What energy leaders need to know about agentic AI and a realistic assessment of what's working today.
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Industrial organizations have invested billions in digital transformation, yet 80% of operational knowledge remains inaccessible to those who need it most. Engineers still spend hours piecing together information from disparate systems. Critical patterns hide in plain sight.
This isn't a technology problem. It is a context problem. And recent advances in agentic AI are making it solvable.
This guide examines where industrial AI stands today: what is actually delivering results in production environments, why approaches that work in consumer applications fail in heavy industry, and how organizations are moving from pilot projects to measurable operational impact.

The data accessibility challenge facing asset-intensive industries and why traditional solutions fall short
How specialized AI architectures differ from generic approaches and why that distinction matters for production-grade accuracy
Measured results from enterprise deployments, including methodology and validation
Practical applications across asset performance management, reliability, maintenance planning, and knowledge preservation
A structured approach to evaluating and implementing industrial AI