Pre-Conference Workshop & Masterclass - February 23, 2026

As AI capabilities accelerate, energy leaders face a critical question: not just what can AI do, but what should it do? This workshop challenges attendees to rethink how they evaluate AI investments, balancing innovation with practicality, and automation with human judgment.

Julie Thyne

Global Improvement Director
Dow

See Bio

Jennifer Rogers

Senior Director of Education and Executive Officer, Learning Technology Standard
AMPP (Association for Materials Protection and Performance) and IEEE (Institute of Electrical and Electronics Engineers)

See Bio

According to Bill Gates, AI is ‘the first technology that is happening faster than even the insiders expected’. With the rapid pace of innovation, energy leaders face a critical question: How do we invest in AI today without being left behind tomorrow? In a landscape where AI capabilities are doubling every 6–10 months, the risk of technological obsolescence is real and costly. Poorly chosen initiatives can lead to mounting technical debt, draining resources from high-value opportunities and delaying ROI in markets where every dollar must count. This session will unpack the strategic and architectural decisions that can make or break your AI investments. Through real-world insights and practical frameworks, you will:

• Explore the roots of tech debt by examining how rapid obsolescence, fragmented data strategies, and misaligned implementation approaches create long-term inefficiencies in AI systems, and learn strategies to avoid these pitfalls
• Understand the importance of designing for longevity through modular, component-based architectures that extend the lifecycle of AI systems, enabling you to integrate emerging models (like GenAI) without rebuilding from scratch
• Learn how to prioritize AI initiatives by evaluating not only their potential value but also their resilience to change to determine whether an initiatives long-term upkeep is justified in the rapidly evolving AI landscape 

Brent Railey

Chief Data and Analytics Officer
Chevron Phillips Chemical Company

See Bio

Today’s energy companies manage complex data ecosystems including legacy platforms, SAP and other large-scale ERPs, siloed datasets, and diverse control systems. While Agentic AI and Operational Intelligence are heavily hyped, achieving ROI and driving Top-line Revenue depends on a strong, intentional data strategy.

This interactive workshop helps you build a data foundation aligned to the six principles of AI-ready data. We’ll challenge traditional data approaches and explore a future-proof roadmap designed for scale.

Key Challenges Addressed:

• Operational Complexity: Break down data silos across exploration, production, refining, trading, retail and more
• Real-Time Requirements: Support millisecond response times in the field and at the trading desk
• Scale & Geography: Manage petabyte-scale data with global governance and security
• Regulatory Compliance: Enable audit-ready lineage and streamlined governance
• Industry Context: Support data-intensive operations from exploration to regulatory compliance
• Integration Bottlenecks: Reduce delays caused by fragmented storage and legacy systems 

Patrick Wolf

Head of Business Value Engineering
Qlik

See Bio

Agentic AI systems are capable of making autonomous decisions that can reshape workflows and unlock efficiency across the energy value chain. Yet adopting these systems in practice comes with real challenges: most AI agents are not built with the industry’s requirements for data governance, operational security, and subject-matter expertise in mind.

• Enhancing SME expertise by applying Agentic AI to energy-specific use cases
• Going beyond data queries to generate actionable insights and resolution recommendations
• Learning from real deployments at SynMax, where Agentic AI is used with highly valuable and sensitive data
• Pinpointing applications with tangible ROI, accelerating workflows, reducing bottlenecks, and creating new opportunities for innovation
• Addressing workforce readiness by identifying skill gaps and designing upskilling programs to support effective collaboration with Agentic AI 

Eric Anderson

Chief Executive Officer
SynMax

See Bio

Rahul Verma

Director of Technology
SynMax

See Bio

In high-risk, asset-intensive environments, early detection of faults and anomalies is critical to maintaining uptime, reducing risk, and optimizing performance. This workshop explores how AI-powered visual inspection technologies are transforming traditional inspection methods. By combining deep learning, edge computing, and real-time image analysis, organizations can move from reactive maintenance to predictive, data-driven decision-making.

• Use real-time image and video analytics to detect early signs of wear, corrosion, or misalignment, reducing unplanned downtime and maintenance costs
• Leverage edge AI to monitor infrastructure remotely and at scale, with instant alerts and automated fault classification, especially valuable in hard-to-access or hazardous environments
• Replace manual inspections with automated, high-frequency analysis that supports faster, more accurate operational decisions
• Explore how visual AI compares and complements other technologies like natural language processing, robotics, and drone-based inspections, each with unique strengths and limitations
• Integrate visual insights into existing workflows and control systems, while addressing challenges like data overload, system integration, and workforce readiness
• Prioritize opportunities, select the right tools, and scale adoption across your operations 

The dawn of AI assistants has revolutionized operations in asset-heavy industries, enabling faster, safer, and more productive workflows. This workshop explores how organizations can achieve transformative efficiency by building team AI capabilities, identifying high-impact opportunities, and bridging data - from corporate process data to operational data - with enterprise decision-making through intelligent AI assistants.

