Nuclear is past the point of asking whether to use AI. Through our nuclear AI landscape research, ScottMadden has tracked more than 60 AI deployments across the U.S. fleet, spanning enterprise and engineering search, corrective action, performance improvement, outage planning, and equipment reliability. ∗
That level of activity is significant. Based on our work across the broader power sector, nuclear appears to be moving faster than many other utility functions in putting AI into practical use. But roughly 60% of the deployments we track are in active use, with no quantified operating benefit associated with them. Engineers, planners, and managers report time savings. Few utilities can yet say what those savings are worth.
What separates deployments that scale from those that stall is integration: with trusted plant data, with existing systems and processes, and, critically, with the people responsible for making and executing decisions.
The applications range considerably in maturity. Some are enterprise tools broadly available to employees. Others are pilots being tested in real-world settings. A smaller group has moved into production workflows, where the operating benefit is clearest, and the integration demands are highest.
Activity is widespread, but measurable operating value is still emerging.
The more useful question for a nuclear executive is not how many AI applications the organization has but whether those applications are changing how work gets done.
Can a plant reduce routine manual corrective action program (CAP) screening? Can outage planners build and challenge a critical-path-loaded schedule faster? Can an engineer find and synthesize operating experience in minutes instead of hours? Can a manager receive practical coaching recommendations based on performance improvement information before going into the field?
These are the outcomes that begin to separate AI adoption from AI integration.
Three Types of AI and Why the Difference Matters
Not all AI applications require the same approach. Three broad types account for much of what we are seeing across nuclear, and the differences matter because the data, integration, validation, and governance requirements increase as AI moves closer to plant and engineering decisions.
1. Generative AI and LLM-Based Tools
Generative AI applications include document and procedure search, operating experience search, regulatory preparation, engineering assistants, and enterprise copilots.
The opportunity is relatively straightforward: reduce the time highly skilled nuclear employees spend searching, compiling, and drafting so they can spend more time applying judgment.
An engineer who previously searched multiple systems for operating experience or technical information can start with an AI-generated synthesis and focus on validating and applying the information. These applications can often be deployed relatively quickly, particularly for lower-risk uses where appropriate human review remains in place.
2. Task-Specific Machine Learning
Task-specific machine learning uses historical data to perform a defined activity repeatedly and consistently. Applications include CAP screening, equipment classification, condition monitoring, performance analysis, and outage prediction.
CAP screening illustrates where the opportunity can become more significant than simple automation. An AI-enabled application can review and classify incoming condition reports, identify potential significance, recommend routing, and elevate exceptions for human review. But the more important question for CAP is not whether AI can help the existing screening committee work faster. It is whether every condition report still needs to reach that committee at all.
Outage scheduling works on the same paradigm. Tools trained on historical schedules and actual performance against baseline can predict critical path and identify schedule risk. The ROI case is the clearest in nuclear, given the cost of a day of outage, but the tooling is among the least mature, largely because historical schedule data is captured inconsistently across the fleet.
In both cases, AI begins to change the role of the employee, from performing repetitive screening and analysis to reviewing exceptions, validating recommendations, and applying judgment where it matters most.
Most recently, models combine generative AI with machine learning in “hybrid” models. Performance improvement tools are one example. Machine learning trends historical observation data to show where performance is changing, while a language model extracts behaviors from free-text field entries to identify what is driving it.
3. Specialized AI and Operational Optimization
The most complex applications combine AI with physics-based models, computer vision, or specialized engineering logic. Examples include computer vision for equipment inspections and radiation mapping, predictive equipment reliability, and physics-constrained models for reactor core design and fuel cycle optimization.
These require the deepest integration with plant data and processes, the greatest involvement from engineering and operating subject matter experts, and the longest development timelines. Physics-based applications are measured in years rather than months, and the talent pool that combines machine learning with reactor physics is small.
They are also, notably, where the strongest evidence of sustained value sits. Core optimization has been deployed fleetwide for multiple operating cycles with publicly documented fuel savings. Computer vision applications in weld imaging and inspection have produced measurable time reductions.
As AI moves closer to engineering judgment and plant decisions, expectations for validation, cybersecurity, governance, and training should increase accordingly. A document-search assistant should not require the same controls as an application influencing equipment reliability or an engineering decision.
There Is More Than One Path to Adoption
Utilities are taking three broad approaches to building AI capability, and most are running more than one at the same time.
Enterprise Platform Rollout
A broad platform such as Copilot deployed across the workforce. Scales fast, but adoption plateaus without sustained leadership reinforcement.
Targeted Vendor Deployment
Nuclear-specific products for a defined function. Vendor carries domain expertise and compliance but creates dependency and slow procurement.
Citizen Development
Plant engineers build with low-code tools, refined centrally before release. Lowest cost and fastest to value, but quality depends on the refinement step.
