Nuclear’s Comeback Needs A Two-Way Power Exchange With AI

Everyone’s talking about how nuclear power can fuel AI. But what if we flipped the script? AI shouldn’t just be powered by nuclear; it should help power nuclear operations itself. In the US, that shift is already underway. Utilities and software vendors are aligning with the Nuclear Regulatory Commission’s AI Strategic Plan, which sets out how to evaluate and deploy AI technologies. This framework is shaping the adoption of AI-enabled maintenance and operations tools that help nuclear plants run more efficiently.

The US Department of Energy (DOE) has backed this vision, with nearly $900 million for advanced nuclear technologies through programmes such as Light Water Reactor Sustainability, which funds explainable AI for predictive maintenance and risk-informed strategies. DOE pilots with utilities and vendors have enabled AI-driven diagnostics and digital modernization in fission plants. These initiatives reflect a growing consensus – digitization isn’t optional; it’s essential to tackle the industry’s mounting pressures, notably:

• Aging fleets: Two-thirds of reactors are over 30 years old, driving up maintenance and lifetime extension costs.
• Economic pressures: Nuclear’s levelized cost of electricity is three times higher than that of solar, while renewables attract 21 times more investment, widening the funding gap for modernization.
• Integration hurdles: Inflexible baseload models clash with modern grids built for flexibility.
• Skills shortages: Workforce gaps and rising downtime costs demand automation and predictive tools.
• Regulatory complexity: Lengthy licensing and safety reviews slow innovation.

Despite these pressures, many nuclear operators still rely on manual processes and siloed systems. But modernizing doesn’t require ripping out existing infrastructure. It starts with enhancing what’s already in place. Industrial AI is accelerating digitization across the nuclear industry by leveraging decades of asset histories, inspection records and operational trends. Verdantix sees AI making an impact in:

• Maintenance optimization.

  CMMS and EAM platforms are increasingly used to centralize maintenance activities. When integrated with AI, these systems detect subtle performance patterns, predict failures before they occur and automate work order generation and planning. This incremental approach builds trust with regulators and staff, while delivering tangible benefits. For example, Azima DLI teamed with Luminant to pilot its Watchman program at Comanche Peak Nuclear Power Plant, using wireless monitoring and predictive analytics to enhance reliability and reduce unplanned maintenance, without replacing existing systems.

• Process optimization.

  Westinghouse, in partnership with Google Cloud, is using AI to optimize nuclear plant construction and operations. Westinghouse’s HiVE and bertha platforms leverage decades of proprietary data to automate modular reactor work packages and improve operational workflows. This helps accelerate deployment and enhance efficiency without replacing existing systems.

• Sensor monitoring and diagnostics.

  Constellation, in partnership with Blue Wave AI Labs, has applied AI tools to CMMS and EAM data to detect sensor drift and optimize fuel usage. At its Limerick Generating Station, AI flagged mis-calibrated sensors before they triggered costly power reductions, avoiding millions in lost generation revenue. The pilot is now scaling across Constellation’s fleet, with projected savings of $80 million over three years.

• Compliance and document management.
  At Diablo Canyon, Atomic Canyon introduced generative AI for compliance documentation, cutting retrieval times from hours to seconds. By accelerating access to critical compliance records, AI reduces the risk of human error during safety audits and emergency procedures. Faster retrieval ensures that operators can verify regulatory requirements in real time, supporting safer plant operations and minimizing the chance of non-compliance incidents.

These examples show that AI in nuclear isn’t hypothetical – it’s operational. Nuclear firms aiming to leverage industrial AI should pursue a gradual, incremental strategy, building on current maintenance and operations systems to boost reliability, compliance and efficiency, without disruptive overhauls.

For a deeper dive into how AI applications are reshaping the nuclear industry, visit our research portal.