intelligent, adaptive operations. 

• Discover how AI is improving planning, operational efficiency and productivity while helping mining companies reduce costs and respond more effectively to changing operational conditions. 

• Learn how integrating enterprise-wide data creates the foundation for trusted AI, streamlined workflows and more informed decision-making across the mining value chain.

• Gain practical insights into how mining organizations are using AI and enterprise transformation to build more resilient, connected and future-ready operations.

• Understanding the evolving cybersecurity threat landscape in mining as operations become more connected through automation, IoT, cloud platforms, and remote operations centers.
• Protecting critical operational technology (OT) and industrial control systems while ensuring continuity and safety across mine sites, processing plants, and logistics networks.
• Bridging the gap between IT and OT security teams to build unified cybersecurity strategies that protect both corporate and operational environments.
• Managing third-party and supply chain cyber risks as mining companies increasingly rely on digital vendors, connected equipment, and integrated platforms.
• Building a resilient cybersecurity culture through workforce awareness, incident response planning, and governance frameworks that support safe digital transformation.

• Moving beyond proof-of-concept projects to successfully scale AI across mining operations 

• Establishing the governance, oversight, and risk management frameworks needed for responsible AI adoption 

• Defining the right balance between AI-driven insights and human decision-making 

• Building organizational trust in AI to support safety, productivity, and operational performance 

• Measuring and demonstrating the business value of AI at an enterprise level 

• Understanding how data silos between OT and IT systems—such as fleet management, plant control systems, maintenance platforms, and enterprise software—limit operational visibility and decision-making across mining operations.
• Exploring practical approaches to OT/IT integration, enabling operational technology (equipment, sensors, control systems) to connect with enterprise IT systems to create a unified and reliable data environment.
• Establishing clear data ownership and governance, including who is responsible for data quality, how standards are defined, and how mining organizations ensure data remains trusted and consistent across sites.
• Identifying how to move beyond dashboards that no one uses by developing KPIs that operators, engineers, and supervisors actually rely on for daily operational decisions.
• Sharing lessons on how a unified mine data platform helps transform fragmented operational data into high-quality, trusted datasets that support analytics, AI, and autonomous mining operations.

• Examining whether mining’s growing digital capabilities are translating into better operational decisions and measurable value 

• Exploring how decision-making can be intentionally designed around the outcomes that matter: safety, throughput and cost 

• Looking at how better connections between planning and execution can shorten the time from operational deviation to action and recovery 

• Drawing on practical mining examples to explore where analytics, optimization and AI can improve the quality, speed and execution of operational decisions 

Session to be outlined

• Creating a unified digital infrastructure that connects power systems, automation platforms, and operational data across mining sites 

• Improving operational visibility and reliability through integrated monitoring of electrical networks, equipment, and production systems 

• Enabling real-time decision-making with connected platforms that bridge operational technology (OT) and enterprise IT environments 

• Supporting safer, more energy-efficient mining operations through advanced automation, electrification, and digital control systems 

• Why many autonomy projects stall after commissioning and struggle to achieve target productivity 

• How network instability, poor interoperability, and reactive support models impact autonomous performance 

• Practical lessons from real-world deployments supporting AHS Deployments 

• Using active OT monitoring, operational analytics, and structured support models to accelerate steady-state operations 

• Exploring where artificial intelligence is delivering the most measurable value in mineral processing, particularly in areas such as grinding circuit optimization and throughput improvements.
• Examining the use of AI and advanced process control in flotation circuits to stabilize performance, improve recovery rates, and reduce variability in plant operations.
• Understanding the role of digital twins in processing plants, enabling simulation, predictive insights, and improved decision-making across complex processing systems.
• Assessing the importance of instrumentation and data quality, including sensor coverage, calibration, and data infrastructure required to support effective AI deployment.
• Identifying the practical limitations and lessons learned from AI initiatives in processing environments, including where expectations have exceeded operational reality.

• How treating ERP and AI as one connected build not two sequential projects let a development-stage miner start banking value in months, not years.  

• Deploying by value link instead of by module: production-to-ledger, procure-to-pay for MRO, and asset-to-availability, each engineered to pay for itself before the next one starts.  

• Turning production, cost, and inventory data that already exists in spreadsheets into a trusted system of record the prerequisite every AI use case depends on.  

• Using pre-built mining configuration on S/4HANA Public Cloud so each value link is activated and adopted, not designed from scratch. 

