AI Use Cases for Utilities: A Practical Framework for Deciding Where AI Actually Belongs

Utilities are facing a growing list of AI ideas and a shrinking amount of patience for pilots that go nowhere. The hard part is rarely finding an AI use case. It’s deciding which ones are worth pursuing, whether the organization is actually ready to support them, and how to move from a promising idea to a defensible investment. This framework lays out that decision path so a utility can identify exactly where it stands and what to work through next.

Who This Is For

This framework applies to electric, gas, and water utilities exploring AI across operations, asset management, field work, and customer-facing functions. It covers the full range of decisions from early opportunity identification through scaling a pilot into production, including:

• Leadership teams that have been asked to develop an AI strategy but do not know where to start
• Operations and asset management teams facing a growing list of AI ideas from different departments and no structured way to compare them
• Teams evaluating whether their data, systems, and skills can actually support a given AI use case before committing resources
• Project teams narrowing a shortlist down to a first pilot
• Finance and planning teams needing to determine whether an AI investment can be justified
• Teams that have completed a successful pilot and need a defensible plan to scale it
• Operations groups exploring generative AI or AI agents while cybersecurity and legal teams raise accountability and risk concerns

Where to Start

The most direct way in is to describe the AI opportunity, operational challenge, or decision you are exploring in your own words. A clear description, whether that’s a specific operational pain point, a list of candidate use cases, or a question about readiness, gives you the fastest path into the relevant decisions.

If you would rather start from a defined question, these six starting points cover the situations utilities most commonly find themselves in:

1. Where should we use AI? Identify utility operations and business areas where AI could create meaningful value.
2. Which AI use cases should we prioritize? Compare candidate use cases based on value, feasibility, readiness, risk, and speed to impact.
3. Are we ready for AI? Assess whether data, systems, connectivity, skills, governance, and operating processes can support the intended use cases.
4. Which use case should we pilot first? Select a practical first implementation based on impact, feasibility, dependencies, and risk.
5. Can we justify the investment? Establish expected value, implementation effort, dependencies, and business case for priority AI initiatives.
6. How do we move from pilot to implementation? Define the capabilities, governance, data, technology, operating model, and execution steps needed to deploy AI responsibly.

If you have already resolved some of these questions, such as a shortlist of candidate use cases or a completed pilot, treat that as settled and focus on the decisions still open.

The Core Decisions in an AI Use Case Plan

Not every utility needs to work through every decision below. A team with a validated pilot moving toward scale has already resolved opportunity identification and prioritization. A team still exploring where AI fits may not need a business case yet. Use the questions below to identify which decisions are still open for your organization.

1. Opportunity & Strategic Fit

Decision question: Where does AI create enough operational or business value to justify deeper evaluation? Before comparing specific use cases, identify the utility operations and business areas where AI could realistically create meaningful value, grounded in an actual operational problem rather than a technology in search of a use. This step can be brief or already resolved for utilities with a mature AI program.

2. AI Use Case Prioritization

Decision question: Which candidate AI use cases should receive attention first? Compare candidate opportunities against each other based on value, feasibility, data and organizational readiness, risk, and speed to impact, and establish a prioritized shortlist. This step is required whenever multiple opportunities exist or priorities have not yet been resolved.

3. Data & AI Readiness

Decision question: Do we have the data, systems, skills, integration, and organizational readiness needed? Assess whether the utility’s data quality, source systems (such as SCADA, GIS, and asset management platforms), technical skills, integration maturity, and operating processes can actually support the intended use cases. This is often where promising ideas stall, and surfacing the gap early avoids investing further in a use case the organization is not yet positioned to execute.

4. Pilot Selection

Decision question: Which use case offers the strongest first-pilot case? Select the candidate with the best combination of value, feasibility, risk, and speed to impact, using the evidence gathered during prioritization and readiness assessment. The result is a selected pilot with clear rationale and stated assumptions, not a guess based on enthusiasm alone.

5. Governance, Risk & Human Oversight

Decision question: What controls, accountability, and human oversight are needed to use AI responsibly? Define the governance requirements, accountability structure, and human-oversight controls needed before an AI use case moves into production, particularly for generative AI, AI agents, or any use case touching operational or safety-critical data. This can surface early when cybersecurity or legal teams raise concerns, or later as a checkpoint before scaling.

