Artificial intelligence is no longer a theoretical discussion in the nuclear industry. Utilities, suppliers, and regulators are actively exploring where AI can reduce friction, improve consistency, and help an increasingly stretched workforce focus on higher-value work. Yet for all the momentum, one truth remains constant: adopting AI in nuclear is fundamentally different from adopting AI anywhere else.

That reality is what led Nuclearn to formally establish its Certified Service Provider program, and to name Raisun Technology Services as its first Certified Service Provider.

At first glance, the announcement may read like a standard partner designation. In practice, it represents something more consequential: a recognition that technology alone is not enough to deliver safe, durable value in a regulated, safety-critical industry.

AI in Nuclear Is an Execution Challenge, Not a Conceptual One

Across the energy sector, organizations have experimented with AI pilots, proofs of concept, and limited deployments. In many cases, those efforts stall. Models look promising in isolation but struggle when introduced into real workflows shaped by procedures, regulatory expectations, and decades of institutional knowledge.

Nuclear magnifies those challenges. Every deployment must align with plant-specific processes, licensing bases, cybersecurity requirements, and human-in-the-loop decision making. There is little tolerance for ambiguity, and even less tolerance for tools that behave unpredictably.

Nuclearn’s leadership has been clear about this distinction. AI adoption in nuclear is not about chasing novelty or deploying general-purpose tools. It is about disciplined execution, traceability, and confidence in how work actually gets done.

That philosophy is what underpins the Certified Service Provider program.

Why a Certified Service Provider Model Matters

The Certified Service Provider designation is designed to ensure that Nuclearn customers are supported by partners who understand both sides of the equation: advanced AI capabilities and nuclear operations realities.

Rather than leaving utilities to bridge that gap on their own, the program formalizes a delivery ecosystem built around:

• Proven nuclear domain expertise

• Experience operating in regulated environments

• Practical, field-first implementation approaches

• A clear understanding of workforce impacts and change management

By certifying service providers, Nuclearn is acknowledging a simple truth: successful AI adoption depends as much on how systems are implemented, governed, and supported as on the software itself.

Why RTS Was Selected

RTS was selected as Nuclearn’s first Certified Service Provider based on a track record that aligns closely with these principles.

RTS brings a delivery-led approach rooted in hands-on nuclear experience. Its teams have supported safety- and compliance-critical workflows, worked alongside plant personnel, and helped organizations navigate the transition from exploratory AI efforts to sustained operational value.

As a Certified Service Provider, RTS will support customers across the full AI adoption lifecycle, including:

• AI readiness assessments grounded in real operational constraints

• Implementation planning aligned to existing procedures and systems

• Workforce enablement that prioritizes trust, usability, and accountability

• Advised managed services designed to sustain value over time

More information about RTS and its nuclear-focused advisory and delivery work can be found at www.raisuns.com.

The goal of the partnership is not simply to deploy AI faster, but to deploy it responsibly and in a way that stands up to internal scrutiny and external oversight.

As Phil Zeringue, Chief Revenue Officer at Nuclearn, noted in the announcement, “AI adoption in nuclear power requires disciplined execution and a clear understanding of how work is performed.” That statement captures the essence of why this partnership exists.

Moving Beyond Pilots to Sustained Value

One of the most persistent challenges in enterprise AI is the gap between pilot success and enterprise impact. Nuclear organizations are no exception. Many have validated that AI can assist with document analysis, corrective action workflows, planning activities, and more. Fewer have successfully scaled those capabilities in a way that becomes part of normal operations.

The Nuclearn–RTS partnership is explicitly designed to close that gap.

By pairing nuclear-specific AI platforms with delivery teams fluent in both regulatory expectations and day-to-day plant realities, the Certified Service Provider model helps organizations move from experimentation to execution. It provides a structured path from initial assessment through long-term adoption, reducing the risk that AI initiatives stall or remain siloed.

A Workforce-Centered Approach to AI

Another critical dimension of the partnership is its emphasis on workforce-centered adoption. In nuclear, AI is not about replacing judgment or automating decisions without oversight. It is about augmenting experienced professionals, improving consistency, and reducing the burden of repetitive, time-intensive tasks.

RTS’s role includes helping organizations introduce AI in ways that build trust with end users, maintain human accountability, and align with existing governance models. That focus is essential in an industry where credibility and transparency are foundational.

Building a Trusted Ecosystem for Nuclear AI

The designation of RTS as the first Certified Service Provider is also the first visible step in building a broader ecosystem around nuclear-specific AI adoption. Nuclearn has been deliberate about avoiding a one-size-fits-all model. Instead, it is creating a network of partners who can meet customers where they are and support diverse operational contexts.

Over time, this ecosystem approach is expected to help establish more consistent best practices for AI deployment in nuclear, informed by real-world experience rather than theory alone.

What This Means for the Industry

For utilities and suppliers evaluating AI initiatives, the announcement sends a clear signal: successful nuclear AI adoption requires more than software procurement. It requires trusted partners, disciplined delivery, and a deep respect for how nuclear work is performed.

For the industry as a whole, it reflects a maturation of the AI conversation. The focus is shifting away from what is possible and toward what is sustainable.

By formalizing its Certified Service Provider program and selecting RTS as its first partner, Nuclearn is reinforcing its commitment to safe, practical, and workforce-aligned AI adoption. It is also acknowledging that the future of nuclear AI will be shaped not just by platforms, but by the people and processes that bring those platforms to life.

