Key Themes
Exploring Retrieval Augmented Generation (RAG) and Agentic Systems – Improving Accessibility and Real Time Decision Making
This theme explores the potential of Retrieval-Augmented Generation (RAG) and agentic AI systems to enhance real-time decision making and data accessibility across operations. It highlights how AI can transform asset visualisation by contextualising data into actionable insights, and the value of building centralised AI capabilities that balance performance, cost, and control. Agentic AI plays a key role in predictive maintenance and well management, enabling intelligent troubleshooting through autonomous agents. The integration of RAG with sensor data and domain-specific archives supports the creation of powerful knowledge bases. Finally, the theme examines agentic digital twins as tools to reduce decision-making bias, enhance system interconnectivity, and promote continuous learning.
Applying AI in Smart Refineries – Enhancing the Impact of AI on Downstream Operations
This theme examines the transformative role of AI in smart refineries, focusing on its growing impact across downstream operations. It explores how field workers interact with machine learning, physics-based models, and symbolic simulation to support more informed decision-making. Tackling operational data challenges is central, with an emphasis on achieving sustained interoperability between systems and solutions. AI is also enhancing asset health visibility, enabling more accurate forecasting for inspections and maintenance. The theme considers how to justify capital investment in autonomous operations to boost safety and mechanical integrity, and highlights the role of large language models in reshaping how users engage with complex industrial data.
Adopting a “User First” Methodology – How to create a Culture of AI Centricity
This theme explores how to build a culture of AI centricity by putting users at the heart of digital transformation. It emphasizes the need to invest in leadership, education, and new ways of working to foster AI readiness across the organisation. Addressing talent gaps is critical—developing a data-native workforce and attracting new skillsets are key to long-term success. The theme also considers how to overcome cultural resistance, highlighting the pivotal role of people managers in enabling smooth technology adoption. Diversity, equity, and inclusion (DEI) are presented as essential foundations for deploying AI responsibly and effectively.
How is AI revolutionising Supply Chain and Inventory Management
This theme explores how AI is transforming supply chain and inventory management, driving efficiency and agility across operations. It highlights the role of deep reinforcement learning in optimising supply chain models and decision-making. By leveraging big data and predictive analytics, organisations can anticipate demand and streamline logistics. The theme also looks at how Causal AI applications enhance inventory and warehouse management, improving accuracy and reducing waste. Additionally, it examines how AI influences supplier performance, procurement behaviours, and the overall quality of supply chain data, paving the way for more responsive and intelligent supply networks.
Understanding Best Practices in Applying AI for Autonomous Operations
This theme focuses on best practices for applying AI in autonomous operations, highlighting how emerging technologies are reshaping decision-making and control. It explores the evolving role of computational power—particularly the future collaboration between AI and quantum computing—and the rise of agentic AI systems that reduce the need for human intervention. Enhanced predictive analytics play a key role in risk assessment, while scaling AI solutions requires robust data integration and overcoming operational maturity gaps. The theme also examines real-time autonomous well control, showcasing how AI can adapt to shifting oil dynamics and maintain stability with greater precision.
Understanding Responsible AI – Implementing ethical and privacy Consideration on using AI Models
This theme delves into the imperative of Responsible AI—focusing on how ethical and privacy considerations must shape the deployment of AI models. It highlights the importance of trustworthy data sources, quality control, and the orchestration of workflows across complex systems. The discussion extends to sustainable AI integration, emphasizing transparency, governance, and the need for robust privacy safeguards. Key challenges such as copyright concerns and misinformation in generative AI are explored, alongside the role of system-wide approaches to ensure resilience, accountability, and long-term trust in AI technologies.
The Role of AI in HSE – Safeguarding Workers and Protecting Assets
This theme examines the vital role of AI in Health, Safety, and Environment (HSE), with a focus on safeguarding workers and protecting critical assets. It explores how contextualised data enhances the safe deployment of AI, enabling smarter decision-making in high-risk environments. The integration of AI with wearable technologies offers real-time monitoring of worker health and safety, while AI-driven simulations and intelligent safety systems help prevent incidents and improve response. Together, these innovations demonstrate how AI can actively contribute to creating safer, more resilient operational environments.
Agenda
Join us for a focused day of innovation with 16 expert-led sessions exploring the latest in AI—from autonomous operations to ethical deployment. Complete the form below to get exclusive access to the full agenda.
Please note, all our agendas are subject to change.