AI-enhanced digital technologies for nuclear supply chain and operational optimization
AI-enhanced digital technologies combine advanced analytics, machine learning and automated data processing tools to support optimization of nuclear construction and plant operations. These approaches use large engineering datasets, real-time sensor information and structured digital models to improve how construction activities are sequenced, how equipment health is monitored and how operational decisions are supported. Deployed at scale, these tools can turn nuclear project delivery and operations into more standardized, repeatable industrial processes.
In nuclear construction, AI can assist with generating work packages, identifying workflow dependencies and improving schedule planning. In operations, AI models support the detection of abnormal conditions, estimation of equipment degradation and maintenance planning. As the nuclear sector introduces more standardized reactor designs, digital platforms incorporating AI can help coordinate supply chain activities and improve consistency across projects. They can also enhance knowledge transfer between projects by capturing lessons learned in digital twins and design platforms. These tools can also support regulatory documentation by structuring data and maintaining traceable, auditable records.
Overall, AI-enabled digital systems provide utilities and developers with enhanced analytical capabilities but remain dependent on integration with established nuclear engineering practices, safety cases and regulatory oversight processes.[1] [2]
Main outcomes of the solution
- Business Value: Improves project execution discipline and delivery predictability by supporting more repeatable construction processes and data-driven operational decision-making. This strengthens confidence in nuclear project delivery and supports scalable deployment of standardized reactor designs. Greater predictability helps attract capital and reduce perceived risk.
- Affordability: Reduces project development and operational costs by automating elements of construction planning, lowering manual engineering effort and supporting maintenance actions that help prevent equipment failures. These efficiencies can limit rework, reduce schedule risk and lower unplanned maintenance costs. Over time, these gains can drive down the cost of new nuclear capacity and life extension.
Key deployment and scaling factors
- Establish access to structured, high-quality engineering and construction datasets suitable for AI model training and validation.
- Integrate AI tools with digital plant design platforms, construction management systems and operational data environments.
- Implement robust cybersecurity, data integrity and regulatory compliance frameworks appropriate for nuclear facilities.
- Develop workforce capabilities spanning AI and data science, nuclear engineering and digital engineering, supported by change management practices.
Case Study
Westinghouse Electric Company and Google Cloud, (2024), USA
Westinghouse and Google Cloud announced a collaboration to apply AI-enabled digital tools to accelerate nuclear plant construction and enhance the operation of existing nuclear facilities. Westinghouse’s HiVE system, introduced in 2024 and built on more than 75 years of proprietary nuclear engineering data, incorporates a nuclear-specific large language model known as Bertha.
As part of the collaboration, Westinghouse paired its HiVE and Bertha platforms with Google Cloud technologies, including Vertex AI, Gemini and BigQuery. A first-of-a-kind proof of concept was demonstrated using Westinghouse’s WNEXUS digital plant design platform to autonomously generate and optimize modular construction work packages for the AP1000 reactor.
The collaboration is intended to support more repeatable construction processes for advanced reactor designs, including the AP1000, AP300 small modular reactor and eVinci microreactor concepts, while also extending AI-enabled analytics to plant operations such as predictive maintenance and equipment-health monitoring. The case illustrates how AI-enabled digital systems can strengthen execution discipline and operational insight in nuclear projects, while remaining subject to established nuclear safety, quality and regulatory requirements.[1][2]
Solution and insights developed with contributions from the World Nuclear Association.
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