The nuclear industry is at an inflection point. Utilities are managing extended plant lifetimes, preparing for new reactor technologies, and navigating workforce constraints, all while maintaining the highest standards of safety, quality, and regulatory compliance. In this environment, artificial intelligence is no longer a future concept. It is increasingly viewed as a necessary capability for sustaining performance and reliability.

Against this backdrop, NPX Innovation and Nuclearn have announced a strategic collaboration to accelerate the responsible adoption of AI across the nuclear sector.

This collaboration reflects a shared belief that AI in nuclear must be practical, transparent, and grounded in the realities of how nuclear organizations operate. Rather than focusing on experimentation alone, NPX and Nuclearn are aligning their expertise to deliver AI solutions that integrate into existing workflows and deliver measurable outcomes.

For the full details and official announcement, read the release directly from NPX Innovation here:
👉 https://www.npxinnovation.ca/post/npx-and-nuclearn-announce-strategic-collaboration-to-accelerate-ai-in-the-nuclear-sector

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Why AI in nuclear requires a different approach

AI adoption in nuclear is fundamentally different from other industries. Nuclear organizations operate in highly regulated environments where decisions must be explainable, auditable, and conservative by design. Any technology introduced into these environments must support, not undermine, existing safety and quality frameworks.

Over the past several years, many nuclear organizations have explored AI through pilots or limited use cases. While these efforts have demonstrated potential, scaling AI beyond isolated applications has proven difficult. Integration challenges, data quality concerns, and organizational trust have slowed progress.

The NPX–Nuclearn collaboration is designed to address these challenges directly. Rather than treating AI as a standalone capability, the partnership focuses on embedding AI into the systems, processes, and decision-making frameworks nuclear teams already rely on.

Complementary strengths, aligned around outcomes

NPX Innovation brings deep experience in nuclear supply chain optimization, digital engineering, and operational modernization. Their work spans complex, regulated environments where reliability, traceability, and long-term sustainability are essential. NPX understands where operational friction exists today, particularly in areas such as parts management, procurement, and engineering data flows.

Nuclearn brings a nuclear-specific AI platform purpose-built for regulated environments. Designed by nuclear engineers for nuclear professionals, the platform focuses on automating and augmenting knowledge-intensive tasks across engineering, maintenance, compliance, finance, and regulatory functions. Its emphasis on transparency, human oversight, and workflow alignment makes it well suited for nuclear applications.

Together, the two organizations are combining domain expertise and technology to move AI adoption from isolated tools to integrated capability.

Moving from pilots to scalable deployment

One of the most important aspects of this collaboration is its focus on scalability. In many industries, AI initiatives stall after initial success because they cannot be reliably expanded across teams, sites, or functions. In nuclear, the stakes of scaling incorrectly are especially high.

The NPX–Nuclearn collaboration is structured to help organizations move beyond proof-of-concept projects toward sustained, enterprise-wide impact. This includes:

• Integrating AI into existing operational systems rather than replacing them

• Supporting consistent, repeatable outcomes across sites and teams

• Maintaining clear governance, documentation, and auditability

• Enabling gradual adoption that aligns with organizational readiness

By focusing on how AI is deployed and governed, not just what it can do, the partnership addresses one of the most common barriers to adoption in the nuclear sector.

Trust, transparency, and human oversight

Trust remains the defining factor for AI adoption in nuclear. Engineers, operators, and leaders must be able to understand how AI outputs are generated and how they fit into established decision-making processes. Regulators expect traceability and clear documentation to support any technology used in safety-related or business-critical workflows.

This collaboration places those expectations at the center. The combined approach emphasizes AI systems that provide context, cite underlying data sources, and support human-in-the-loop decision making. Rather than replacing expert judgment, AI is positioned as a means of reducing manual burden, improving consistency, and surfacing insights more efficiently.

This philosophy aligns closely with how nuclear organizations already operate: conservative by design, data-driven, and focused on continuous improvement.

Practical value across the nuclear ecosystem

The partnership between NPX and Nuclearn is intended to support a broad range of nuclear stakeholders, from utilities and suppliers to engineering and service organizations. By addressing common challenges across the nuclear ecosystem, the collaboration aims to deliver value in areas such as:

• Improving efficiency in engineering and documentation workflows

• Enhancing supply chain visibility and parts management

• Reducing manual effort in compliance and reporting activities

• Supporting workforce effectiveness amid demographic and skills shifts

Importantly, these improvements are not framed as transformational disruption. Instead, they reflect incremental, practical enhancements that compound over time and strengthen organizational resilience.

A signal of where the industry is headed

This announcement also reflects a broader shift in how the nuclear industry is approaching innovation. Rather than pursuing technology in isolation, organizations are increasingly recognizing the importance of partnerships that combine technical capability with deep domain understanding.

AI in nuclear is no longer a question of whether it will be adopted, but how it will be adopted responsibly. Collaborations like this one signal a maturing approach, one that prioritizes alignment with industry values over speed for speed’s sake.

Looking ahead

The NPX–Nuclearn collaboration represents the beginning of a longer journey. As AI capabilities evolve and regulatory expectations continue to develop, the partnership will focus on learning from real-world deployments and adapting to the needs of nuclear organizations.

By working closely with industry stakeholders, NPX and Nuclearn aim to refine how AI is applied, governed, and scaled across the sector. The objective is not to chase the latest trend, but to build durable capabilities that support nuclear performance for decades to come.

