A four-step approach to realizing broader operational value from AI
AI adoption is growing across utilities, but deploying more tools and agents does not automatically produce broader operational gains. Individual tasks may get faster while manual reconciliation, repeated validation, and cross-functional handoffs remain embedded in the surrounding workflow.
Beyond the Copilot productivity boost: Scaling AI value across utility operations explores a practical progression from focused AI use cases to broader workflow redesign. The white paper examines the capabilities utilities can build along the way, including use case prioritization, governance, reusable development practices, workforce enablement, and measurement.
You’ll also see insights from a utility AI program that expanded from early use cases to more than 100 production-ready AI agents, more than 5,000 additional agents built by utility teams, and more than 400 employees trained in AI.
Key takeaways
Evaluating AI use cases for value
Focused use cases vs. larger transformations
Foundations for scaling AI
Redesigning workflows around AI
Measuring operational AI value
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A glimpse into the white paper
Step 3: Reimagine larger workflows
High-friction processes with multiple handoffs, decisions, and cross-functional dependencies are strong candidates for AI-enabled redesign. An end-to-end process review examines AI’s potential role in process execution and identifies steps for elimination, simplification, automation, or restructuring.
Redesign also addresses the flow and validation of information. AI may support earlier data assembly, automate some validation, and reduce repeated reconciliation. For example, a regulatory process that draws inputs from multiple sources could use AI to assemble and validate data before review, reducing manual reconciliation later in the process.
The redesigned workflow assigns AI to appropriate automation and decision-support roles while preserving human oversight, judgment, and accountability based on business and risk requirements.
Explore the four-step approach
Download the white paper for the complete framework for prioritizing AI use cases, building capabilities for expansion, redesigning larger workflows, and measuring operational value.
About the authors
Lionel Bodin leads Logic20/20’s Digital Strategy & Transformation practice, helping organizations set digital and AI strategy, build the operating models to sustain it, and move agentic AI from experimentation into core operations. With more than 20 years in consulting and technology leadership, he has led transformation programs across utilities, financial services, insurance, and enterprise technology.
Tom Cunnie is a Manager in Logic20/20’s Digital Strategy and Transformation practice, specializing in AI readiness, strategy, and governance. Drawing on a background in systems analysis and technical project leadership, he brings both business and technical acumen to designing practical AI strategies that deliver measurable value.