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:
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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.
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Generic adoption, limited disruption: Consumer tools help with quick drafting, but enterprise-grade deployments stall.
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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.
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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. -
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. -
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:
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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.
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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.
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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.
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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:
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AI automates the screening and categorization process.
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Experienced engineers remain in the loop, reviewing and verifying.
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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:
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Each evaluation takes between 8 and 40 hours.
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Engineers must search thousands of pages of licensing documents.
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Work often involves multiple layers of review and verification.
With Nuclearn’s agent-based workflows:
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Relevant licensing basis documents are retrieved automatically.
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Key requirements and citations are assembled.
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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:
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Process-specific customization: We don’t try to solve everything. We focus on CAP, 50.59, outage planning, and licensing.
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Workflow integration: Our tools are embedded in actual plant processes, not running in isolation.
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Learning and adaptation: Our models are trained on nuclear-specific data and tuned for each utility.
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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.