For nuclear leaders evaluating how and when to adopt AI, this collaboration offers a clear signal. The future of AI in nuclear will be shaped by solutions that respect the industry’s complexity, uphold its standards, and deliver tangible value where it matters most.

To read the official announcement and learn more about the collaboration, visit NPX Innovation’s full release here:
👉 https://www.npxinnovation.ca/post/npx-and-nuclearn-announce-strategic-collaboration-to-accelerate-ai-in-the-nuclear-sector

The Nuclear Business Innovation Council (NBIC) 2026 arrived at a pivotal moment for the nuclear industry. Artificial intelligence is no longer a speculative topic or a future-state discussion. It is actively being evaluated, implemented, governed, and scaled across nuclear organizations today.

What made NBIC 2026 different was not simply the quality of the sessions or the caliber of attendees, but the maturity of the conversations. The dialogue has clearly moved beyond curiosity. Leaders are now grappling with practical questions: how AI fits into existing workflows, how it should be governed, and how to ensure it strengthens—not undermines—the principles of safety, traceability, and accountability that define nuclear work.

Across panels, informal discussions, and conversations that unfolded on the show floor, several themes emerged that are worth capturing. Together, they offer a clear picture of where nuclear AI stands today and where it is headed next.

Lesson One: AI Is Becoming Foundational, Not Experimental

One of the strongest signals from NBIC 2026 was the shift in mindset around AI’s role in nuclear organizations. The conversation is no longer about pilots or proofs of concept. Instead, leaders are treating AI as foundational infrastructure.

Engineering teams spoke candidly about the need for AI systems that understand nuclear-specific documentation, licensing bases, and design commitments. Business and finance leaders emphasized defensibility—how AI-supported decisions can be audited, explained, and trusted over time. Compliance and regulatory professionals reinforced that traceability and transparency are non-negotiable.

This shift matters. In nuclear, foundational systems are held to a higher standard than experimental tools. They must be reliable, repeatable, and aligned with existing governance structures. NBIC 2026 made it clear that AI is now being evaluated through that same lens.

Lesson Two: Nuclear Problems Are Cross-Functional by Nature

Another recurring theme was the recognition that many of the industry’s most persistent challenges do not belong to a single department. Parts issues, for example, are rarely just supply chain problems. They intersect with engineering judgment, quality requirements, procurement processes, and regulatory obligations. Similarly, corrective action programs touch engineering, operations, compliance, and business performance simultaneously.

Participants shared lessons learned from disconnected point solutions—tools that worked well for one function but created friction elsewhere. Those experiences reinforced an important takeaway: AI that operates in isolation can introduce as much risk as value.

The most compelling discussions at NBIC focused on connected systems that respect how nuclear work actually happens. AI that supports engineering must also account for downstream business and compliance implications. AI that helps finance teams must remain grounded in technical reality. The industry is increasingly aligned on the need for shared context across functions.

Lesson Three: Governance Is Now Central to the Conversation

Governance emerged as a central topic throughout the event. As AI adoption expands, organizations are recognizing that success depends as much on oversight and structure as on technical capability.

Attendees discussed the importance of defining clear roles and responsibilities, maintaining human accountability, and ensuring that AI outputs can be explained and defended. There was broad agreement that AI should augment decision-making, not replace it, and that strong guardrails are essential.

This focus on governance signals a healthy evolution. Rather than slowing adoption, it is enabling more confident deployment by aligning AI initiatives with nuclear values and expectations.

Partnerships Are Accelerating Progress

Perhaps the most encouraging takeaway from NBIC 2026 was the growing emphasis on partnership. Across the industry, leaders acknowledged that the challenges facing nuclear—workforce transitions, supply chain complexity, regulatory demands—are too interconnected for any single organization to solve alone.

That mindset was reflected not only in conversation, but in two notable announcements that surfaced directly from discussions on the show floor.

Park Nuclear and Nuclearn Combine Forces to Build Parts AI

One of the most widely discussed developments at NBIC 2026 was the announcement that Park Nuclear and Nuclearnare combining forces to build Parts AI.

This collaboration brings together complementary strengths. Park Nuclear contributes decades of experience in nuclear supply chain, parts qualification, commercial-grade dedication, and procurement support. Nuclearn brings a nuclear-specific AI platform designed to operate within the industry’s regulatory, safety, and data constraints.

The objective of Parts AI is not to introduce a new workflow, but to reduce friction within existing ones. By providing better context, faster insight, and clearer documentation, Parts AI is intended to support decisions related to qualification reviews, equivalency evaluations, obsolescence management, and inventory strategy.

What makes this partnership particularly significant is its grounding in real-world use cases. It reflects the understanding that parts decisions are rarely isolated—they carry engineering, business, and compliance implications simultaneously. By addressing those dimensions together, the collaboration aims to deliver practical value without compromising rigor.

Nuclearn Names Raisun Technology Services as Its First Service Provider

Another important announcement heard on the show floor was Nuclearn naming Raisun Technology Services (RTS) as its first certified service provider.

This designation highlights a growing recognition across the industry: deploying AI successfully in nuclear environments requires more than technology alone. Organizations need support in readiness assessment, workflow alignment, change management, and sustained adoption.

