AI Playbook for Utilities

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Many AI projects fail before completion. The technology isn’t the problem. Skipped preparation steps are.

THE PATTERN

Utilities are being pitched AI for everything

Predictive maintenance. Demand forecasting. Capital planning. Leak detection. Vegetation management. The demos look impressive, and the ROI projections are compelling.

But the odds of your AI effort delivering the outcomes you expect aren’t good. Many AI projects fail before completion, and they fail for the same reason: critical preparation steps get skipped.

This playbook outlines the steps to take before you invest, so your utility avoids the common failure points and gets the outcomes the demos promised.

KNOW BEFORE YOU INVEST

AI isn’t equally useful everywhere

AI excels at specific problems utilities face, and it struggles with others. Knowing where AI
fits helps you avoid investing in a solution that won’t deliver.

Where AI Works Best

✓
Pattern Recognition at Scale
Finding correlations across millions of data points no human analyst could spot.
✓
Anomaly Detection
Identifying early warning signals buried in sensor data, SCADA readings, or consumption patterns.
✓
Optimizing Complex Trade-offs
Balancing multiple competing factors to recommend optimal decisions.
✓
Demand Forecasting Under Changing Conditions
Projecting future needs by integrating historical data with weather, population growth, and climate trends.

Where AI Struggles

–
Data Collection
The technology itself works well here. The bottleneck is getting data into enterprise systems in usable formats and maintaining it over time.
–
Replacing Institutional Knowledge
AI augments human expertise. It doesn’t replace decades of field experience.
–
Problems with Unclear Definitions
Vague goals like “make operations more efficient” don’t translate to deployable AI..

AI trained on poor data produces confidently wrong answers. Confidently wrong is worse than obviously wrong.

Inside the Playbook

The four steps, in detail

Skip one of these and your AI investment risks becoming an expensive lesson instead of a working system.

Define the Problem

A vague goal won’t get you deployable AI.

Most AI projects fail because utilities never define exactly what they’re solving. Get specific about whether you’re reducing emergency responses, optimizing replacement timing, or prioritizing high-consequence repairs before you evaluate a single vendor.

Audit Your Data Foundation

AI amplifies your data, for better or worse.

Most GIS and asset management data isn’t AI ready. Check system integration, data completeness, consistency, and currency before you invest in a platform built on top of what you have today.

Design Around Real Workflows

Technically perfect AI fails when it doesn’t fit how your utility works.

Field crews won’t adopt tools that add disjointed steps. Engineers won’t trust models disconnected from planning systems. Map who will use the output, and build AI to enhance existing work instead of creating a parallel process.

Pilot Before Scaling

Prove AI works at small scale before you scale the investment.

Utilities getting real value from AI tested small, proved the value, and built trust before they expanded. Pilot on a well documented network subset, define success metrics first, and stay willing to adjust or stop if the pilot reveals a real problem.

Ready to build AI on a proper foundation?

Get all four steps, in full, with the questions to ask before you invest in any AI platform.

The Pattern Behind Failed Projects

There’s a consistent pattern across failed AI projects

Early Enthusiasm

→

Visible Results in Isolated Cases

→

Gradual Stall as AI Hits Data & Workflow Limits

Your best next step: focus on foundation

Pressure to do more with aging infrastructure, tight budgets, and smaller workforces has never been greater. AI can help in practical, measurable ways once you build the foundation first.

Utilities that treat data infrastructure as strategic, define problems clearly, and design around real workflows see genuine value. Without that foundation, AI becomes another expensive platform that promised transformation and delivered disappointment.

Put real effort into planning, and the promise of AI becomes a reality for your utility.

GET THE PLAYBOOK

Get the AI Playbook

Fifteen pages on the four steps to take before you invest in AI, so your utility avoids the common failure points and builds AI that delivers.

✓Start with specific problems, not general ambitions
✓Invest in data quality before investing in platforms
✓Design around real workflows, not ideal ones
✓Treat AI as augmenting human judgment, not replacing it
✓Pilot small, prove value, then scale