Power utilities are increasingly exploring artificial intelligence tools to improve planning, asset management, operations, forecasting, customer service, and decision support. However, the use of AI in a safety- and reliability-critical sector introduces new classes of operational, cyber, governance, and human-factor risks. These include poor data quality, biased or nontransparent outputs, hallucinated recommendations, model drift, privacy and confidentiality concerns, and misuse of AI in operational and decision-support environments. In high-consequence utility settings, these risks must be managed in a structured and risk-informed manner rather than through ad hoc deployment.
This paper presents a practical framework for managing risks associated with AI tools in power utilities. First, the paper identifies the main challenges and risk categories associated with AI adoption in utility environments, with particular attention to safety, reliability, cybersecurity, compliance, and organizational accountability. Second, it proposes an initial roadmap for utilities and other infrastructure organizations to begin controlling and mitigating these risks over time. The framework emphasizes use-case screening, risk classification, governance ownership, data and model controls, validation and human oversight, and continuous monitoring after deployment. It also distinguishes between lower-risk enterprise uses and higher-risk operational or decision-support applications, where stronger controls are required.
The objective of the paper is to provide utilities with an actionable starting point for responsible AI adoption. By combining practical governance steps with a risk-based perspective, the proposed framework supports a phased approach that enables innovation while protecting system reliability, public trust, and operational safety.