RTS operates with a technology-agnostic, advisory-first approach, working across utilities, suppliers, and advanced reactor developers. As Nuclearn’s first service provider, RTS will help organizations implement Nuclearn’s platform in ways that align with their specific objectives, constraints, and cultures.

The announcement underscored a broader NBIC theme: trusted service partnerships are becoming essential to scaling AI responsibly and effectively.

What NBIC 2026 Signals for the Industry

Taken together, the lessons and announcements from NBIC 2026 point to a maturing nuclear AI landscape. Several signals stand out:

• AI is moving from experimentation to infrastructure

• Cross-functional context is essential for meaningful impact

• Governance and accountability are prerequisites for scale

• Partnerships are accelerating progress and reducing risk

NBIC continues to serve as an important forum where these ideas can be debated openly and refined collaboratively. By bringing together utilities, suppliers, service providers, and technologists, it creates space for alignment across the industry.

As nuclear organizations move from asking what AI can do to defining what it should do, the direction is becoming clearer. The future of nuclear AI will be built collaboratively, grounded in real workflows, and shaped by those who understand both the opportunity and the responsibility.

NBIC 2026 made that unmistakably clear.

In nuclear, very few problems belong to a single team.

An engineering question often becomes a business decision.
A parts issue turns into a compliance review.
A financial classification can require regulatory justification.

Yet many organizations still rely on systems and processes that operate in silos, forcing work to move from team to team through handoffs, emails, spreadsheets, and rework. Over time, those gaps introduce delay, inconsistency, and risk.

The Nuclearn ecosystem was built to address that reality.

Rather than focusing on one function or one task, the ecosystem is designed to support how nuclear work actually happens, across engineering, business, finance, compliance, and regulatory roles,  with shared context, traceability, and accountability.

Nuclear Work Is Inherently Cross-Functional

Consider a common scenario. An engineer identifies an issue that requires evaluation. That evaluation may trigger corrective actions, parts sourcing, schedule changes, or cost implications. Each step touches multiple teams, each with their own responsibilities, tools, and decision criteria.

What often slows progress is not lack of expertise, but lack of alignment. Information is reinterpreted as it moves. Assumptions are repeated. Documentation is recreated in different formats for different audiences. Teams spend time validating work that has already been done, simply because it lives somewhere else.

The Nuclearn ecosystem is designed to reduce those gaps by allowing teams to work from the same underlying information, even while maintaining clear role boundaries and approvals.

What the Nuclearn Ecosystem Is and Is Not

The ecosystem is not a replacement for existing plant systems. It does not require organizations to rip and replace CAP systems, ERP platforms, document repositories, or scheduling tools.

Instead, it works alongside them.

At its core, the ecosystem connects nuclear-specific AI capabilities with existing data, documents, and workflows so that different teams can interact with the same information in ways that make sense for their roles.

Engineering teams can focus on technical accuracy and requirements.
Business and finance teams can focus on classification, cost, and impact.
Compliance and regulatory teams can focus on traceability, defensibility, and documentation.

Each group remains accountable for its decisions, but they are no longer starting from scratch or working in isolation.

Reducing Handoffs Without Reducing Accountability

One of the most consistent themes across nuclear organizations is the cost of handoffs. Every time work moves from one team to another, context can be lost. Questions must be re-answered. Decisions must be re-justified.

The Nuclearn ecosystem reduces unnecessary handoffs by preserving context as work moves across functions. When an engineer documents an issue, that context can inform downstream reviews without requiring reinterpretation. When finance evaluates a classification, the supporting technical basis is already linked and accessible. When compliance reviews documentation, the decision trail is intact.

This does not eliminate human review. In fact, it strengthens it by ensuring reviewers are working with complete, consistent information rather than fragments.

Supporting Different Roles With the Same Source of Truth

A key principle of the Nuclearn ecosystem is that different roles should not need different versions of the truth. They should need different views of the same truth.

An engineer may need to search technical documents, requirements, or prior evaluations.
A finance professional may need to understand cost drivers and classifications.
A compliance professional may need to verify that decisions align with procedures, guidance, or regulatory commitments.

The ecosystem enables each of these perspectives without duplicating work or data. That shared foundation is what allows teams to move faster without sacrificing rigor.

Built for Nuclear Standards and Expectations

Another lesson that has emerged across the industry is that generic AI tools struggle in nuclear environments. Nuclear work demands accuracy, conservative bias, version control, and the ability to explain how an answer was derived.

The Nuclearn ecosystem is purpose-built for those expectations. It is designed to operate within nuclear quality standards, support auditability, and maintain human accountability at every step. When the system does not have sufficient confidence, it is designed to defer to human review rather than force an answer.

That approach reflects how nuclear professionals already work, cautiously, deliberately, and with a clear understanding of consequences.

Why Ecosystems Matter More Than Point Solutions

Many organizations have experimented with point solutions that solve a single problem well. While those tools can be useful, they often introduce new friction when they do not integrate with broader workflows.

The ecosystem approach recognizes that nuclear efficiency comes from coordination, not optimization in isolation. Improvements in engineering only matter if they carry through to business decisions. Gains in automation only matter if they reduce downstream rework.

By connecting capabilities across functions, the Nuclearn ecosystem helps ensure that progress in one area does not create new burdens elsewhere.

Enabling Better Decisions, Not Faster Guessing

A common concern around AI is speed without understanding. The ecosystem addresses that concern by emphasizing decision support rather than decision replacement.

