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AI Business Case for Nuclear Operations Efficiency

At a Glance

AI Exploration

Researched potential use cases for LLM applications and outlined possible risks and benefits

Multivariate Enterprise Analysis

Used interviews and 5-year historical workload and performance data to estimate duration variance between manual and AI-assisted task completion

Time Savings and Productivity Gains

Identified opportunities for up to 400+ hours in time savings and nearly $9M in productivity gains

Challenge

A nuclear power plant operator sought to develop a business case to evaluate the potential benefits of large language model AI integration to improve the efficiency of key engineering tasks. The ultimate goal was to reduce manual workload associated with complex technical evaluations such as regulatory screenings and maintenance reviews while building a scalable framework for broader AI adoption.​

Process

  • Interviewed engineering personnel across experience levels to compare manual vs. AI-assisted task durations
  • Analyzed five years of historical workload data across three representative use cases
  • Modeled 10-year net present value (NPV) using three complementary approaches: use case expansion, workforce productivity improvement, and avoided reliability events
  • Identified more than 25 high-value additional use cases and developed scale-up implementation scenarios
  • Assessed AI time savings, productivity impact, and potential risk reduction across operations

Result

  • Estimated 330 to 405 hours of annual time savings across three initial use cases, valued at up to $39,000 or around $10,500 per use case on average
  • Identified potential for more than $8.8 million in NPV through workforce productivity gains for a typical engineering function
  • Projected up to $5.5 million in avoided costs by preventing one to three plant trips over 10 years through redirected engineering

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