Amid the excitement surrounding artificial intelligence (AI), Cumberland Electric Membership Corporation (EMC) is focused on something more important than innovation for innovation's sake: member value. The cooperative is identifying opportunities where AI can help employees work more efficiently, make more-informed decisions and better serve members.
Headquartered in Clarksville, Tennessee, Cumberland EMC serves more than 110,000 members across five counties. As leaders evaluate AI, they are starting with specific business challenges rather than trying to deploy the technology across the organization all at once.
“We’ve never seen technology advance at this pace, so we’re monitoring it closely while making sure every use case delivers meaningful value,” said Brad Taylor, manager of financial services.
That approach reflects a challenge facing many cooperative finance leaders. AI can be a powerful tool, but finance teams must still protect data, validate results and ensure decisions are grounded in sound judgment. At Cumberland EMC, those responsibilities guide every AI evaluation.
Rather than asking, “what can this tool do?” the cooperative starts with a different question: “What problem are we trying to solve?” By focusing on clearly defined challenges, leaders can test, measure and refine AI applications in a controlled way.
That pragmatic approach aligns with broader trends across the finance sector. In KPMG's 2026 Global AI in Finance survey of more than 1,000 senior finance leaders, 70% reported improved decision-making quality and 64% reported better forecasting accuracy. The greatest benefits came not from replacing finance professionals but from helping them make better-informed decisions.
Cumberland EMC uses a variety of AI platforms, including Microsoft Copilot, NISC AI Assistant, ChatGPT, Claude and Gemini. Access varies by role, business need and intended use.
“What surprised me most was how quickly AI became integrated into people’s daily work,” Taylor said. “Asking employees what they use AI for is becoming a lot like asking what they use the internet for. The answer is: almost everything.”
What surprised me most was how quickly AI became integrated into people’s daily work. Asking employees what they use AI for is becoming a lot like asking what they use the internet for. The answer is: almost everything.
For the finance team, some of the most valuable applications are also among the most practical. Cumberland EMC has explored using AI to review general ledger coding, support forecasting activities with nonsensitive data and assist with financial analysis. The goal is not to remove finance professionals from the process. It is to help them identify issues more quickly, test assumptions and spend more time interpreting results.
“The real advantage comes when AI works alongside a subject matter expert,” Taylor said. “Technology can help identify issues, but it’s still the finance professional who brings the context, judgment and expertise needed to make the right decision.”
The cooperative is also exploring applications beyond traditional accounting functions. AI-supported tools are being evaluated for vegetation management planning—helping teams assess operational priorities, budgets and resource allocation. Cumberland EMC has also begun using AI in broadband pricing analyses.
Before AI, a pricing analysis often required building a large spreadsheet, adjusting a single assumption, recalculating results and repeating the process. AI-supported modeling allows the team to evaluate multiple scenarios quickly and focus on interpreting the results rather than generating them.
As adoption grows, so do questions about risk. At Cumberland EMC, the focus remains on security, reliability and governance. The cooperative has established policies governing approved AI tools, data protection and licensing considerations while reinforcing the need for human oversight. Even advanced AI systems can generate inaccurate information, making validation essential.
Cost is another area finance leaders are monitoring. As AI providers increasingly adopt usage-based pricing models, cooperatives will need to understand consumption patterns and evaluate whether the value delivered justifies the expense.
The takeaway for cooperatives is straightforward: start with a real business problem, use trusted data, keep experienced employees involved and verify the results. Used that way, AI becomes a practical tool for helping cooperatives serve members more effectively.