• Moving beyond single-solution magnetic inversion to improve confidence in subsurface interpretation  

• Using probabilistic modelling and ensemble analytics to quantify geological uncertainty  

• Applying AI and statistical techniques to identify robust exploration targets and reduce decision risk  

• Real-world applications of advanced geophysical modelling to support exploration and resource evaluation 

• The strategic importance of digital innovation in strengthening the competitiveness of the mining sector

• Supporting responsible and sustainable mining through technology and data-driven operations
• Government priorities for innovation, digital infrastructure, and workforce development in the mining industry

Under Invitation: The Minister for Mining & Energy, The Government of Ontario  

• Managing organizational change as AI initiatives expand across multiple mining sites
• Developing a standardized operating model for deploying digital and AI solutions
• Balancing internal capability development with the use of external technology partners
• Lessons learned from scaling digital initiatives beyond early pilot projects

• Examining how mining operations are evolving to handle growing volumes of operational and sensor data 
• Exploring the connectivity requirements behind emerging applications such as automation, digital twins, and AI-driven analytics 
• Reviewing how mission-critical voice and telemetry carried over TETRA can be integrated with the high-bandwidth private LTE/5G networks these new applications demand, and where MCX fits in bridging the two in active mine environments. 
• A case study from Sierra Gorda, Chile, where a combined TETRA and private LTE deployment supports safety, efficiency, and the operation's future digital initiatives 

• Learn how mining operations are using machine learning and multi-source data integration to improve refueling accuracy and maximize haul truck utilization. 

• Explore how fuel flow meters, tank sensors, geo-location, fuel temperature, and vehicle network data can be combined to provide trusted real-time fuel visibility. 

• Understand how predictive models can estimate remaining operating cycles and support smarter dispatch and refueling decisions. 

• Discover how intelligent fuel management strategies can reduce unnecessary refueling events, eliminate fuel-out risks, and increase equipment availability. 

• Examine real-world results, including 40+ additional operating hours per truck annually and measurable productivity improvements across fleet operations. 

• Overcoming fragmented data environments across legacy historians, control systems, and operational platforms. 

• Building a strong data foundation to enable reliable analytics, operational visibility, and AI adoption. 

• Integrating OT and IT systems to unlock real-time insights across mining and processing operations. 

• Applying advanced analytics and automation to move from pilot projects to scalable AI-driven optimization. 

• Mining haul trucks consume approximately 32% of total energy and account for up to 22% of operating costs in open-pit mines. 

• Fuel consumption can be affected by equipment condition and characteristics, road design and conditions, weather conditions and driving behaviour, among others. 

• Using data collected from sensors installed on haul trucks, the effect of important factors such as travel speed, road inclination, payload and weather conditions on fuel consumption is studied. 

• Supervised predictive analytics modelling, followed by scenario analyses are used to support decision-making related to mine operation, maintenance and design. 

• Our findings suggest strategies such as payload and maximum speed limit adjustments can yield significant fuel savings. 

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. 

1. If the Network Fails, Everything Fails: Securing Autonomous Mining - Why mission-critical wireless is now the backbone of safe, always-on operations. Rountable Leader: Rob Graham, President & CEO, Genwave

2.  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. Roundtable Leader: Andy Prince, Head of Mining Solutions, SK Godelius

3. The Connectivity Challenge Behind AI in Mining - Explore how reliable connectivity, mission-critical communications and private networks are enabling AI, automation and data-driven mining operations. Roundtable Leader: Jesús Borobia, Business Development Manager for North America, PowerTrunk

4. Mining Digital Transformation Through the Policy & Regulatory Lens - Explore how mining companies are balancing innovation, regulatory compliance, ESG commitments and business value as they accelerate digital transformation. Roundtable Leader: Subhasis Ray, General Manager – Americas, ABB

5. Real-Time Data Integration: From Fragmented Systems to a Unified Operational - Explore how integrating real-time data across fragmented systems creates a single operational view that enables faster, smarter and more informed decision-making. Roundtable Leader: Ruediger Schroedter, Global Lead for Mining, SAP

6. Beyond Go-Live: Who Owns the Autonomous Mine? - Building an operating model for 24/7 support of mission-critical mining technology. Roundtable Leader: Indy Kar, Vice President of Strategic Growth, FTP 

