-    Celebrating the contributions of women in AI and the challenges they face in the traditionally male-dominated oil and gas industry
-    Practical strategies for increasing diversity and inclusion within AI roles
-    Case studies of successful women leaders in AI and their impact on the industry
 

-    Optimizing production and maintenance in older oilfields despite infrastructure limitations
-    Identifying the types of assets that benefit most from AI-driven upgrades in legacy fields and how to prioritize them
-    Overcoming challenges in retrofitting AI technologies into aging infrastructure while minimizing disruption to operations
 

-    Reviewing how AI is applied across different timelines and stages of operations:
     o    Horizon 1 (short-term, immediate applications like efficiency gains)
     o    Horizon 2 (mid-term, such as integrating larger datasets for predictive capabilities).
     o    Horizon 3 (long-term, transformative uses like full automation)
-    Discussing the challenges and benefits of adopting AI technologies at each stage

-    Understanding how AI algorithms are revolutionizing document processing in areas such as contract reviews, regulatory compliance, and data analysis
-    Building a knowledge base with AI to capture internal expertise and ensure knowledge retention, especially with an aging workforce
-    Real-world examples of AI applications in document management and knowledge sharing

The lightning rounds will showcase case studies from speakers who each have 15 minutes to present their update on the Problem, challenge + solution

-    Augmenting Predictive Models with AI: When Traditional Approaches Fail
-    Building vs. Buying AI Solutions: How to Make the Right Choice
-    Path to Product: Bringing AI Solutions to Market
-    AI Decision Support for Smarter Well Pulling and Abandonment Projects

-    Building audit-ready, transparent AI systems that can demonstrate compliance with new regulations
-    Establishing internal AI governance frameworks to monitor performance and mitigate risks
-    Aligning AI investments with regulatory requirements while ensuring a focus on innovation
 

-    Highlighting strategies for data standardization using data mesh and data fabric
-    Overcoming data silos to enable cross-departmental AI applications
-    Understanding the role of unified data frameworks in driving more accurate AI models

The lightning rounds will showcase case studies from speakers who each have 15 minutes to present their update on the Problem, challenge + solution

-    The Future of Predictive Maintenance: A Convergence of Physics & Machine Learning
      Ruzbeh Homji, Mechademy
-    AI for Calculating and Reducing Emissions
-    The Role of DEI in Shaping AI and Machine Learning
      Srimoyee Bhattacharya, Shell Oil Company

-    Providing first-hand insights from the CURT POC, including challenges faced, solutions developed, and measurable impacts on knowledge ownership, attribution, and monetization.
-    Adapting iSPAI’s platform and its AI agent, Giblet, to meet the unique needs of Oil and Gas, building on the success of iSPAI in Construction.
-    Exploring the ways decentralized AI and blockchain technologies can address knowledge gaps, boost workforce capabilities, and drive new revenue opportunities in Oil and Gas.
 

-    Training programs and hands-on workshops that have increased AI literacy among engineering teams
-    Exploring co-pilot AI solutions that assist engineers in daily tasks, making operations more efficient
-    Case studies where AI augmentation has led to improved productivity without replacing the human element

-    Assessing the Current Landscape of AI Agents in the Industry focusing on practical applications and immediate benefits that AI agents are delivering today
-    Discussing how agentic AI could potentially evolve into more sophisticated systems, while acknowledging the uncertainties and challenges along the path to greater autonomy
-    Considering how these technologies might reshape the industry in the future
 

-    Addressing user concerns by mitigating fears related to job displacement, data privacy, and technology reliability
-    Developing and implementing effective onboarding and training programs to equip users with the necessary skills for working alongside AI systems
-    Fostering a supportive culture by engaging leadership, encouraging continuous feedback, and providing ongoing support to maintain user engagement and adaptability
 

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

-    Spotlighting AI-driven approaches to streamline supply chain logistics, inventory management, and demand forecasting
-    Reducing supply chain disruptions through predictive analytics and AI-enabled process automation
-    Case studies showing how AI has transformed supply chain operations in oil and gas
 

This exclusive session brings together the Advisory Board for the 2025 AI in Oil & Gas Conference to provide strategic guidance on the evolving role of AI in the industry. The session will focus on identifying key trends, challenges, and opportunities in AI adoption for sustainability, efficiency, and workforce integration.

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.

1. Adapting AI Solutions for Legacy Systems: Overcoming Integration Challenges
2. Demystifying AI: Bridging the Knowledge Gap
   George Danner, Business Laboratory
3. Data Democratization: Empowering Teams with Data-Driven Insights
4. Optimizing AI for Edge Computing: Extending AI Capabilities Beyond the Cloud
5. Building Trust in AI Models: Transparency and Explainability
   Tanima Dutta, ONGC Videsh
6. Scaling AI Applications: From Pilots to Enterprise-Wide Adoption
7. Empowering Middle Management in AI Implementation: Bridging Strategy and Execution
8. Ensuring Data Quality for AI: Challenges in Data Collection and Cleaning
9. Exploring AI-Driven Predictive Maintenance: Real-World Use Cases
10. Balancing Automation with Human Expertise in AI-Driven Decision Making
11. Future-Proofing AI Investments: Adapting to Rapid Technological Change
     

-    Adapting AI to the Workforce: How the millennial and Gen Alpha workforce will engage with AI tools and what training will be necessary for them to succeed
-    Understanding the expectations of a younger, more tech-savvy workforce and how AI can cater to their working styles
-    Preparing for a Generational Shift: How AI can help transition knowledge from older workers to the younger generation, ensuring continuity in expertise
 

The lightning rounds will showcase case studies from speakers who each have 15 minutes to present their update on the Problem, challenge + solution
-    AI-Driven Digital Twins for Asset Management
-    Production Optimization and Downtime Reduction through AI/ML in Permian
     Amit Kumar, ExxonMobil
-    Integrating Generative AI for Enhanced Workflow Efficiency

-    Discussing the advantages and challenges of keeping a human-in-the-loop for AI decision-making versus fully automating processes
-    Focusing on use cases where human oversight has been critical, such as safety checks and regulatory compliance
-    Including guidelines for determining which tasks should remain human-centric and which can be safely automated

-    Sharing first-hand experiences from implementing AI and GenAI pilots, highlighting both successes and challenges encountered
-    Providing practical takeaways and best practices that can guide future AI projects within the oil and gas industry
-    Identifying common pitfalls and strategies to avoid them, ensuring more effective and efficient AI adoption in future initiatives

-    Understanding when to apply Machine Learning for structured problem-solving and how Generative AI can address unstructured data needs
-    Exploring how ML can supplement Generative AI to create more reliable, explainable, and accurate outputs in oil and gas applications
-    Practical Examples where ML and Generative AI have worked together to optimize operational performance, reduce costs, or drive efficiency