Discussion goes beyond tools and technology to explore the fundamentals - data quality, governance, culture, and leadership—that separate high‑impact initiatives from costly experiments. Attendees will walk away with practical insights, hard‑earned lessons, and a clearer view of how to build trusted, value‑driven AI into everyday business operations:

• Foundations First: Why people, process, and data must come before technology—and how strong fundamentals unlock real AI value
• Making AI Work in the Organization: What’s actually worked when setting up IT and business teams with AI, how to upskill the workforce to meet business outcomes
• Governance Without Gridlock: How to implement the right structures to manage risk, build trust, and scale AI responsibly

• Replacing fragmented dashboards and static reports with a single AI-driven decision layer across operational data

• Enabling engineers, controllers, and executives to query data directly and generate insights in minutes, without analyst dependency

• Generating live dashboards and board-ready outputs on demand, aligned to how decisions are actually made in Canadian operations

• Future-proofing digital strategies through model-agnostic AI integration that avoids vendor lock-in and keeps pace with evolving technology

• Explaining the technical drivers behind the shift to agentic AI: compute, algorithms, data scale and cost, in language business stakeholders can understand.
• Showing what “agents” actually do on real Canadian use cases, from automating multi-step workflows to coordinating with humans.
• Equipping technologists to explain this shift credibly to CIOs, COOs and regulators.

• Reframing AI strategy away from chatbots and agent trends toward redesigning the work that creates value.
• Redesigning workflows and decision paths to embed AI where it changes speed, quality, and cost to serve.
• Reshaping roles, accountability, and human to AI teaming to unlock operating leverage without breaking governance.
• Upskilling teams with the practical capabilities needed to adopt AI at scale and sustain measurable performance gains.

• Sharing quantified results from predictive maintenance, leak detection and commercial optimization on Canadian assets.
• Discussing how Calgary based operators are deciding where to double down, where to pause, and how to stop “pilot sprawl.”
• Comparing governance and investment models in large integrated companies versus mid-size independents.

• Discover how a Canadian industrial services company launched its AI transformation using an internally developed roadmap - without overspending or relying on a large IT team.
• This session highlights a real-world use cases that delivered measurable results for Accounts Payable and other business areas.
• Walk away with actionable insights, lessons learned, and practical strategies to help your organization unlock AI’s potential on a realistic budget.

Three rapid 15-minute talks. One messy data problem, one approach, one result.

• Ensuring the First Mile of Data - Jesus Borobia, PowerTrunk
• AI governance and guardrails in safety-critical, highly regulated environments - Alain Siewe - Candidate, Doctor of Business Administration – University of Calgary
• SLB’s Tela: Agentic AI enabling domain expertise for the subsurface and automated workflows - Steve Dahl, SLB

• Identifying the specific new risks introduced by GenAI and agentic AI for Canadian operators: hallucinations, IP leakage, bias and safety-critical decision support.
• Comparing approaches to guardrails: internal policies, vendor controls, prompt retention, auditability and “kill switches.”
• How legal, cyber, data and operations teams in Calgary are sharing accountability for AI governance instead of passing the buck.

The Enbridge Innovation Team will deliver two 15-minute presentations. Across the session, the team will also reflect on the challenges the Lab had to overcome, the new types of issues that emerged during implementation, and what they are working on today.

Presentation 1: Responsible AI and AI Literacy

Allan Cheung, Enbridge

Presentation 2: Machine Learning to Cut Pipeline Power Costs Without Compromising Safety A product-specific presentation exploring how machine learning is being used to reduce pipeline power costs while maintaining safety standards.

Fed Briceno and Kevin Wuerstl, Enbridge

• Defining when an AI agent can act, when it must ask, and when it must stop in pipeline, plant and field contexts.
• Designing user interfaces and notifications that engineers, controllers and field staff will actually trust and respond to.
• Building auditability and reversibility into every agent workflow so operations keeps ultimate control.

• Defining high consequence events in OT and critical infrastructure, for prioritizing what truly matters, to protect safety, uptime, and public trust.
• Mapping threat vectors and attack paths back to those high consequence outcomes, for focusing controls on real operational risk, to reduce blind spots and wasted effort.
• Applying the CCE methodology to identify critical functions and weakest links, for strengthening resilience, to prevent disruption of essential services.
• Aligning engineering, operations, and cyber teams around consequence based risk decisions, for clearer accountability, to accelerate action without compromising safety.

Explore real-world lessons and case studies on how energy leaders design intuitive human-AI interactions, scale impactful use cases, and orchestrate technology across the AI landscape to unlock business value. In this session, we'll dive into the following topics:

· Designing human-AI interactions that drive adoption

· Where Energy organizations typically start, and how they scale to maximize impact

· How to set up an AI technology ecosystem that serves end-to-end business processes

Rapid 15-minute talks to spark discovery.

