•    Understanding current AI capabilities and its real impact on exploration, automation, and operational efficiency.
•    Debunking the myths—separating exaggerated AI promises from real-world applications and practical limitations in mining.
•    Learning from case studies of companies that have effectively implemented AI-driven solutions to improve safety, productivity, and cost savings.
•    Exploring the road ahead—emerging AI trends, workforce impacts, and what’s needed for large-scale adoption in the mining industry.
 

•    Exploring advancements in AI, automation, and IoT that are reshaping mining operations.
•    Enhancing efficiency and productivity through digital tools, predictive analytics, and smart systems that optimize extraction, processing, and logistics.
•    Discovering how technology-driven solutions minimize waste, reduce emissions, and improve resource management.
•    Overcoming adoption challenges by addressing key barriers such as integration, cost, and workforce readiness to ensure the successful implementation of new technologies.
 

•    Exploring AI-powered monitoring for worker health and fatigue detection.
•    Utilizing predictive safety analytics and real-time hazard detection.
•    Leveraging wearable sensors and IoT for early detection of environmental risks.
 

•    Transforming mining operations—how AI enhances exploration, extraction, and mineral processing through automation and real-time data analysis.
•    Boosting safety and profitability—autonomous machinery and predictive analytics leading to safer work environments and improved financial performance.
•    Driving sustainability—AI minimizes environmental impact by optimizing resource use and reducing waste.
•    Implementation roadmap—starting with system assessments, collecting quality data, launching pilot projects, and scaling up while continuously monitoring AI performance.
 

•    AI-Driven Mineral Exploration: The Next Frontier
•    AI-Powered Predictive Maintenance: Reducing Downtime & Costs
•    Real-Time Ore Grade Prediction Using AI & IoT Sensors
•    Digital Twins & AI for Mine Planning & Simulation
•    AI-Powered ESG & Sustainability Analytics for Mining
 

•    Tackling implementation costs & ROI concerns—balancing upfront investments with long-term gains and securing executive buy-in for digital projects.
•    Bridging the talent gap—addressing workforce shortages by upskilling current employees, attracting digital talent, and fostering partnerships with universities.
•    Discussing industry collaborations—how mining companies can accelerate innovation by partnering with tech startups, leveraging incubators, and co-developing AI-driven solutions.
 

•    Real-life examples of autonomous haulage system implementation and how mining operations are deploying autonomous trucks and drills to improve efficiency and safety.
•    Understanding how advanced AI, LiDAR, and real-time data analytics enable autonomous decision-making in mining operations.
•    Addressing workforce transition, regulatory hurdles, and integration with existing infrastructure.
•    Discussing the future of autonomy in mining—exploring next-generation autonomous solutions, from underground robotics to fully integrated smart mines.
 

•    Autonomous Haulage Systems & AI-Driven Fleet Optimization
•    AI-Powered Robotics for Ore Sorting and Material Handling
•    Drone-Assisted Mining: Aerial Robotics for Site Mapping and Monitoring
•    Autonomous Drilling: Precision, Speed, and Safety

•    Unlocking operational efficiency—how automation and AI solutions streamline mining processes, reduce downtime, and optimize resource utilization.
•    Leveraging smart technologies to enhance worker safety, meet regulatory standards, and ensure operational compliance in challenging environments.
•    Demonstrating the economic benefits of automation and AI—reducing operational costs and improving long-term profitability through smarter decision-making.

•    Exploring how machine learning, AI-powered hazard detection, LiDAR, and radar technology work together to detect and prevent collisions in real-time.
•    Discussing the role of collision avoidance systems in meeting global mining safety regulations and reducing workplace incidents.
•    Discovering how CAS solutions improve safety for both automated and operator-driven vehicles in open-pit and underground mines.
•    Exploring the future of collision avoidance in mining—emerging trends such as predictive analytics, digital twins, and V2X (vehicle-to-everything) communication for next-gen mine safety.
 

•    Leveraging IoT and AI for real-time emissions tracking – Exploring how smart sensors, machine learning, and automation can enhance accuracy and efficiency in Scope I and II emissions data collection.
•    Discussing the role of blockchain technology in ensuring data integrity, traceability, and compliance with ESG regulations.
•    Using AI-driven insights to identify trends, optimize energy usage, and reduce greenhouse gas emissions in mining operations.
•    Understanding how virtual models of mining sites can improve scenario analysis, operational efficiency, and environmental impact assessments.
 

•    Discovering the advancements in battery-electric vehicles (BEVs) and hydrogen-powered mining fleets
•    Looking at AI-driven predictive maintenance for sustainable equipment lifecycle management
•    Discussing the impact of automation and robotics on reducing carbon emissions
 

•    Understanding how to apply AI and machine learning to optimize energy consumption and emissions reduction
•    Digital twins for modeling and simulating low-carbon mining operations
•    Blockchain for transparent carbon tracking and ESG compliance
 

•    Aligning security and business goals by redefining cybersecurity success to support mining innovation rather than hinder it, ensuring security objectives align with business growth.
•    Shifting from “No” to “How”: Transforming cybersecurity teams into enablers of innovation by integrating security early in project planning and risk management.
•    Measuring security success beyond budget by evaluating cybersecurity effectiveness based on risk reduction, operational resilience, and business continuity, rather than just cost avoidance.
•    Building a culture of security-first Innovation and encouraging cross-functional collaboration to embed security into digital transformation projects without stalling progress.
 

The lightning rounds will showcase case studies from speakers who each have 10-15 minutes to present their update on the Problem, challenge + solution.
•    Pathways to Decarbonizing Mining Operations.
•    Electrification and Renewable Energy in Mining.
•    Digital Tools for ESG Compliance and Carbon Reporting.
•    Advancing Sustainable Equipment and Circular Economy Practices.
 

•    Enhancing precision and efficiency in underground machinery.
•    Using autonomous robots to monitor equipment, detect faults, and reduce downtime.
•    Safety and risk reduction by minimizing human exposure to hazardous environments through robotic-assisted operations.
•    Leveraging AI and sensor-based robotics to optimize data-driven underground mining processes in real time.

•    Enhancing productivity and precision with self-operating trucks and AI-driven drilling systems.
•    Leveraging AI to optimize ore processing, reduce waste, and improve overall operational efficiency.
•    Using automation to lower operational expenses and maximize resource utilization.
•    Enhancing safety and workforce efficiency by minimizing human exposure to hazardous environments while improving decision-making through AI-driven insights.

The lightning rounds will showcase case studies from speakers who each have 10-15 minutes to present their update on the Problem, challenge + solution.
•    The impact of autonomous haul trucks, drills, and robotic systems on efficiency and safety in remote mining.
•    Remote Monitoring and Predictive Maintenance.
•    Connectivity solutions for remote mining sites and the role of satellite communications, private LTE, and 5G networks.
•    Cybersecurity challenges in remote and autonomous mining.
•    Sustainability and Energy Management in Remote Operations.

•    Discussing how machine learning and data analytics optimize drill patterns for better fragmentation and minimal waste.
•    Understanding the role of remote-controlled and AI-assisted blasting in improving efficiency and ensuring compliance with safety regulations.
•    Looking at how automation and robotics are minimizing human exposure to hazardous drilling, and blasting environments and reducing operational risks.
•    Using IoT sensors, AI, and cloud computing to monitor drilling and blasting performance and dynamically adjust strategies.

•    AI and automation: Transforming efficiency and safety.
•    Robotics and remote operations: Overcoming challenges in implementation.
•    ESG and sustainability: Balancing innovation with environmental responsibility.
•    The Road Ahead: Collaboration, cybersecurity, and workforce transformation.