PRAGMA × Project Canary Executive AI Forum

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Accelerating AI Adoption

PRAGMA and Project Canary convened senior leaders from across the energy value chain to answer one question: how do we turn AI from slideware into measurable operational wins? The consensus was clear—AI is now an operational necessity; the edge lies in adoption, not algorithms.

What’s Working Now

• Practical ML use cases first. The fastest, most defensible returns are in EHS—LDAR planning/close-out, earlier anomaly detection from SCADA/logs, and admin automation for reports, tickets, and scheduling.
• Run AI as a program, not a project. Short sprints (~6 weeks), named owners, clear goals, and tight toolsets beat sprawling pilots. Share results in business terms: hours saved, costs avoided, incidents prevented.
• Fuse signals to cut noise. Integrate continuous monitors, affordable sensors, OGI, satellites, SCADA, ops logs—and use operational context to triage alerts and reduce false positives.
• Invest in people. Effective training is role-specific; upskilling helps teams act on AI recommendations and builds trust without creating dependence. Pair tools with apprenticeships and hands-on drills.

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How Operators Are Implementing

1. Pick 3–5 MVP pilots with accessible data and accountable owners; aim for measurable outcomes within ~45 days.
2. Keep the toolset small; work in time-boxed sprints and publish plain-language guidelines on acceptable AI use.
3. Scale what works via cross-department ambassadors, light controls (data lineage, validation checks), and consistent metrics. If value isn’t clear in a month, narrow scope and try again.

Bottom Line

Focus on machine-learning-first use cases that deliver trusted, decision-ready outputs. Grow through short, well-governed cycles that emphasize culture, incentives, and data quality.

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