• Explainability and the operational value created for our automated mining haul system at Kearl - Bryan Kan, ExxonMobil and Hannah Zhao, ExxonMobil
• Own Your Data, Own Your AI: Sovereign Compute at the Industrial Edge - Glen Crockford, Telus
• Session by Accelerated Focus - Travis Cavanagh, Accelerated Focus

• Highlighting the themes, tensions, and practical takeaways that emerged across Day 1 discussions
• Identifying where operators are seeing real traction, where pilot fatigue is setting in, and where tougher decisions still need to be made
• Connecting Day 1 lessons to the Day 2 agenda, from data foundations and governance to workforce readiness and scaled deployment

• What is working in enterprise AI today, and where organisations are seeing measurable outcomes
• Why deployments stall after the pilot stage, including data, operating model, risk, and adoption blockers
• Where agentic AI and automation are taking hold first, and what “safe to scale” looks like in practice
• The emerging blueprint for AI ready organisations, from governance to delivery and change management
• What leaders should prioritise over the next 12 to 18 months to move from pilots to portfolio value

• Identifying the few AI competencies operators actually need (by role) for avoiding generic training, to improve day-to-day usage.
• Embedding governance into workflows for reducing mistakes, to make adoption safe in control rooms and the field.
• Creating feedback loops between users and model owners for preventing drift, to keep performance reliable over time.
• Standardizing what “good” looks like for scaling faster, to replicate success across assets.

• Using AI on LDAR, satellite and sensor data to cut false positives and focus field crews on real leaks.
• Shortening the time from detection to fix for methane and other emissions under Canadian and provincial regimes.
• Integrating AI workflows with existing environmental management systems and reporting processes.

• Exploring where agentic AI can automate end to end supply chain workflows
• Identifying the highest value “starter use cases” in procurement, logistics, and maintenance supply
• Assessing the data, governance, and operating model guardrails needed to deploy agents safely
• Applying practical lessons on integration, human in the loop design, and change management

• Using chatbots and self-service tools to resolve simple invoice and PO queries without tying up supply chain staff. - Brian Emmerson, EY
• Applying advanced analytics and AI to identify and address risks in procurement and your supply chain. Carl Hammersburg, SAS
• Optimize operations securely with AI on the Edge - Javier Lopez, HP

Move from presentation to problem-solving in these highly interactive, peer-driven roundtables. Each roundtable covers a different topic, allowing attendees to choose the discussion most relevant to them.

Roundtable leaders will open with a brief 10-minute introduction and a short overview of the topic, which can include a case study, lessons learned, or an industry update. This is followed by a 40-minute peer discussion, with the leader facilitating questions, feedback, and practical exchange. The final 10 minutes are dedicated to aligning on five clear industry recommendations to share back with the wider audience.

Choose one topic that aligns with your most urgent priority.

1. Starting Your First Agentic AI Pilot Without Over-Promising - Travis Cavanagh, Accelerated Focus

2. Proving AI's Bottom-Line Impact: From Unit Economics to Portfolio ROI - Nizar Mohamed, IBM Consulting Canada

3. The challenges with AI: Going from POC to Enterprise Solution- Sohail Thaker, Sia Partners

4. Winning Hearts and Minds: Change Management for AI on the Frontlines - Mitch Perreault, Celonis

5. The Data Defects That Derail AI: Prioritizing Quality for Trusted Answers - Stephen Petch, Wrench Solutions

6. Your AI Vendor Blueprint: Build, Buy, or Bolt-On for 2026-2028 – Glen Crockford, Telus

7. LLMs vs. Small Models: Matching the Tool to the Operational Task - Amir Shamsa, SLB

8. AI at the Edge: Solving Power, Connectivity, and Harsh Environment Challenges - Jesus Borobia, PowerTrunk

9. Contracts at Full Throttle: Automation for a Faster, Smarter Energy Industry - Mohan Mailvaganam, Xerox

10. Supply Chain and Contractor Management - Using AI Without Breaking Relationships – Carl Hammersburg, SAS

11. AI at the Edge: Secure, private and customized to your needs - Kevin Campbell, HP

12. Cybersecurity & AI- Amir Ashuri, EY

13. GenAI Disrupting Oil & Gas: Rapidly Deploying the Use Cases That Drive Margin, Safety, and Speed- Rodrigo Coronado, Avanade

14. We Have the Data. We have the tools. So Why Are Our Biggest Decisions Still the Slowest Ones We Make - Yatharth Sejpal, KNOWIDEA

15. AI with Purpose: Driving Transformation Through People, Process, and Performance - Gulnara Omar, Gulnara Omar Consulting

16. Beyond the Bot: Orchestrating AI Agents and Human Workers Across Canadian Energy Operations
Amber Hanna, SS&C Blue Prism

• Sharing operator results for maintenance, production tuning, and leak detection to show real outcomes
• Quantifying value using fewer incidents, higher output, and lower energy cost to build the business case
• Shortening time to value by targeting data that already exists to move fast
• Sustaining results by tracking model quality and user adoption to avoid drop off

• Deploying AI models that can run reliably at remote well sites, compressor stations and power facilities.
• Managing model drift, updates and synchronization when connectivity is intermittent.
• Defining the right balance between edge processing and cloud intelligence for Canadian geographies.

• Data Quality, Retrieval and Trusted Metrics Across the Asset Lifecycle - Bob Jessey, SIA Partners & Matt Skolnik, Engagement Director, Sia
• AI Is Only as Smart as Your Data: Modernizing Land & Asset Intelligence to Scale Digital Transformation - Samantha Bush, Pandell. An ESG Company

• Assessing how analytics, data science, and AI are currently deployed across energy operations, and where agentic AI is beginning to add value

• Evaluating which use cases are delivering measurable impact, and identifying where AI has fallen short or introduced unnecessary complexity

• Building the right balance of people, process, and data literacy to ensure teams apply AI where it truly improves outcomes

• Exploring the next wave of opportunities for AI in energy, and how organizations can position themselves for value over the next 2 to 3 years