AI is used to surface relevant information, draft documentation, identify patterns, and reduce manual effort,  but final decisions remain with qualified professionals. The goal is not to shortcut judgment, but to give teams better inputs so judgment can be applied more effectively.

In practice, this means fewer hours spent searching, reformatting, and re-explaining, and more time spent evaluating, validating, and improving outcomes.

What This Means for Nuclear Organizations

For nuclear organizations, the value of the ecosystem shows up in practical ways:

• Less rework between engineering, business, and compliance teams

• More consistent documentation and decision trails

• Faster reviews without sacrificing quality

• Better alignment across departments

• Increased confidence in audits and assessments

These improvements are incremental, not disruptive. They respect existing processes while making them easier to execute well.

Looking Ahead

As the nuclear industry continues to modernize, the ability to work across functions with shared context will become increasingly important. Workforce transitions, supply chain complexity, and regulatory expectations all point toward the need for better coordination, not more tools.

The Nuclearn ecosystem is one approach to meeting that need, by supporting how nuclear teams already work, while removing unnecessary friction that slows them down.

Trust in AI is not built through promises.

It is built through exposure.

When nuclear teams can see how AI is working, where data is coming from, and why outputs look the way they do, adoption accelerates.

Questions become easier to answer.
Concerns surface earlier.
Governance becomes practical instead of theoretical.

By opening the black box, the Nuclearn Platform shortens the path from skepticism to confidence.

Platform, Not Point Solution

This level of transparency only works because the Nuclearn Platform is unified.

Rather than stitching together disconnected tools, the platform maintains shared context across data, workflows, and outputs.

That means:

• Changes in configuration propagate consistently
• Guardrails apply everywhere
• Traceability is preserved end to end
• Oversight does not fragment as adoption grows

For nuclear organizations that adopt AI incrementally, this matters more than any single capability.

Partnership Is What Makes Transparency Usable

Transparency alone is not enough.

Without guidance, visibility can become overwhelming.

This is where the working relationship matters.

Nuclearn works alongside customers to:

• Identify which platform components should be exposed to which teams
• Determine appropriate levels of configurability
• Align platform behavior with operational expectations
• Validate outcomes as usage expands

The platform does not just show what is possible.

The partnership ensures it is applied responsibly.

A Higher Standard for Nuclear AI

The Nuclearn Platform sets a different standard.

AI that is visible, not hidden.
Configurable, not rigid.
Auditable, not mysterious.
Supported by people who understand nuclear work.

That combination is rare.

And in an industry where trust is built slowly and deliberately, it is also essential.

Final Thought

The future of AI in nuclear energy will not be defined by who has the most advanced models.

It will be defined by who gives organizations the most control, clarity, and confidence.

The real value of the Nuclearn Platform is not just what it delivers out of the box.

It is the fact that nothing important is hidden inside it.

And that is what makes it usable, governable, and trusted in nuclear environments.

 

Anyone who has worked inside a nuclear plant knows one universal truth: there is no room for “best guess.”

We operate in an environment where accuracy is not just a standard. It is a regulatory, safety, and operational expectation. That is why the rise of generic AI tools has created both excitement and justified caution across the industry.

AI can accelerate engineering work, support better decision-making, and reduce repetitive administrative burden. But only if it behaves in a way that aligns with nuclear norms: precision, transparency, and traceability.

Most tools are not built for that.
Nuclearn is.

After years of working through FSAR updates, 10 CFR 50.59 screenings, CAP investigations, engineering changes, work packages, and audits, one thing becomes clear: choosing the wrong tool is not a minor efficiency issue. It introduces uncertainty into processes that depend on alignment and clarity.

Here is why nuclear teams often prefer Nuclearn (Atom Assist) over Microsoft Copilot and other general-purpose AI systems.

 

1. Nuclear-Grade Accuracy, Not Guesswork

Copilot is optimized for general office tasks. When it is unsure, it often attempts a “best guess,” which can introduce errors or hallucinations.

That behavior does not translate well into regulated environments.

Nuclearn’s models are tuned to nuclear use cases and are more likely to pause when information is uncertain or incomplete. In many cases, Atom Assist will respond with variations of “I do not know based on the available data,” which aligns better with nuclear expectations around conservative decision-making.

This reduces the risk of false confidence and supports more deliberate engineering and licensing work.

 

2. Answers You Can Verify When Needed

Verification is not optional in nuclear work.

Nuclearn can provide citations directly to source documents such as procedures, FSAR sections, work management artifacts, and licensing basis documents. When personas are configured with the appropriate datasets, answers can be traced back to the exact supporting material.

This level of transparency gives engineers, licensing specialists, and Ops staff a clear way to review and confirm the information before taking action.

Copilot does not support structured, document-level traceability in the same way.

 

3. Personas and Workflows That Reflect Real Nuclear Roles

Nuclear work is structured around defined processes and responsibilities.

Nuclearn includes personas that are modeled after real plant roles and job functions. These can be configured once and shared across teams, which helps reduce repetitive context-setting and leads to more consistent outputs.

Copilot agents generally need to be built manually and require heavy customization to mimic nuclear expectations. Even then, they may not align with nuclear vocabulary, QA expectations, or the nuances of configuration-controlled information.

Nuclearn’s approach mirrors how nuclear teams already work.