7. Unlocking Real ROI: How mining companies identify the right opportunities and measure value to realize meaningful returns on Data & AI. - This discussion will focus on identifying and capturing quantitative and qualitative value, from the team and project level, rolling up to enterprise-wide portfolios.  Roundtable Leader: Nizar Mohamed, Data & AI Associate Partner, IBM Consulting 

8. From Pilot to Pit: Why AI Struggles to Scale in Operations - Explore where AI is delivering measurable value in mining today, what is preventing promising use cases from moving beyond pilot programs, and how companies are overcoming barriers.  Roundtable Leader: Crystal Lui, Mining & Energy Associate Partner, IBM Consulting

9. From Digital Mine to Intelligent Mine: Practical Applications of Integrated Digital Platforms and AI in Mining - Explore how mining companies are using integrated digital platforms, AI-driven analytics, and operational data to improve decision-making, optimize performance, and deliver real-world results. Roundtable Leaders: Borys Vorobyov & Vitaliy Monastyrov, SIGHTPOWER INC

10. AI and Cybersecurity: Opportunity or Threat? - Explore how AI is strengthening cyber defenses while simultaneously enabling more sophisticated attacks. Roundtable Leader: Chandra Majumdar, Deloitte

• Understanding the strategic decision between building internal digital capabilities and relying on external technology vendors, consultants, and platform providers.
• Identifying which capabilities mining companies should develop internally, including data engineering, analytics, and digital product ownership.
• Exploring where external partners add the most value, particularly in areas such as AI development, specialized software platforms, and advanced analytics.
• Managing vendor relationships and avoiding long-term technology lock-in while maintaining flexibility and scalability.
• Lessons learned from mining companies that have successfully built internal digital teams while leveraging external expertise to accelerate innovation.

1. The Last Analog Mile – Why Mining's AI Ambitions Stall at the Frontline 

• Understanding why frontline, paper-based processes continue to limit AI adoption despite significant investment in digital technologies and automation.  

• Exploring how inspections, permits, pre-starts, hazard observations and other operational data create critical gaps in enterprise data strategies when captured manually.  

• Examining the impact of incomplete frontline data on AI models, operational visibility and decision-making across mining operations.  

• Identifying practical approaches to digitising frontline workflows and creating the data foundation required for scalable AI deployment.  

• Lessons learned from mining operators successfully bridging the gap between analogue processes and enterprise-wide digital transformation. 

Speaker: Mya Walters, Account Director, Mining & Natural Resources, Safety Culture  

2. Leveraging Digital Tools to Boost Energy Supply Chain Efficiency  

• Creating, sorting, and using meaningful data 

• Using digital tools to track and optimize supply chain operations in real-time. 

• Leveraging IoT and AI to predict equipment failures and proactively address issues, reducing downtime and improving supply chain reliability. 

• Implementing blockchain and cloud-based platforms to enhance data sharing, visibility, and traceability across the energy supply chain. 

• Employing machine learning for increased operational efficiency. 

Speaker: Sandeep Krishnan, Director, IT, YASH TECHNOLOGIES 

• How treating AI as an ongoing program has helped NTEC avoid scattered PoCs, failed pilots, and company data used in ChatGPT.  

• Building predictive models that catch major component failures worth $1.5M each, using only data the mines already had.  

• Capturing senior operator intuition before it retires, improving time, safety, and cost.  

• Embedding the governance, training, and change management to make change stick. 

• Building a Fabric data platform designed to carry and scale the next use case, and the one after that.  

• Handing ownership to NTEC's own people so the program sticks after the consultants leave, including what they'd do differently starting over. 

• Training and supporting frontline teams to work effectively with new digital technologies
• Creating incentives that encourage adoption and long-term engagement with digital tools
• Understanding workforce realities, including collaboration with unions and evolving job roles
• Identifying the digital skills and specialist roles that mining organizations increasingly require

• Exploring how AI copilots are being deployed to support supervisors and frontline teams with real-time decision support, operational insights, and improved shift management.
• Examining the growing use of computer vision technologies to improve safety monitoring, detect hazards, and enhance situational awareness across mining operations.
• Understanding the next phase of autonomous technologies, including autonomous drilling and blasting systems designed to improve precision, productivity, and operational safety.
• Leveraging advanced analytics and digital platforms to optimize energy consumption, reduce emissions, and support decarbonization goals across mining operations.
• Identifying the digital initiatives mining leaders are prioritizing over the next two years and the practical steps required to move from experimentation to operational scale.