Welcome to Solutions Cast: Industry Insights. I’m Amy LaVigna, Director of Alliances and Research at CFC. Today, I want to talk about artificial intelligence in a way that is practical for electric cooperative leaders.
Here’s the big idea up front: AI probably won’t be running your cooperative tomorrow, but it can absolutely help your team work smarter today. The key is separating hype from real, usable value.
With that in mind, we’re going to cover a few different AI-topic areas in this video: first, we’re going to define what people mean by “AI;” then, we’ll talk about where co-ops are getting value from AI right now; and finally, we’ll look at using AI with guardrails—because having your cooperative's AI policy in place and managing risk matters as much as opportunity.
So how do we define AI? For electric cooperatives, AI is best understood as a range of tools that can deliver meaningful benefits, such as summarizing and generating content, recognizing patterns, making predictions, classifying information, and providing information to support decision-making. It’s important to recognize that AI isn’t just one thing.
Generative AI is what most people are talking about today because it can create things like text, images, code and more.
Machine learning models are trained on data to learn patterns and produce predictions or recommendations, potentially improving over time.
Agentic AI systems are designed to act autonomously within defined boundaries. They typically combine AI or machine learning capabilities with goal-setting, planning and task execution, while still requiring monitoring and human accountability.
That distinction matters because when you are building or expanding an AI strategy, the first questions should be: What business goals are we trying to support? And what type of AI is best suited to support them?
As co-ops explore their options, they need to determine what is realistic for them today. The most immediate value comes from helping people work through information more efficiently and effectively. Tasks like document summarization, drafting communications, and coding support where appropriate controls are in place are good starting points for any organization. AI can help us find, organize, and summarize information more quickly, but people are still the ones making the decisions.
In member support, AI can enable faster updates, more consistent responses, and improved self service while still preserving the human connection. This isn’t about replacing human judgement or involvement. It’s about enhancing it.
For utilities that are further along in their AI adoption, AI-enabled analytics are beginning to deliver measurable reliability and operational benefits. In some cases, it is acting like an early warning system. Instead of learning about a problem only after equipment fails or an outage occurs, co-ops can identify issues sooner through predictive maintenance, vegetation management and outage management tools powered by AI. Several technology providers serving the electric cooperative community now offer these types of capabilities.
Cooperative leaders who will get the most value from AI are the ones who stay grounded, ask practical questions, and focus on tools and strategies that support their organization’s goals.
So why does this matter now? First, there is the issue of workforce and knowledge transfer. Cooperatives manage a large volume of unstructured knowledge, much of it buried in reports, records, and legacy documents. AI can help make that knowledge more accessible and more useful.
Second, AI is affecting the grid from the outside as well as the inside. According to the Electric Power Research Institute, U.S. data centers could consume 9% to 17% of national electricity by 2030, driven in part by AI and broader digital demand. Whether or not your cooperative serves major data center load directly, this trend is shaping planning assumptions, transmission needs, generation discussions, and long-range reliability conversations across the industry.
Third, security and operational risk remain a high priority. The number of fraudulent techniques targeting AI-enabled systems is growing quickly, and the risks can affect safety, security, reputation and reliability. For cooperative leaders, that means any use of AI within operational technology or security systems deserves careful scrutiny.
Many organizational leaders find it daunting to know where to begin with AI. A smart approach is to start with goals and your cooperative's AI policy. That means defining the business problem, understanding the level of risk, identifying the data involved, and assigning human accountability for the output. Many organizations find it useful to begin with a small number of low-risk use cases where the benefits are easy to measure, such as drafting communications or summarizing documents. These are often the areas where utilities can see value early while limiting risk exposure.
Ultimately, success with AI depends on discipline and preparation. Co-ops that focus on practical use cases, maintain human oversight for important decisions, and treat AI with the same rigor as cybersecurity will be well-positioned to achieve their goals.
Before we wrap up, here are three things I'd encourage every cooperative leader to take away from today's conversation:
First, find use cases with limited risk exposure to start. Uses such as document summarization, drafting communications, and member support tools are great entry points.
Second, develop or review your cooperative's AI governance. A clear policy helps your team understand what's expected, what's allowed, and where the guardrails are.
And third, keep your board informed. AI adoption is a strategic decision, and your board needs to understand both the opportunities and the risks so they can support the direction you're heading.
