• Reviewing real-world results from predictive maintenance, production optimization, and leak detection to demonstrate tangible improvements in uptime, output, and emissions.
• Comparing pilot project results with year-round operational data to highlight the real-world impact and sustainability of AI solutions.
• Analyzing why some projects stalled-due to poor data, IT/OT handoff issues, or model drift-to help attendees avoid common and costly pitfalls.
• Identifying scalable patterns like small on-site models and trusted data search to guide effective and expandable AI deployment across assets.

• Defining clear boundaries for what AI can see, suggest, and automatically change to reduce operational risk and prevent scope creep.
• Establishing approval workflows and emergency stop procedures to ensure human accountability remains central to safety-critical decisions.
• Testing all AI-driven changes in a safe, simulated environment before live deployment to prove reliability and prevent costly downtime.
• Recording every AI action and human override to create a transparent audit trail for compliance, incident reviews, and continuous improvement.

• Sharing quantified operator results for predictive maintenance, production tuning, and leak detection to identify high-value, repeatable wins.
• Translating AI outcomes into business metrics like uptime and throughput to build compelling business cases and secure executive funding.
• Detailing the playbook for moving from a successful pilot to a multi-site rollout to accelerate deployment and maximize ROI.
• Highlighting common pitfalls in data, change management, and oversight to protect safety and ensure long-term user adoption.

Three rapid 15-minute talks to spark discovery. One problem, one approach, one result.

• Implementing RAG (Retrieval-Augmented Generation) with effective connectors, caching, and guardrails to ensure AI provides accurate, context-aware answers from enterprise data.
• Structuring schemas and metadata to dramatically improve the quality and relevance of information retrieved by AI systems.
• Tracking data-quality metrics that directly correlate to financial outcomes to prioritize cleansing efforts and justify data governance investments.

Instructions

• • MIN 0-1: FIND your most compelling takeaway from the Lightning Round.
  • MIN 1-3: SHARE with a partner: The idea is… and it’s valuable because…
  • MIN 3-4: COMMIT to one next step: “One thing I will do to explore this is…
  • MIN 4-5: SUBMIT your partner’s most promising action step via Slido.

• Applying AI to analyze unstructured text from daily morning reports to automatically identify operational insights, risks, and non-productive time.
• Transforming a manual, time-consuming documentation process into an automated source of actionable intelligence for drilling and completion teams.
• Connecting insights from morning reports with real-time operational data to create a more complete picture of wellsite performance and safety.

• Leveraging the OSDU data platform to break down subsurface and operations data silos, creating a unified foundation for AI applications.
• Ensuring AI models have access to trusted, well-contextualized data with clear lineage to improve the accuracy and reliability of their outputs.
• Navigating the practical challenges of implementing OSDU from data migration to taxonomy alignment to enable scalable AI solutions across the asset lifecycle.

• Designing approval chains and audit trails that align with existing OT safety procedures to build trust with field personnel and engineers.
• Implementing kill-switches and rollback protocols that give operations ultimate control over any AI-driven action or recommendation.
• Clarifying the RACI matrix for AI sign-off in OT environments to ensure clear accountability and prevent operational disruptions.

• Automating the conversion of Standard Operating Procedures (SOPs) and permit documents into clear, actionable tasks for field crews.
• Reducing manual administrative work and interpretation errors to increase crew efficiency and enhance compliance with safety procedures.
• Streamlining the workflow from documentation to execution to accelerate job startup times and improve overall operational tempo.

• Evaluating the trade-offs between large language models (LLMs) and smaller, domain-specific models based on cost, latency, and accuracy requirements.

• Selecting the right model architecture for the specific task from generative text applications to real-time sensor analytics to optimize performance and value.
• Balancing the flexibility of general-purpose LLMs with the precision and efficiency of smaller models to meet the stringent demands of operational environments.

Three rapid 15-minute talks to spark discovery. One problem, one approach, one result.

• Designing approval and escalation workflows for autonomous AI agents to ensure humans are involved in critical or exceptional decisions.
• Selecting the right AI tool for each task based on a balance of cost, latency, and accuracy to optimize overall system performance and ROI.
• Determining the optimal points for human-in-the-loop interaction to maintain safety and control without creating unnecessary bottlenecks.

• Integrating AI-generated predictive maintenance alerts directly into the Computerized Maintenance Management System (CMMS) to automate work order creation.
• Closing the gap between data insight and physical action to ensure maintenance is performed proactively, preventing failures and maximizing asset uptime.
• Tracking the full lifecycle from AI prediction to work completion to measure the true financial impact of predictive maintenance programs.

Get a rapid-fire preview of the future in this high-energy session. Ten hand-picked start-ups will pitch their groundbreaking AI solutions for oil and gas in just six minutes each.

• Witnessing a curated showcase of the most promising AI technologies, from sector-specific LLMs to robust edge computing platforms.
• Evaluating real-world applications and quantifiable results that address your toughest operational challenges.
• Voting live for the most compelling solution, crowning the winner and shaping the future of the vendor landscape.
• Connecting with the next generation of technology partners poised to transform the industry.

• Building a strategic portfolio approach to AI investment to move from scattered pilots to coordinated, enterprise-wide transformation.
• Establishing a center of excellence and cross-functional steering groups to standardize best practices and accelerate organization-wide learning.
• Developing a vendor and technology strategy that balances build-vs-buy decisions to ensure scalability, interoperability, and long-term value.