 

4. Connected to Nuclear-Relevant Data Sources

Plant information is distributed across a wide variety of systems, not just SharePoint or shared drives.
Nuclearn can connect to:

• FSARs
• CAP data
• Maximo
• Engineering program documents
• Internal systems
• OE databases
• Licensing basis information

By integrating with these sources, Atom Assist can reference the datasets nuclear staff rely on every day.

Generic AI tools are limited to more basic document repositories, which means critical plant context can be missed or misinterpreted.

 

5. Auditability Designed for Environments That Require It

Documentation matters.
Traceability matters.

Nuclearn supports interaction logs that allow teams to review how an answer was generated and what information contributed to it. This supports internal QA, oversight reviews, and long-term recordkeeping.

Copilot is not built with these expectations in mind, and its outputs are less suited for environments where documentation must hold up under internal or external scrutiny.

 

6. Support From People Who Understand Nuclear Work

When questions come up, Nuclearn users work directly with Customer Success Engineers who have real nuclear backgrounds. They understand the workflows and constraints around:

• Engineering programs
• Licensing processes
• 50.59 considerations
• Design basis work
• QA requirements
• CAP processes
  

This helps plants configure agents and workflows in a way that reflects real operational expectations rather than generic assumptions.

Generic help desks cannot offer that level of relevance or context.

 

When the Stakes Are High, Tool Selection Matters

AI is becoming an important part of digital modernization, but the approach has to respect nuclear expectations around accuracy, transparency, and traceability.

Regulated work.
Safety-significant considerations.
Audit-sensitive tasks.
Design basis implications.

These areas require tools that behave conservatively and provide pathways to verification.

Nuclearn is developed specifically with these expectations in mind.
Copilot is built for general productivity.

For teams evaluating how AI can support plant performance and analysis, understanding this distinction is essential.

Executive Summary

A new pattern is emerging in enterprise AI adoption: Shadow AI. Employees, frustrated with slow-moving official systems, are turning to consumer-grade AI tools like ChatGPT to get work done. MIT’s State of AI in Business 2025 study reports that over 90% of knowledge workers now use unsanctioned AI for drafting, research, and analysis.

In many industries, this trend raises governance and security concerns. In nuclear, it introduces regulatory, safety, and compliance risks that cannot be tolerated.

This paper examines Shadow AI as an enterprise trend, analyzes why it is incompatible with nuclear operations, and outlines how regulator-ready, domain-specific AI addresses the gap.

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1. Shadow AI: A Growing Trend

MIT’s research identifies Shadow AI as one of the fastest-growing dynamics in AI adoption:

• Broad worker adoption. Employees bypass enterprise systems and use consumer AI tools directly.

• Enterprise lag. Corporate IT and compliance groups struggle to deploy secure alternatives at the same pace.

• Risk exposure. Sensitive information is copied into cloud-based tools with little oversight.

In industries like retail or marketing, the risks are financial. In nuclear, they are regulatory and existential.

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2. Why Nuclear Cannot Tolerate Shadow AI

Nuclear operations rely on strict compliance regimes that consumer AI tools cannot meet:

• Part 810 Regulations. Export control prohibits the uncontrolled transfer of nuclear technical data. Shadow AI platforms, typically hosted on global cloud infrastructure, are non-compliant by default.

• Licensing-Basis Sensitivity. Technical Specifications, FSARs, and design-basis documents cannot be exposed to uncontrolled platforms. Even summaries must be regulator-ready.

• Audit Requirements. NRC oversight requires every evaluation and document to be traceable and verifiable. Shadow AI outputs are not.

Simply put: Shadow AI creates compliance gaps that nuclear regulators and operators cannot accept.

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3. Case Study: Licensing Research

Observed Behavior:
Frustrated by slow search systems, engineers tested consumer AI tools to summarize licensing requirements. The AI produced text that appeared helpful but lacked citations and omitted key references.

Outcome:
Outputs could not be defended in NRC-facing documentation. The practice created risk, not efficiency.

Domain-Specific Alternative:
Nuclearn’s Gamma 2 model retrieves licensing basis documents from secure, on-premise repositories. Outputs include full citations, reasoning steps, and maintain alignment with IV&V. Engineers remain in control, but repetitive search is automated.

Result: regulator-ready documentation, compliant with Part 810, without Shadow AI risk.

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4. Shadow AI vs. Secure AI

The Shadow AI trend reflects a workforce reality: employees want faster, more usable tools. Restrictive policies alone will not stop Shadow AI. Without secure alternatives, adoption will continue underground.

The solution is not prohibition, but replacement. Nuclear operators must provide systems that are:

• As usable as consumer AI. Engineers will only adopt what improves their daily work.

• As secure as required. On-premise, Part 810 compliant, and regulator-ready.

• Domain-specific. Trained on nuclear acronyms, licensing structures, and workflows.

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5. Implications for Nuclear Operators

• Shadow AI is not theoretical. It is already happening across industries. Nuclear cannot assume immunity.

• Regulatory exposure is immediate. Even one instance of sensitive data entered into a consumer AI platform may trigger compliance investigations.

• Workforce demand must be addressed. Engineers will seek usable AI. If utilities don’t provide compliant systems, Shadow AI will fill the gap.

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Conclusion

Shadow AI is the new trend shaping enterprise AI adoption. In nuclear, it is untenable. The compliance, regulatory, and safety demands of the industry mean that consumer-grade AI tools cannot be tolerated inside the plant.

The solution is not banning AI use — it is providing secure, domain-specific alternatives that meet the same standard as the industry itself: safety, compliance, and reviewability.