That’s all for today. As always, thank you for joining us. Be on the lookout for more industry and technology content at nrucfc.coop/Solutions. We’ll talk with you soon!
Here’s the big idea up front: AI probably won’t be running your cooperative tomorrow, but it can absolutely help your team work smarter today. The key is separating hype from real, usable value.
With that in mind, we’re going to cover a few different AI-topic areas in this video: first, we’re going to define what people mean by “AI;” then, we’ll talk about where co-ops are getting value from AI right now; and finally, we’ll look at using AI with guardrails—because having your cooperative's AI policy in place and managing risk matters as much as opportunity.
So how do we define AI? For electric cooperatives, AI is best understood as a range of tools that can deliver meaningful benefits, such as summarizing and generating content, recognizing patterns, making predictions, classifying information, and providing information to support decision-making. It’s important to recognize that AI isn’t just one thing.
Generative AI is what most people are talking about today because it can create things like text, images, code and more.
Machine learning models are trained on data to learn patterns and produce predictions or recommendations, potentially improving over time.
Agentic AI systems are designed to act autonomously within defined boundaries. They typically combine AI or machine learning capabilities with goal-setting, planning and task execution, while still requiring monitoring and human accountability.
That distinction matters because when you are building or expanding an AI strategy, the first questions should be: What business goals are we trying to support? And what type of AI is best suited to support them?
As co-ops explore their options, they need to determine what is realistic for them today. The most immediate value comes from helping people work through information more efficiently and effectively. Tasks like document summarization, drafting communications, and coding support where appropriate controls are in place are good starting points for any organization. AI can help us find, organize, and summarize information more quickly, but people are still the ones making the decisions.
In member support, AI can enable faster updates, more consistent responses, and improved self service while still preserving the human connection. This isn’t about replacing human judgement or involvement. It’s about enhancing it.
For utilities that are further along in their AI adoption, AI-enabled analytics are beginning to deliver measurable reliability and operational benefits. In some cases, it is acting like an early warning system. Instead of learning about a problem only after equipment fails or an outage occurs, co-ops can identify issues sooner through predictive maintenance, vegetation management and outage management tools powered by AI. Several technology providers serving the electric cooperative community now offer these types of capabilities.
Cooperative leaders who will get the most value from AI are the ones who stay grounded, ask practical questions, and focus on tools and strategies that support their organization’s goals.
So why does this matter now? First, there is the issue of workforce and knowledge transfer. Cooperatives manage a large volume of unstructured knowledge, much of it buried in reports, records, and legacy documents. AI can help make that knowledge more accessible and more useful.
Second, AI is affecting the grid from the outside as well as the inside. According to the Electric Power Research Institute, U.S. data centers could consume 9% to 17% of national electricity by 2030, driven in part by AI and broader digital demand. Whether or not your cooperative serves major data center load directly, this trend is shaping planning assumptions, transmission needs, generation discussions, and long-range reliability conversations across the industry.
Third, security and operational risk remain a high priority. The number of fraudulent techniques targeting AI-enabled systems is growing quickly, and the risks can affect safety, security, reputation and reliability. For cooperative leaders, that means any use of AI within operational technology or security systems deserves careful scrutiny.
Many organizational leaders find it daunting to know where to begin with AI. A smart approach is to start with goals and your cooperative's AI policy. That means defining the business problem, understanding the level of risk, identifying the data involved, and assigning human accountability for the output. Many organizations find it useful to begin with a small number of low-risk use cases where the benefits are easy to measure, such as drafting communications or summarizing documents. These are often the areas where utilities can see value early while limiting risk exposure.
Ultimately, success with AI depends on discipline and preparation. Co-ops that focus on practical use cases, maintain human oversight for important decisions, and treat AI with the same rigor as cybersecurity will be well-positioned to achieve their goals.
Before we wrap up, here are three things I'd encourage every cooperative leader to take away from today's conversation:
First, find use cases with limited risk exposure to start. Uses such as document summarization, drafting communications, and member support tools are great entry points.
Second, develop or review your cooperative's AI governance. A clear policy helps your team understand what's expected, what's allowed, and where the guardrails are.
And third, keep your board informed. AI adoption is a strategic decision, and your board needs to understand both the opportunities and the risks so they can support the direction you're heading.
That’s all for today. As always, thank you for joining us. Be on the lookout for more industry and technology content at nrucfc.coop/Solutions. We’ll talk with you soon!