• Defining the clear thresholds and business rules that determine when an AI agent can act autonomously and when it must seek human approval.
• Implementing robust audit trails and rollback capabilities for all autonomous actions to ensure safety and provide accountability.
• Balancing the efficiency gains of automation with the irreplaceable judgment of human experts in complex or high-consequence situations.

• Applying non-deterministic AI models to real-time process data to identify hidden optimization opportunities for energy consumption on brownfield assets.
• Reducing energy costs and carbon emissions without major capital investment by making continuous, AI-driven adjustments to existing control systems.
• Demonstrating how AI can learn and adapt to the unique operating signature of individual assets to uncover sustained performance gains.

• Leveraging AI to analyze LDAR and sensor data, dramatically reducing false positives to focus field crews on genuine leak events.
• Accelerating the find-to-fix timeline by providing crews with precise location data and prioritized repair schedules to maximize emissions reduction.
• Improving the overall efficiency and cost-effectiveness of methane management programs to meet regulatory and ESG commitments.

• Implementing exception-based surveillance powered by AI to shift engineers from monitoring data to managing by exception, focusing only on assets needing attention.
• Generating daily AI summaries for well and asset performance triage to accelerate decision-making and prioritize intervention efforts.
• Using machine learning to identify schedule risks in turnaround planning, enabling proactive mitigation and protecting project timelines.

Instructions

• MIN 0-1: FIND your most compelling takeaway from the Lightning Round.
• MIN 1-3: SHARE with a partner: The idea is… and it’s valuable because…
• MIN 3-4: COMMIT to one next step: “One thing I will do to explore this is…
• MIN 4-5: SUBMIT your partner’s most promising action step via Slido.

• Codifying the specific scenarios, risk levels, and data confidence thresholds that trigger a mandatory hand-off from AI to a human operator.
• Designing seamless user interfaces that make hand-off requests clear, context-rich, and easy for humans to action quickly.
• Ensuring these rules are baked into the core of AI system design to build a foundational layer of operational safety and trust.

Move from presentation to problem-solving in these highly interactive, peer-driven roundtables. This is your opportunity to roll up your sleeves and tackle your most pressing AI implementation challenges with a small group of fellow operators facing the same hurdles.

Choose one topic that aligns with your most urgent priority.

• Building Your AI Center of Excellence: Scope, Charter, and RACI
• Proving AI's Bottom-Line Impact: From Unit Economics to Portfolio ROI
• Fueling GenAI with OSDU: Taxonomies, Security, and Speed
• Governing Autonomous Agents: Approval Matrices, Audit Trails, and Rollback
• Winning Hearts and Minds: Change Management for AI on the Frontlines
• The Data Defects That Derail AI: Prioritizing Quality for Trusted Answers
• Your AI Vendor Blueprint: Build, Buy, or Bolt-On for 2026-2028
• LLMs vs. Small Models: Matching the Tool to the Operational Task
• AI at the Edge: Solving Power, Connectivity, and Harsh Environment Challenges
• The AI-Powered M&A Playbook: Rapid Data Integration in the First 12 Weeks

• Business case that clearly articulates the problem and the proposed solution

• Compelling financial metrics that resonate with decision-makers

• Clear risk mitigation and value creation metrics

• Navigating the customer's budget approval process

• Enabling champions to become effective advocates

• Deploying AI models capable of operating reliably in harsh environments with limited power, connectivity, and computing resources.
• Managing model drift at the edge by establishing triggers for retraining and developing efficient data syncing strategies with the cloud.
• Ensuring the robustness and failure-resilience of edge AI systems to maintain operational integrity and data continuity in remote locations.

• Running sophisticated visual inspection and anomaly detection models directly on edge devices to identify leaks and corrosion in real-time.
• Minimizing bandwidth usage and latency by processing video and sensor data locally, sending only alerts and key metadata to central systems.
• Enabling immediate response to integrity threats by providing field personnel with instant notifications and evidence, enhancing safety and reducing environmental impact.

• Building retrieval systems that work in real-world OT environments by focusing on robust connectors, intelligent caching, and safety guardrails.
• Curating metadata and taxonomies that are purpose-built for operations to dramatically improve the relevance and accuracy of AI-generated answers.
• Monitoring data-quality KPIs that are explicitly tied to financial outcomes to focus data management efforts on what truly impacts the bottom line.

• Automating the triage and resolution of common, low-risk service tickets using AI to free up skilled personnel for more complex tasks.
• Learning from historical ticket data to intelligently route and escalate complex issues to the appropriate human expert along with relevant context.
• Reducing resolution times and improving workforce productivity by creating a seamless collaboration between AI and human teams.

See AI move from concept to concrete reality. In this rapid-fire showcase, we move beyond slides to live demonstrations that prove the operational and financial value of AI on real-world oil and gas challenges.

• Witnessing five distinct, end-to-end AI solutions operating in a live environment to understand their practical application and limitations.
• Evaluating the integration of AI into core operational workflows from subsurface analysis to regulatory compliance to assess their readiness for your assets.
• Validating vendor claims with live metrics and before/after comparisons that demonstrate clear uplift, accuracy, and speed.
• Identifying the specific technology partners and architectural patterns that can accelerate your own AI deployment plans.