You know what they say about power: it never sleeps. Neither do the challenges facing today’s utilities. Grid reliability, customer expectations, budget constraints…The old ways aren’t cutting it anymore.

Electricity demand is hitting record levels through 2026. Data centers, manufacturing and electric vehicles are all driving higher consumption while we navigate a massive transformation. We need to keep the grid reliable AND transition to cleaner energy, all with tighter budgets.

That’s why it’s time to think about AI differently. Not as another technology we need to “figure out someday,” but as a genuine partner that can help us see patterns in demand we might miss, anticipate maintenance needs before equipment fails, and give our customers clearer insights into their energy use. In an industry where resilience and reliability are everything, we need to shift from assuming things will work to actively anticipating what might not.

Where AI Can Actually Help

Let’s be honest about what AI can do for utilities. The impact spans pretty much every area of operations:

Grid Operations. AI monitors transmission lines and isolates faults in real time. Portland General Electric deployed AI cameras across fire zones to detect smoke and pinpoint ignition locations, sharing data with 40 fire agencies across Oregon. More importantly, AI turns unplanned outages into predictive maintenance. Equipment monitoring spots degradation weeks before failure, letting you schedule proactive repairs instead of emergency responses.

Customer Experience. Intelligent systems handle routine requests, bill analysis and generate work orders without phone calls. Customers ask, “Why is my bill higher?” and get real answers: Usage comparisons, weather impacts and rate changes. Systems spot unusual consumption and alert customers proactively or recommend energy-saving opportunities.

Workforce Productivity. AI eliminates tedious work such as automated data entry, intelligent document processing and streamlined compliance reporting. Staff who spent hours on spreadsheets now focus on analysis and decisions. This improves job satisfaction by letting people do meaningful work like solving problems, serving communities and keeping the power on.

Data Insights. Beyond just automating tasks, AI fundamentally changes how utilities get value from their data. We’re moving from “generate a report every month” to having continuous intelligence. You make faster, smarter decisions because the insights surface automatically instead of waiting for someone to think to ask the right question.

The Next Wave: Agentic AI

AI’s evolving beyond simple automation. We’re moving from generative AI (creates content on command) to agentic AI (understands context, makes decisions and takes action across systems).

Ask your system “What maintenance should we prioritize this quarter?” Agentic AI analyzes equipment data, evaluates risks, considers budget and produces an action plan. Customer reports flickering lights? System checks grid status, reviews account history, schedules inspection and follows up automatically, handling routine cases.

Within two to three years, expect these workflows in core applications with embedded intelligence augmenting expertise.

How We’ll Actually Work: From Clicking to Talking

Here’s maybe the biggest shift: How we interact with information is about to change completely. For decades, we’ve relied on reports generated on schedules. AI enables conversational data interactions.

Instead of clicking through dashboards, people just ask, “Which substations showed weird voltage patterns during last week’s heat wave?” The AI gets it, pulls data, runs analysis, creates visualizations and finds correlations you didn’t ask about.

This is bigger than a chat feature. Today’s utility software is packed with tabs, dropdowns and nested navigation that reflect older technology. Conversational interfaces flip it. The interface adapts to you. Simply ask a question to get relevant data that is formatted properly. The conversation IS the navigation.

Field supervisors analyze progress without software expertise. Customer service managers investigate patterns without database knowledge. Finance explores scenarios by asking, not modeling. By late 2026, this becomes normal.

The Trust Question: Choosing Vendors Carefully

As AI capabilities spread, here’s your challenge: Every vendor will promise amazing efficiency…But only if you hand over all your data. For member-owned utilities, be careful here.

Member data is a trust obligation. When members share consumption patterns, payment histories and service interactions, they expect responsible stewardship. That trust becomes your competitive differentiator.

Watch for “snake oil” promises. Vendors making big claims without transparency about how their AI works, what data they need, where it’s stored and how it’s protected…All red flags. Legitimate implementations provide clear documentation about data usage, model training and security controls.

Here are some questions to ask every AI vendor:

• Where exactly is our data stored and how is it processed?
• Are you using our data to train models that benefit other utilities?
• What specific security certifications does your AI system have?
• Can we audit how your AI makes decisions?
• What happens to our data if we stop using your service?

Utilities that set up strong AI governance frameworks and demand real transparency from vendors are positioning themselves well. Those rushing into AI without these protections risk breaking the trust that defines their customer relationships.

What’s Coming: The Regulatory Piece

If you’re getting ready to implement AI, know where regulation is headed. The Trump administration’s recent executive order signals federal interest in uniform frameworks. Meanwhile, California, New York and Colorado passed AI transparency laws. The EU AI Act sets requirements for “high-risk” systems starting August 2026.

For utilities, it is important to keep an AI inventory documenting what you use and why. Have explainability standards so you can explain how AI reaches conclusions. Maintain vendor evaluation documentation. Monitor for bias in AI-driven decisions.

The good news? Transparency requirements align with how public power already works. You explain decisions to member boards and communities. Same accountability works for AI.

How to Actually Get Started

Utilities moving beyond pilot projects to actual embedded AI capabilities are following a pretty consistent approach, and it works.

Start with governance. Set clear policies on what’s acceptable AI use before you start deploying things. Figure out who can approve AI implementations, what data can be used for training and what kind of human oversight you need for AI-assisted decisions.

Identify your champions. You need people across departments who get both the technology and the operational needs. They’re the bridge between IT capabilities and what actually has to work.

Begin with low-risk, high-value stuff. Customer service automation, energy forecasting, work order routing and compliance reporting that’s eating up time. These deliver value while your organization learns.

Scale knowledge and trust. Share what’s working. Explain how the AI works and what safeguards are in place. Address concerns directly. Build confidence through showing, not just telling.

Common Pitfalls Worth Avoiding

As utilities dive into AI, there are some patterns worth learning from such as mistakes early adopters made that you don’t need to repeat.

Waiting for perfect clarity. AI evolves fast. Waiting for “the right moment” means falling behind. Start with low-risk pilots now.

No governance framework. Shadow IT with AI is dangerous. Establish policies and approved tools before adoption creates security gaps.

Skipping training. Tools without education lead to poor results and resistance. People need to know when to trust AI and when to override it.

Treating AI like magic. It’s a tool requiring thoughtful implementation, oversight and refinement.

Forgetting change management. AI changes workflows. Address concerns, communicate benefits and support transitions.

Not measuring impact. Track time saved, quality improvements and user satisfaction from day one. Metrics justify investment and guide refinement.

The utilities thriving with AI won’t be the ones with the most of it. They’ll be the ones implementing it thoughtfully, avoiding these pitfalls while building on a clear vision, practical pilots, strong governance and continuous learning.

The pressures are real: Unprecedented load growth, aging equipment, evolving expectations, retiring expertise and climate impacts. AI offers solutions across all of it, but only when implemented with public power’s core values of accountability to communities, stewardship of resources and reliable service at fair cost.