Executive Playbook: Using AI to Shorten Outages at Nuclear Power Plants

by Steven Mauss, CEO & President at Knowledge Relay, Inc.

Every day a nuclear unit sits offline can cost operators more than $1 million in lost generation and contractor labor. Multiply that by the scope, complexity, and unpredictability of a modern refueling outage — tens of thousands of simultaneous maintenance and surveillance activities, large crews with diverse skill sets, scheduling and planning one to two years in advance — and it becomes clear why outage performance is one of the most consequential levers in your plant operating economics.

The good news: AI is now mature enough to address the specific challenges that cause outages to run long… Not as a distant promise, but as deployed capability available to operators today.

The Core Problem: Data Silos and Manual Lag

Most nuclear plants do not suffer for lack of data. They suffer because critical information is scattered across EAM systems, work management platforms, scheduling tools, vendor databases, and finance systems, each operating in isolation. During an outage, this fragmentation is dangerous, as outage teams spend valuable hours debating which numbers are correct instead of executing safely and efficiently. Decisions get made on stale information and emerging schedule risks go undetected until they cascade.

The traditional response (more manual reporting, more coordination meetings, more spreadsheets) simply does not scale. The answer is a unified data foundation that consolidates operational data in real time and delivers AI-powered insight directly to the people who need it.

Four Ways AI Can Shorten Your Outages

1. Predictive Maintenance Eliminates Unplanned Discoveries
   Machine learning models trained on sensor histories, equipment records, and work management data can anticipate component failures before they occur. When unexpected equipment degradation is discovered mid-outage—i.e., a cracked fuse block, a leaking seal, a failed bearing—it generates a new work order that wasn’t in the schedule, consumes resources earmarked for other tasks, and extends the critical path. AI-driven predictive maintenance moves those discoveries to before the outage begins, when scope adjustments are cheaper and the schedule can absorb them.
2. Natural Language Processing Learns from Every Past Outage
   One of the most underutilized assets in nuclear operations is the historical record of past outage activities: The actual durations, the emergent work orders, the issue reports that document why planned tasks ran long. NLP-based tools can now mine this data at scale, matching a queried activity to semantically similar activities from past outages across multiple units or utilities to produce statistically grounded duration estimates. Rather than treating every activity as a fixed-point value, planners can understand the true variance and use it to identify which activities represent the greatest schedule risk before the outage begins.
3. Monte Carlo Simulation Reveals Hidden Critical Path Risk
   Even a well-constructed schedule built in Primavera P6 understates risk if activity durations are treated as fixed. AI-powered schedule resilience tools propagate duration uncertainty through the entire outage schedule using Monte Carlo simulation, producing a statistical distribution of possible completion times and revealing the conditions under which the critical path shifts. This means outage managers know, before the first valve is turned, which delays could cascade and where to pre-position resources. The difference between a 21-day outage and a 25-day outage is often a handful of critical path activities that no one knew were at risk and millions of dollars saved.
4. Real-Time Metrics Close the Decision Gap
   During outage execution, speed of decision-making directly determines schedule outcome. Centralized metrics that pull live data from all plant systems—consolidating scope, schedule, cost, and resource availability into a single, trusted view—eliminate the manual reconciliation lag that delays management decisions during outage cycles. When metrics refresh automatically and KPIs identify variances in real time, plant leadership can act on emerging issues within minutes rather than hours or days. The right data, in front of the right people, at the right time is the primary focus of Knowledge Relay and delivers a measurable impact on outage duration.

From Silos to Single Source of Truth

Each of the AI capabilities above depends on data quality, data integration, and governance. A modern data lakehouse architecture that merges the structured discipline of a data warehouse with the flexibility needed to handle time-series sensor logs, work orders, cost data, and unstructured documents provides the foundation that makes AI deliver at scale.

When business rules, calculations, and data definitions are standardized once and reused across the fleet, the reconciliation debates disappear and executive-level reporting reflects the same numbers engineering and project controls are working from.

For nuclear operators, this architecture must also meet non-negotiable requirements, starting with data sovereignty through deployment in your secure on premises or private cloud environment, along with role-based access controls that align with NRC regulatory expectations. In a data lakehouse architecture, innovation and security are not trade-offs; they are both mandatory.

What This Means for Capacity Factor and Operating Costs

The industry average capacity factor is approximately 81.5%. Leading operators with advanced analytics capabilities consistently operate above 93%.

That gap is not primarily a function of reactor design or workforce quality; it is a function of information quality and decision speed. With 75% of nuclear plant production costs tied to fixed operations and maintenance expenses, any capability that shortens outage duration, reduces unplanned work, and improves execution quality has direct and material impact on the economics of the fleet.

AI does not replace the experienced outage manager, the skilled craft worker, or the judgment of your plant leadership. It gives them the information they need, faster and turns decades of outage history into a competitive asset rather than an untapped archive.

One Solution. One Truth.

Knowledge Relay delivers One Truth across teams, unifying plant operations data so the right people get the right data at the right time. We have served commercial energy operators for over forty years, and approximately 75% of U.S. nuclear companies rely on Knowledge Relay for metrics, analytics, and integration today.

Email or DM me today to learn how KR AI solution (and our new KR Data Lakehouse®) can help shorten outages for your nuclear power plant in 2026.