Nuclearn demonstrates that when AI is designed for nuclear — Part 810 compliant, regulator-ready, and embedded in real workflows — it delivers measurable value while eliminating the risks of Shadow AI.

Executive Summary

Enterprise adoption of generative AI is widespread, but measurable impact remains rare. The MIT State of AI in Business 2025 report found that only 5% of enterprise AI pilots advance into production. The remainder stall due to integration challenges, lack of compliance alignment, and outputs that do not withstand scrutiny.

In nuclear energy, this failure rate cannot be tolerated. Pilots that never scale waste engineering hours, introduce compliance risk, and erode workforce trust. This paper examines why most AI efforts fail to transition, why nuclear’s regulatory environment magnifies the risk, and what design principles are required for AI systems to succeed in production.

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1. The Pilot Trap

Across industries, the “pilot trap” is common. Demos and small-scale trials show potential but collapse when scaled. Three recurring factors are identified in MIT’s research:

1. Workflow Misalignment – Pilots address isolated tasks but fail when integrated into enterprise systems.

2. Compliance Blind Spots – Outputs lack the transparency needed for audit or regulatory review.

3. Cultural Resistance – After repeated failures, workforces lose trust in AI initiatives.

For most industries, these failures represent opportunity costs. In nuclear, the consequences are higher. Every pilot requires engineering time, often from senior staff. If the pilot fails, scarce expertise has been diverted from safety and operational priorities.

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2. Why Nuclear Is Different

Nuclear operations impose requirements that generic AI tools rarely meet:

• Independent Verification and Validation (IV&V): All calculations, evaluations, and analyses must be reviewable. Outputs that cannot be traced to source data are unusable.

• Part 810 Compliance: U.S. export control regulations prohibit uncontrolled data transfer. Cloud-hosted consumer AI platforms cannot meet this requirement.

• Licensing Basis Alignment: Documentation associated with plant licensing must withstand regulatory audit. Outputs that lack defensibility introduce unacceptable risk.

These conditions mean that nuclear cannot rely on general-purpose AI. Tools must be designed specifically for regulated, documentation-heavy workflows.

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3. Case Study: Condition Report Screening

Nuclear plants generate thousands of Condition Reports annually. Each requires screening for safety significance, categorization, and assignment. Historically, this workload demands dedicated teams of experienced staff.

Pilot attempts with generic AI:

• Demonstrated short-term gains in categorization speed.

• Failed to provide traceable reasoning or regulatory-suitable documentation.

• Stalled at the pilot stage due to lack of reviewability.

Production deployment with nuclear-specific AI:

• Automated initial screening with embedded reasoning steps and citations.

• Retained IV&V by keeping engineers in the review loop.

• Scaled to full fleet use, saving tens of thousands of engineering hours annually.

This example illustrates the critical distinction: pilots demonstrate potential; production requires compliance-ready outputs.

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4. Case Study: 50.59 Evaluations

The 50.59 process determines whether plant modifications require NRC approval. Evaluations typically require 8–40 hours of engineering time and extensive document research.

Pilot attempts with generic AI:

• Produced draft summaries of licensing documents.

• Lacked sufficient traceability for NRC acceptance.

• Failed to progress beyond trial use.

Production deployment with nuclear-specific AI:

• Retrieved relevant licensing basis documents with citations.

• Assembled draft evaluations in ~30 minutes.

• Enabled engineers to complete reviews in ~2 hours, maintaining full compliance.

The ability to produce regulator-ready outputs was the determining factor in moving from pilot to fleet deployment.

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5. Lessons from MIT Applied to Nuclear

MIT’s research identifies three conditions for bridging the gap between pilots and production:

1. Domain Specificity: Tools must be trained on industry-specific data sets.

2. Workflow Integration: Systems must embed within existing processes rather than operate in isolation.

3. Adaptive Learning: AI must improve with use and align with regulatory context.

Applied to nuclear, these principles translate to:

• Training models on NRC filings, license renewals, and utility documents.

• Embedding tools into CAP, 50.59, and outage workflows.

• Designing outputs for traceability, citation, and regulatory review.

Without these conditions, AI pilots in nuclear will remain demonstrations with no lasting impact.

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6. Implications for Nuclear Operators

The findings have clear implications:

• Evaluate vendors beyond demos. Demand evidence of regulator-ready outputs, not just functional prototypes.

• Prioritize compliance from the start. Systems must be Part 810 compliant and built for IV&V.

• Focus on critical workflows. Target documentation-heavy processes where measurable impact can be achieved without compromising safety.

• Guard against cultural fatigue. Each failed pilot increases resistance. Operators should commit only to systems designed for production.

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Conclusion

The majority of enterprise AI pilots fail to transition into production. In nuclear, this failure rate is not sustainable. Documentation is safety-critical, compliance is non-negotiable, and workforce trust is essential.

To bridge the gap from pilot to production, AI systems must be domain-specific, workflow-integrated, and regulator-ready. Evidence from early deployments shows that when these conditions are met, nuclear plants can save thousands of engineering hours annually while maintaining safety and compliance.

The lesson is clear: nuclear must move beyond pilots. Production-ready AI, designed for nuclear, is not optional — it is required.

Introduction

Across industries, generative AI is being tested in pilots, proof-of-concepts, and trials. The promise is simple: automate routine work, generate documentation faster, and let knowledge workers focus on higher-value tasks.

