The demand for energy is anticipated to rise significantly due to the increased deployment of artificial intelligence (AI) technologies, which are highly energy intensive. As we discussed in a recent thought leadership piece, there is a strong intersection between nuclear power and AI. Not only is nuclear power generation well-positioned to address the growing need for powering AI, but the nuclear power industry and its primary regulator, the US Nuclear Regulatory Commission (NRC) are looking to leverage AI to increase efficiency and strategic decision-making.

The NRC staff recently published a report identifying how AI may be used to enhance work within the NRC. The report is the result of NRC Chairman Christopher Hanson’s October 2023 memorandum directing “NRC staff to review how AI could streamline operations, optimize processes, and make well-informed decisions.”

The report follows the NRC’s issuance of NUREG-2261 in 2023, also known as the AI Strategic Plan (Strategic Plan), wherein the NRC acknowledged that while it does not currently employ AI technologies, it “anticipates increased use of AI in NRC-regulated activities.” The NRC explained the purpose of the Strategic Plan is to ensure the NRC staff’s readiness to review and evaluate the use of AI in NRC-regulated activities. Now, through the report, the NRC outlines recommendations for how it can leverage AI technology within the agency. Through the report, the NRC continues toward accepting AI technology’s role in the nuclear industry.

The Process

To conduct the review detailed in the report, the executive director for operations established a team from the Office of the Chief Information Officer and the Office of Nuclear Regulatory Research (AI Team). The AI Team encouraged staff to evaluate and suggest ways that the NRC might use AI to enhance their work, termed “AI use cases.” The AI Team then partnered with NRC data scientists and outside AI experts to evaluate whether the proposed AI use cases were viable.

The Results

The AI Team identified 36 potential AI use cases that would enhance staff productivity and support the workforce. Of the 36 cases, 16 “would apply AI to automate routine tasks and implement workflow and process improvements, making many of the staff’s daily tasks more efficient.” An example of these AI use cases is summarizing meeting transcripts, documents, and web pages. The remaining 20 AI use cases use other forms of “AI to perform predictive analytics, automating the review of public comments on proposed regulations, and aiding inspectors in scheduling and planning their availability across the regions.”

Next Steps

The staff identified two next steps for the NRC to successfully implement AI. First, they recommend that the NRC “[d]evelop an enterprise-wide AI strategy to advance the use of AI within the agency.” This enterprise AI strategy would ensure that “the NRC is aligned with the federal policy that federal agencies use AI in a safe and responsible manner” and would include preparing AI governance to ensure responsible and trustworthy AI implementation that precedes the full deployment of AI.

Further, it would mature the agency management program by implementing and supporting data architecture and data stewardship, while ensuring the entire NRC workforce has the necessary data literacy skills required for a comprehensive understanding of how data underpin and enhance the agency’s operations. It would also include strategically hiring and upskilling the existing workforce to ensure workplace adaptability. Further, it would include allocating resources to support the integration of AI tools as part of the IT infrastructure.

Second, the staff recommends that the NRC “invest in foundational tools to advance the use of AI.” Specifically, in fiscal year 2025, staff plans to invest in two foundational AI tools that will address several AI use cases and facilitate staff learning and understanding of various AI tools needed to develop other AI cases. Staff recommends that emphasis be placed on governance, data readiness, and training before implementing these foundational AI tools.