-    Defining a digital transformation project and the specific threshold 
-    Understanding how new technologies and thinking play out against scaling up existing technologies
-    Discussing the role of end applications and what value they unlocked the most value to date
 

-    Understanding the role of AI and digital technologies in accelerating the shift towards sustainable energy
-    Reviewing insights into how energy companies are adapting to new technologies
-    Overcoming challenges in integrating renewables into the energy mix
 

-    Discussing ways to measure ROI from initiatives, including reduced deployment times, improved model accuracy, and cost savings
-    Understanding ROI calculation and cost-sharing strategies that have been successful in AI collaborations
-    Exploring collaborative models where companies can share costs for AI development and data infrastructure
 

-    Explore approaches to AI security, focusing on risk mitigation strategies for AI models in critical operations.
-    Discuss integration challenges related to security protocols and how to protect AI systems from cyber threats.
-    Include examples of risk management frameworks that have been successfully applied to AI deployments in sensitive environments.
 

-    Implementing machine learning for equipment monitoring
-    Enhancing operational efficiency with virtual simulations
-    Integrating IoT devices for smarter energy systems
-    Accelerating Gen AI Applications Across Industry Use Cases

-    How AI algorithms can predict equipment failure before it happens, minimizing operational interruptions and reducing costs
-    The role of machine learning in enhancing predictive models and improving accuracy over time with larger datasets
-    Real-world examples of AI-driven predictive maintenance programs and their impact on operational efficiency in upstream and downstream operations

Each round table has a different topic, and attendees can select the round table they want to participate in. Round table leaders will spend the first 15 minutes meeting the delegates and providing a case study/lessons learned log/industry update and then moderating a 30-minute discussion with the group where they can give feedback and ask questions. The final 10 –15 minutes will be spent agreeing on 3 -5 industry recommendations. A selection of these recommendations will be shared with the whole audience, and all of them will be summarized in the post-event report.

-    Data Management and Analytics Challenges
-    Workforce Transformation and Skill Development
-    Regulatory and Compliance in Digital Energy
-    AI Skill Gap: Training and Education Strategies
-    Cybersecurity Frameworks for Energy Companies
-    Maximizing ROI on AI Investments
-    Ethical AI: Developing Responsible AI Policies
 

-    Explore the key components of a data governance roadmap that ensure data integrity, compliance, and responsible use of AI
-    Discuss how to establish clear data ownership, quality standards, and security protocols within an organization
-    Include examples of successful governance frameworks that have been implemented in the oil and gas sector, emphasizing scalability and regulatory compliance
 

-    Clarifying AI vs. Generative AI: Breaking down the differences between traditional AI and Generative AI for stakeholders who may be unfamiliar with the nuances
-    AI's Practical Impact Today: Discussing the current state of AI technology and its realistic applications in the oil and gas industry
-    Getting Started with AI: Practical steps for organizations to begin their AI journey, from building data infrastructure to identifying small, high-impact projects
 

-    Demonstrating AI’s Value: How to make the case for AI within oil and gas organizations by showcasing the potential for increased efficiency, cost savings, and innovation
-    Overcoming Resistance: Addressing common concerns and skepticism within the organization about AI adoption and explaining its benefits in understandable terms
-    Change Management Strategies: Practical methods for gaining cross-functional buy-in for AI, from executives to field operators, ensuring smooth implementation
 

-    The Build vs. Buy Dilemma: How to decide whether to build in-house AI capabilities or buy off-the-shelf solutions, depending on the business needs
-    Avoiding the Shiny Object Syndrome: Ensuring companies don’t fall into the trap of chasing the latest technology trends without a solid business case
-    Case Studies on Building and Buying AI: Reviewing examples where companies made the right (or wrong) decisions on building or buying AI and the lessons learned
 

-    Discuss the critical skills needed to support AI deployment and data management in the oil and gas industry
-    Focus on the importance of upskilling existing employees in data science, cloud management, and cybersecurity
-    Explore collaborative training programs and partnerships with educational institutions to build a pipeline of skilled professionals
 

-    Explore how AI can help companies achieve their core business objectives, focusing on profitability, risk reduction, and safety enhancement
-    Discuss how to align AI projects with strategic goals, ensuring that AI solutions are cost-effective and aligned with risk management strategies
-    Include examples of AI-driven initiatives that have directly contributed to financial gains or risk mitigation
 

-    Discuss the current state of AI regulation in the oil and gas sector and the potential impact of emerging laws on innovation
-    Explore how a lack of regulation creates opportunities for fast-paced innovation, but also poses risks around data privacy and model accountability
-    Focus on strategies for staying agile while preparing for future regulatory changes, such as developing internal compliance frameworks
 

This unique, audience-driven panel discussion is designed to tackle the most pressing digitalization challenges in the oil and gas sector — in real time. Unlike traditional panels, this impromptu session puts the power in your hands: attendees will identify the topic, nominate themselves or colleagues, and shape the conversation as it unfolds.
How the Session Will Run:
-    The Chair will kick off the session by outlining the format and inviting the audience to identify key challenges or topics they’d like the panel to address
-    Using Sli.do, attendees will submit and upvote topic ideas. The highest-ranked topic will become the focus of the discussion
-    Attendees can nominate themselves or colleagues with relevant expertise to join the panel, forming an ad hoc team of experts
-    The Chair will guide the panel as they explore the selected topic, offering practical solutions, real-world insights, and debating approaches to overcome obstacles.
-    The audience is encouraged to participate actively by asking questions, sharing experiences, and contributing ideas, fostering a collaborative environment
-    The Chair will summarize the session, highlighting the most impactful insights and actionable strategies shared during the discussion
 

-    Explore how AI can function as a co-pilot, enhancing the capabilities of the workforce rather than replacing them
-    Discuss practical applications of AI in day-to-day tasks, such as data analysis, report generation, and equipment monitoring
-    Include examples of AI tools that have helped increase productivity and reduce errors in collaborative settings
 

-    Navigating the Surge of New AI Tools: From Co-Pilot to ChatGPT
-    Data Privacy and Security: Balancing the Need for Standards with Speed
-    Successfully Navigating the AI Journey & Prioritizing Use Cases
 

-    How AI is used to predict demand, optimize inventory, and reduce supply chain disruptions in real-time
-    The integration of machine learning models for logistics optimization, including transportation and storage solutions
-    Strategies for implementing AI-driven supply chain solutions in complex oil and gas environments with multiple stakeholders
 

This session explores how the energy sector can leverage AI advancements and best practices from industries like healthcare, finance, and manufacturing to accelerate its own AI journey