• AI growth is rapidly increasing energy demand nationally and globally
• AI-based data centers require reliable, and clean base-load energy sources
• Solutions lie on the form of new nuclear using scalable Small Modular Reactors (SMRs)

• Real time simulation at scale to improve energy efficiency, reduce flaring and unplanned shutdowns, and stabilize operations

• AI driven scenario planning to compare different options for reducing emissions, such as changing operating conditions, maintenance timing, or electrification before making real world changes

• Geospatial dashboards to give leaders a clear, map based view of emissions, asset performance, and risks across the entire enterprise

• The daily struggles in the life of a foreman
• Taking the holistic efficiency & impact-based approach
• Shortest route for doers, data and decision makers with a bi-directional Control Tower & the Digital Foreman
• Introducing the PACE Project Guardian – 24x7 assistant to the human for data, workflows and predictions

• Logistics is a key National Security asset
• Cybersecurity for Critical Infrastructure
• Zero Trust models for OT/IoT

• See how AI turns daily planning conversations into a powerful new lens for risk intelligence and field leadership, revealing the hidden signals most indicative of safety risk, operational disruption and rework, and high financial impact.
• Learn how AI-enabled benchmarking and real-time insights give leaders clear visibility into field work planning, uncovering where to focus attention and coaching for earlier intervention, target leadership support, and achieve performance gains.
• Discover new approaches to deliver insights to field crews using AI and natural language dialogue rather than static reports, empowering crews to make better decisions, adapt effectively, and take action before small issues escalate.

• Despite major investments in monitoring and anomaly detection, unplanned failures remain a persistent challenge in Oil & Gas because traditional approaches are reactive.
• This talk explores the critical difference between detection and prediction, showing why true failure prevention requires anticipating issues before they occur. It outlines why prediction is fundamentally harder—requiring identification of time-dependent precursor patterns—and presents a practical path from detection to prediction using modern AI techniques and domain knowledge.
• Attendees will learn why detection alone is insufficient and how to bridge the gap between AI innovation and real operational impact.

• How digital twins can be used as a unifying safety backbone—connecting people, processes, and technology into a single, integrated system.
• Drawing from real-world experience building and scaling digital twins within a large reliability and maintenance organization, this talk explores how spatial intelligence, asset context, and operational data can be brought together to proactively identify risk, prevent incidents, and close safety gaps at scale.
• Rather than treating safety as a standalone program, digital twins enable safety to be embedded directly into daily execution—maintenance planning, lockout/tagout workflows, access validation, condition-based monitoring, and technician decision-making.
• How an integrated digital twin approach improves system integrity, enhances visibility across the asset lifecycle, and enables closed-loop feedback between frontline observations and leadership decisions

• How a scan-agnostic visual positioning layer enables AR work instructions, AMR/drone navigation, and QA workflows to share a single coordinate system across dynamic environments.
• Drawing from real deployments, we'll cover what actually determines field success: time-to-lock, offline resilience, map update cadence, and security requirements for GPS-denied industrial sites.
• Attendees will leave with a vendor-neutral framework for evaluating spatial infrastructure readiness and a clear picture of deployment archetypes that deliver measurable uptime and safety outcomes.

• Digital twins create change in the real world, but are often designed as software only bundles of capabilities.
• Multiple digital twin projects are “flaming out” 2-3 years after inception, due to lack of measurable improvements and low adoption.
• In order to avoid the no-ROI situation, digital twins require strong product thinking to create, measure and improve change in the real world.

Industrial facilities generate massive volumes of 3D scan data, yet most of it is never operationalized. At the same time, as-built conditions often diverge from existing documentation, limiting visibility into what actually exists in the field. This session explores how AI can transform scan data into structured, queryable systems. By identifying components, capturing as-built conditions, and enabling direct interaction with physical assets, teams can move beyond static visualization toward usable, system-level intelligence. Attendees will gain insight into how this approach improves facility visibility, supports process safety initiatives, and enables more effective inspections and digital walkdowns.

• A deterministic alternative to manual, consultant-led modernisation discovery — and what it changes for cost, timeline, and risk
• How to ground AI coding tools in real system intelligence so modernisation work doesn’t pile up new context debt as it retires the old
• Where to start: identifying the highest-leverage legacy system in your portfolio and the questions to ask before kicking off discovery

The session will explore how organizations in asset-intensive sectors such as oil & gas move from AI pilots to accountable operational decisions — and what governance, leadership, and system design are required when AI begins influencing capital, safety, and operational outcomes.