Each path produces working applications. What differs is what it takes to sustain them.
What Separates Scaling from Stalling
The difference between AI initiatives that gain traction and those that stall is increasingly organizational rather than technical. The sharpest divide is whether AI is layered onto existing processes or whether the process itself gets reconsidered around what AI makes possible.
| Scaling Successfully | Stalling or Regressing |
|---|---|
| Executive sponsorship with visible, sustained reinforcement | AI delegated primarily to IT or innovation |
| Clear operating problems with accountable business owners | Long lists of use cases without clear priorities |
| Enterprise priorities combined with ideas from employees closest to the work | Enterprise rollout with little frontline input |
| Governance matched to application risk and consequence | The same governance applied to every AI application |
| Data, integration, and cybersecurity addressed early | Integration addressed after the pilot |
| Measures tied to operating outcomes | Success measured by licenses, usage, or pilot count |
| Processes reconsidered around what AI makes possible | AI layered onto existing processes without changing the work |
Five Moves for Nuclear Leaders
As AI activity expands, five actions can help leaders focus on investment and turn promising applications into sustainable operating capabilities.
1. Focus on the Solution and Desired Outcome—not the AI Tool
Most utilities now have more AI ideas than they can realistically pursue. Narrow the portfolio to problems with meaningful operating value, an accountable business owner, accessible data, and a realistic path to implementation.
CAP screening, outage planning, engineering search, equipment reliability, and performance improvement are examples.
2. Challenge the Process, Don’t Just Accelerate It
Before automating today’s process, ask what AI makes possible. What work can be eliminated? What can be simplified? Where does human judgment add the most value?
If AI can reliably perform initial CAP screening, for example, the opportunity may be to rethink routine manual screening and the front-end entry process and back-end decision tree rather than simply automate pieces of the existing committee process.
3. Address Integration Early
A promising AI application has limited value if it cannot securely access the information it needs or fit within the utility’s existing environment.
For priority applications, integration should be treated as part of the use case, not as a technical issue to address. To succeed, understand what data is required, where it resides, who owns it, and how the application will connect with work management, performance improvement, outage, engineering, document management, and plant systems. Cybersecurity and the IT/OT boundary should be part of that discussion from the beginning.
4. Match Governance to Consequence
Governance should reflect what an application does and what happens if it is wrong.
Establish appropriate risk tiers, human review, cybersecurity and validation requirements, and decision rights. At the same time, define what will be needed for a successful application to become sustainable: business ownership, technology ownership, integration, support, funding, training, and change management. The objective is enough rigor to manage the risk without creating a process that prevents useful applications from reaching production.
ScottMadden’s Adaptive Innovation Management System (AIMS) provides one approach for doing this, adding governance and implementation discipline as an application matures rather than treating every early idea like finished production software.
5. Measure What Matters and Scale What Works
Licenses, users, and prompt counts can tell you whether people are trying a tool. They do not tell you whether it is improving the business.
Tie priority applications to operating measures—for engineering search, time spent finding and reviewing information; for outage planning, planning cycle time, schedule quality, earlier identification of constraints, or ultimately outage performance.
From AI Adoption to Operating Capability
Nuclear has demonstrated it can adopt AI. The next divide will be between organizations that accumulate AI tools and those that change how the work is performed and can show what that is worth.
The utilities that will make that transition are the ones that can combine nuclear operating knowledge with trusted data, integration, fit-for-purpose governance, and clear ownership of the work itself.
How Can ScottMadden Help
ScottMadden helps nuclear operators make that transition. Our role sits at the intersection of nuclear operations and technology integration, which allows us to help answer questions a technology vendor or general AI advisor may not:
- What applications are worth pursuing?
- How should the underlying nuclear process change?
- What plant and enterprise data are required?
- How should the solution fit within the IT and OT environment?
- What level of governance is appropriate? And what needs to be true for the application to deliver sustainable operating value?
That is the objective: a repeatable path from a meaningful nuclear operating problem to an integrated solution with measurable value.
Take a Deeper Look at the Nuclear AI Landscape
Complete the form below to request a deeper discussion of ScottMadden’s Nuclear AI Landscape Report, compare your AI activities against leading practices across the fleet, and identify opportunities to move the highest-value applications from pilot to sustained deployment and integration.
*About the Research
ScottMadden’s Nuclear AI Landscape research examines more than 60 AI deployments across more than 10 U.S. nuclear operators, spanning applications in knowledge management, corrective action, work and outage planning, equipment reliability, performance improvement, regulatory support, and operational optimization. The research draws on utility interviews, vendor and research partner input, industry research, and a utility survey conducted with Idaho National Laboratory’s Light Water Reactor Sustainability (LWRS) Program, completed in February 2026. Deployments include applications at various stages of maturity, from pilots and field applications to production tools and broader enterprise capabilities.