6. Economics & Business Case

Decision question: Is the expected value sufficient to justify the required investment and effort? Establish the expected operational or financial value, implementation effort and cost, and the assumptions and sensitivity behind the case, so the investment decision rests on a defensible business case rather than a pilot result alone.

7. Implementation Readiness

Decision question: Are the dependencies ready for implementation or scale? Assess the technical, data, integration, operating-model, and organizational dependencies that must be in place before a use case moves from pilot into broader deployment. This step is relevant for any project moving toward production or scale.

8. Pilot-to-Production Roadmap

Decision question: What must happen to move from pilot or a selected use case into sustainable production and scale? Translate accepted decisions and evidence into a sequenced implementation and scale roadmap, typically the final step for projects that have already validated a use case and are ready to commit to full deployment.

Principles That Keep the Plan Grounded

• Start from the operational problem, not the AI technique. A use case framed around reducing equipment failures or speeding outage response is more defensible than one framed around “using generative AI,” because the technique should follow the problem, not the other way around.
• Treat data and organizational readiness as a real gate, not a formality. Many AI initiatives fail after the pilot because the underlying data or integration work was never actually ready, not because the model didn’t work.
• Do not let interest in a specific technique like generative AI or AI agents skip governance. Enthusiasm for a new capability does not remove the need for accountability, human oversight, and risk controls before it touches operational data.
• Keep the business case tied to a specific use case, not AI in general. Value, cost, and risk vary enormously across use cases, so a generic AI business case is rarely defensible; a use-case-specific one is.
• Treat a successful pilot as the start of the next decision, not the end of the process. Moving from pilot to production requires its own readiness check on data, integration, operating model, and organizational dependencies.

Example Scenarios

• A utility whose leadership wants an AI strategy but does not know where to start
• A utility with dozens of AI ideas from different departments and no structured way to decide which are worth pursuing
• An outage-management team spending too much time reviewing information across multiple systems during major events, unsure whether AI could help
• A utility comparing AI use cases across vegetation management, asset inspection, outage prediction, and field workforce productivity
• A team concerned that operational data fragmented across SCADA, GIS, and asset management systems may undermine several AI ideas at once
• A utility that selected predictive maintenance as a priority but is unsure whether it has enough historical asset and failure data to make it viable
• A team narrowing a shortlist of transformer failure prediction, drone image analysis, and outage prediction down to a single first pilot
• A utility with an approved pilot budget that needs a use case capable of demonstrating measurable operational value within six months
• A team believing AI-based predictive maintenance could reduce truck rolls and equipment failures, but needing to determine whether the investment can be justified
• A utility that completed a successful computer-vision pilot for asset inspection and now needs a plan to deploy it across the full service territory
• An operations team wanting to use generative AI and AI agents while cybersecurity and legal teams raise concerns about operational data, model errors, and accountability

Frequently Asked Questions

Do we need a specific AI technique in mind before we start? No. The right starting point is identifying where AI could create real operational or business value. Committing to a technique such as generative AI, computer vision, or predictive AI comes later, once the use case and readiness picture are clearer.

We already have a long list of AI ideas. Where do we start? Start with prioritization. Comparing candidate use cases on value, feasibility, readiness, risk, and speed to impact turns a long list into a defensible shortlist, which is usually more valuable at this stage than adding more ideas.

What if our data isn’t in great shape? That is common, and it is exactly what the readiness step is for. Identifying data, integration, or skills gaps early prevents investing further in a use case the organization cannot yet support, and often points toward a different, more achievable first use case.

Does interest in generative AI or AI agents change the process? It raises the importance of the governance step. Generative AI and AI agent use cases typically bring earlier and closer scrutiny from cybersecurity and legal teams around operational data, model errors, and accountability, so governance may need to be addressed sooner rather than later.

We already ran a successful pilot. What’s next? Scaling a pilot into production is its own decision, not an automatic next step. It requires assessing technical, data, integration, operating-model, and organizational dependencies, then sequencing the path from pilot to sustainable production.

What should we have by the end of this process? Depending on which steps apply to your project, you should come away with a defined opportunity or strategic-fit statement, a prioritized use-case portfolio, a readiness assessment with clear gaps and dependencies, a selected pilot with rationale, governance and risk requirements, a value and business case, an implementation-readiness assessment, and a sequenced pilot-to-production roadmap.

Get Help Working Through This

TeckNexus works with utilities at every stage of AI use case planning, from early opportunity identification through scaling a validated pilot. Describe your AI challenge, opportunity, or project, and we will help you identify where you stand and what to work through next.