•    Understanding current AI capabilities and their 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.
 

•    Leverage AI and digitalization to understand the connectedness of you operational business processes.
•    Define who does what when, how and why.
•    Explore the possibilities for LLM’s (large language models) against AI augmented work flow database
 

•    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.
 

•    Building a Unified Data Strategy: The role of data centralization in driving efficiency.
•    Vendor Selection & Assessment: Key criteria for choosing AI and digital solutions.
•    From Data to Decisions: Leveraging AI for real-time, automated decision-making
•    Vendor Deployment: Discussing challenges with adoption, data quality, and integration with broader corporate strategy

•    Understanding how predictive maintenance with AI works in practice - How machine learning and digital twins help prevent equipment failures before they occur.
•    Enhancing maintenance strategies with GenAI recommendations and diagnostics for decision support.
•    Looking at reducing downtime, increasing safety, and optimizing resource use through intelligent maintenance.
 

•    Successfully leverage digital technologies throughout the lifecycle of a mine to address challenges facing the industry and ensure profitable operations.
•    Optimize design, unify operations, and take intelligent decisions with Unified Power and Process solutions.
•    Adopt Dynamic Digital Twins to shorten Project Engineering time and enable data-driven decisions
•    Employ Real-Time Operational Digital Twins for site and enterprise-wide optimization of critical assets.

•    Autonomous haulage, AI-powered drilling, and smart processing plants.
•    Improving efficiency, reducing costs, and enhancing safety.
•    Real-time data analytics for predictive maintenance.
•    AI-driven decision-making to optimize resource extraction.
•    Environmental benefits of automation in mining.
•    Overcoming challenges in AI adoption and workforce transformation.
 

•    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.
 

•    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.
 

•    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.
 

•    Dataset Standardization & Managed AI Services
•    Real-Time Decision Making Through Edge AI
•    Predictive Analytics for Operational Excellence
•    Human-AI Collaboration in Mine Planning

•    Government initiatives supporting digitalization and AI adoption
•    Regulatory challenges and opportunities in AI-driven mining
•    Ethical AI and responsible mining practices
 

•    Translating the subject-matter-expert knowledge to build KPI analytics and Power BI reports around ASARCO's mining operations, focusing on operational productivity, maintenance, and fleet management
•    Enable data-driven decisions to optimize operations based on reports showcasing critical insights into mining operations
•    Targeted analytics to facilitate rapid response for performance metrics, issue identification, and continuous improvement
•    Customizable, dynamic reporting allowing users to filter data by equipment, manufacturer, operator, crew, and date for tailored 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.

•    Revolutionizing Tooth Wear Monitoring and Changeout Timing Through AI & Machine Vision. 
•    Sustainability and Energy Management in Remote Operations. 
•    Connectivity solutions for remote mining sites and the role of satellite communications, private LTE, and 5G networks.

•    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.