In this workshop, discover how to prepare your organization for AI transformation and deploy intelligent assistants to:

• Cut valuable manhours in highly manual processes that require inefficient integrations, extensive data manipulation, and repetitive knowledge work
• Digitize tribal knowledge by capturing experienced workers' insights and integrating them into AI-powered decision workflows that accelerate operations
• Make operational knowledge searchable and actionable, unifying data sources with enterprise-wide systems through robust data governance and contextualization layers
• Meet regulatory requirements through automated reporting, consolidated metrics, and exception-based workflows that reduce manual effort from hours to minutes
• Optimize inspection and maintenance workflows to reduce unplanned downtimes and enable predictive maintenance through quality operational signals
• Accelerate back-office processes by coupling operational data to billing, revenue recognition, and compliance systems with built-in governance

We’ll also explore a real-world case study with ProPetro sharing their transformation journey - demonstrating how they achieved ~50% time savings, reduced operational staff requirements, and improved billing accuracy by building foundational data quality, process redesign, and scalable operating models - now positioning for GenAI enhancement through intelligent copilots for exception handling.

Having a holistic view of your assets and workflows is essential, but without understanding why things happen, how actionable are your insights?

• Real-world use cases of causal AI in energy, from predicting and preventing equipment failure, to reducing emissions and mitigating performance degradation
• Dynamic modelling of interactions between resource allocation, process variables, and field conditions, using causal graphs to tailor recommendations for frontline teams
• Creating a feedback loop that continuously learns from operational data, enabling adaptive workflows and proactive interventions 

In this workshop you will learn how Ansys, a part of Synopsys is operationalizing the best of physics simulation, AI/ML and field data to optimize asset management with virtual sensors, virtual commissioning and predictive maintenance.

Nima Bahrami

Digital Twin Sales Executive
Ansys part of Synopsys

See Bio

Jose Luis Gonzalez Hernandez

Lead, Application Engineer
Ansys part of Synopsys

See Bio

Vitor Lopes Pereira

Regional Manager , Systems and Digital Twins, Americas
Ansys part of Synopsys

See Bio

Institutional knowledge is one of the energy industry’s most valuable assets, yet it is at constant risk of being lost through retirements, turnover, and organizational change. This masterclass explores how AI can preserve, scale, and activate that knowledge across the workforce. We will look at how captured knowledge, when paired with data, can streamline repeatable processes through AI Workflows, and how Causal Agentic AI moves beyond automation by combining expert logic and data to reason, predict, make decisions, and explain itself in context.

Ron Norris

Former Director of Innovation at Georgia-Pacific, and Founder
Advanced Innovation Management

See Bio

Matthew Alberts

Manager, Innovation and Emerging Technologies
Southern Company

See Bio

This interactive masterclass will provide you with the opportunity to actively learn the building blocks to begin developing your AI toolbox providing learnings to action long after the summit ends. The value of AI is no longer in question, therefore it’s imperative that organizations learn from the challenges and obstacles encountered by organizations of high AI level maturity. The opportunity to learn from your peers and share insightful takeaways to not only your executive team but all key business units involved in implementing AI strategies, is paramount to successful creation, implementation and adoption. This masterclass will focus on:

• The importance of creating an AI roadmap, including success metrics, resource allocation and business value alignment
• Creating a proof-of-concept environment to test AI models for hallucinations and unreliable results to ensure successful enterprise-wide operationalization with reduced friction
• Understanding data requirements for your specific AI use cases and the optimal data strategy that matches organizations objectives (futureproofing, time to operationalize, avoiding getting stuck in an endless data cleansing project)
• Creating correct oversight & governance frameworks internally to avoid wild west outcomes
• Ensuring AI readiness of infrastructure and talent to assess requirements and costs associated to modernize systems and upskill workers
• Guard railing data to ensure a secure environment to build and operationalize AI models
• Factors to consider when deciding on whether a vendor-partnership model fits your organizations business model and goals 

Arundhati Biswas

Senior Director, IT Strategy and Business Operations
National Grid

See Bio