But the data tell a different story. In its State of AI in Business 2025 report, MIT found that 95% of enterprise GenAI pilots fail to deliver measurable value. Most never move beyond a demonstration. They stall because they don’t integrate into workflows, they forget context, or they produce outputs that can’t be trusted in regulated environments.

For nuclear, this failure rate isn’t just disappointing — it’s unacceptable. Documentation in nuclear isn’t optional; it is the backbone of safety, compliance, and regulatory oversight. If an AI tool cannot produce outputs that are traceable, reviewable, and regulator-ready, it has no place inside the plant.

This is the GenAI Divide. Most industries are struggling to cross it. Nuclear requires a different approach.

What MIT Found

MIT researchers analyzed more than 300 AI initiatives and interviewed senior leaders across industries. Their conclusions highlight why adoption is high but impact is low:

• High pilot activity, low production: More than 80% of organizations have tested tools like ChatGPT or Copilot. Fewer than 5% of custom AI solutions made it to production.

• Generic adoption, limited disruption: Consumer tools help with quick drafting, but enterprise-grade deployments stall.

• The learning gap: Most tools don’t retain context, adapt to workflows, or improve over time. This brittleness means they can’t handle complex processes.

In short, pilots succeed at showing potential. They fail at delivering operational transformation.

Why Nuclear Can’t Afford the Divide

In many industries, failed pilots mean lost time or missed efficiency. In nuclear, they can undermine safety and compliance.

1. Documentation is not peripheral.
   Every Condition Report, Corrective Action Program entry, or 50.59 evaluation is required by regulation. These aren’t internal notes; they are part of the permanent regulatory record.

2. Traceability is essential.
   Every calculation, every engineering judgment, every modification review must be linked back to source material. If outputs cannot be cited and verified, they cannot be used.

3. Workforce turnover magnifies the need.
   With a quarter of the nuclear workforce set to retire within five years, plants need tools that help new engineers become productive quickly. AI that generates unreviewable or inaccurate documentation wastes scarce expertise instead of preserving it.

The conclusion is clear: nuclear cannot tolerate the 95% failure rate seen in other industries. AI must meet the same standards as the industry itself — safety, transparency, and compliance.

Nuclearn’s Approach

Nuclearn was founded by nuclear professionals who saw these challenges firsthand at Palo Verde. Our approach is fundamentally different from generic AI deployments:

• Nuclear-specific data sets: Our Gamma 2 model is trained on NRC filings, license renewals, technical specifications, and utility-provided documentation. It understands the acronyms, licensing basis requirements, and processes unique to nuclear.

• Reviewable outputs: Every output includes citations back to source material and exposes the AI’s reasoning steps. Engineers can perform independent verification and validation (IV&V) just as they would for junior engineer work.

• Workflow integration: Nuclearn doesn’t sit on the side as a chatbot. It is embedded into CAP screening, 50.59 evaluations, outage planning, and licensing research — the real processes that consume plant resources.

• On-premise, secure deployment: Data never leaves plant control. Our systems are Part 810 compliant and designed to meet U.S. export control regulations.

Case Example: CAP Screening

At a typical reactor, thousands of Condition Reports are filed every year. By regulation, every CR must be screened and categorized: is it adverse to quality? Does it require corrective action? Which group is responsible?

Historically, this requires full-time teams of experienced staff. It is repetitive, manual, and essential.

With Nuclearn:

• AI automates the screening and categorization process.

• Experienced engineers remain in the loop, reviewing and verifying.

• Plants save tens of thousands of hours annually, freeing highly skilled staff for higher-value work.

The process is faster and more consistent — but still compliant with regulatory expectations for reviewability.

Case Example: 50.59 Evaluations

The 50.59 process requires engineers to determine whether a proposed modification changes the plant’s licensing basis and whether NRC notification is required. It is one of the most documentation-intensive processes in the industry.

Traditionally:

• Each evaluation takes between 8 and 40 hours.

• Engineers must search thousands of pages of licensing documents.

• Work often involves multiple layers of review and verification.

With Nuclearn’s agent-based workflows:

• Relevant licensing basis documents are retrieved automatically.

• Key requirements and citations are assembled.

• Engineers receive a draft evaluation in about 30 minutes.

The final review still takes human expertise, but the process now takes ~2 hours instead of several days. Outputs remain fully traceable, with citations back to source material for regulatory confidence.

Aligning with Industry Findings

Where most AI pilots fail, Nuclearn succeeds because our approach directly addresses the barriers highlighted by the industry reports:

• Process-specific customization: We don’t try to solve everything. We focus on CAP, 50.59, outage planning, and licensing.

• Workflow integration: Our tools are embedded in actual plant processes, not running in isolation.

• Learning and adaptation: Our models are trained on nuclear-specific data and tuned for each utility.

• Compliance and traceability: Outputs are regulator-ready, built for IV&V.

This is exactly what MIT identifies as the path across the GenAI Divide: adaptive, embedded, domain-specific systems

Closing Thought

The MIT study is a warning. Most enterprises will spend money and time on AI tools that never scale. They will produce demos, not durable solutions.

Nuclear does not have that luxury. Our industry requires AI that can withstand NRC oversight, peer review, and decades of operational scrutiny. That is what Nuclearn delivers: solutions that are reviewable, verifiable, and regulator-ready.

If AI can meet nuclear’s bar, it can meet any bar.