With many struggling to quantify the ROI of AI initiatives, building a solid business case is essential for securing buy-in and ensuring long-term success. In this workshop participants will learn how to effectively calculate both the value and cost of AI use cases and create an irresistible business case. This session will provide practical tools and strategies to help you maximize the impact of your AI investments while minimizing risk. The workshop will focus on: 

• Developing a framework for assessing the financial impact of AI projects, from cost savings to revenue generation 
• Calculating the total cost of ownership (TCO) for AI use cases, including technology, implementation, and maintenance expenses 
• Exploring real-world examples of AI use cases at Shell that have delivered measurable business value  
• Best practices for building internal support by communicating the tangible and intangible benefits to key stakeholders and budget holders 

Srividhya Vaidyanathan

Global Supply Chain Strategy & Process Transformation
Shell

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Less than 25% of organizations consider themselves data-driven, despite almost all of them ‘drowning’ in operational data. To drive value from data, companies must not only harness the right data but also employ advanced tools to turn it into actionable insights. This will help you create a strong and reliable foundation for AI initiatives. This workshop

will explore how leading operators are transforming raw data into valuable insights that unlock opportunities for innovation, efficiency, and growth. The workshop will focus on:

● Identifying high-value data and eliminating noise, ensuring your teams focus on the most impactful information

● Exploring strategies for improving data accessibility and collaboration, breaking down silos to foster cross-functional insight sharing

● Learning about key data visualization and analytics tools that enable quicker, more accurate decision-making

● Ensuring compliance with industry and governmental regulations

● Discussing how to draw insights by leveraging AI and ML to turn operational data into predictive models that drive efficiency and uncover new opportunities

Cathy Normand

Upstream Data Governance Principal
ExxonMobil

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Generative AI is rapidly transforming operations, yet only 20% of organizations have moved beyond proof-of-concept (PoC) to full-scale deployment. In the oil and gas sector, where operational complexity is high, the potential for generative AI to enhance productivity and innovation is immense. This workshop will dive into how companies can successfully scale generative AI solutions beyond PoC, unlocking value across a range of operational use cases, from predictive maintenance to automated data analysis. The workshop will focus on:

● Identifying high-impact use cases for generative AI within your organization and how to prioritize the opportunities

● Discovering strategies for overcoming common challenges in scaling generative AI, such as data integration,governance, and resource allocation

● Exploring real-world examples of generative AI applications that are transforming workflows, reducing downtime, and enhancing decision-making

● Gaining insights into creating a scalable AI framework that supports continuous learning, improvement, and long- term operational efficiency

In the oil and gas sector, real-time data is a critical asset, yet some estimate less than 15% of organizations effectively leverage it for predictive analytics. This workshop will demonstrate how AI-powered real-time analysis can significantly enhance asset performance by providing actionable insights, optimizing operational efficiency, and reducing downtime. Join us to learn how cutting-edge AI solutions can help your organization turn real-time data into a competitive advantage. The workshop will focus on:

• Learning how AI-driven real-time analysis can identify inefficiencies and optimize asset utilization, leading to improved operational outcomes
• Discovering practical approaches for integrating real-time AI analytics into existing systems to drive faster, data- informed decisions
• Exploring real-world use cases where real-time AI-based monitoring has maximized asset reliability and reduced unplanned maintenance
• Gaining insights into setting up scalable infrastructure that supports continuous AI-based analysis, enabling predictive and prescriptive maintenance