The Future of Nuclear Project Controls: 10 AI Capabilities Every Operator Needs in 2026

by Lee S. Gliddon III, Executive Vice President at Knowledge Relay, Inc.

NOTE: This blog is an abridged version of Lee’s original article published on LinkedIn here.

For decades, nuclear energy Project Controls have been the gold standard of industrial precision: Orchestrating thousands of interdependent activities—from refueling outages to capital modifications—and navigating a maze of regulatory, operational, and financial constraints.

However, as the energy industry faces aging infrastructure and a shifting workforce, traditional methods are reaching their limits. The question is no longer whether AI belongs in the nuclear plant, but which specific capabilities will define the next generation of efficiency.

Based on Knowledge Relay’s recent survey of industry experts, here are ten AI-driven value propositions that can transform nuclear operations in 2026 . . .

1. Predicting the Top Five At-Risk Tasks Within Schedules
The foremost priority articulated by SMEs is the ability to proactively identify the tasks most susceptible to schedule slippage, prior to any actual delays manifesting. Through the deployment of AI-driven predictive models, it becomes possible to:

• Detect patterns in historical schedule delays
• Analyze conflicts in resource allocation and external constraints
• Forecast impacts upon the critical path of project execution

This functionality allows project planners and operational leaders to strategically focus their attention and resources on areas of greatest vulnerability, facilitating earlier intervention and significantly enhancing risk management and mitigation efforts.

2. AI-Assisted Work Package Planning
The preparation of work packages is an inherently painstaking and labor-intensive process, necessitating profound domain expertise. AI can serve as an invaluable addition to human planners by:

• Suggesting optimal sequences for task execution
• Identifying commonly omitted elements
• Flagging inconsistencies or absent prerequisites
• Recommending appropriate durations based on accumulated historical performance data

Rather than supplanting the role of seasoned planners, AI is envisioned as a tool to enhance the consistency, reduce the need for rework, and elevate the overall quality of planning outcomes.

3. Automatic Construction of Schedules from Scope and Engineering Plans
Despite technological advancements, many nuclear outages still depend heavily on manual schedule creation. AI offers a compelling solution by enabling:

• Direct conversion of scope documents and engineering plans into draft schedules
• Application of standardized logic templates
• Integration of relevant performance data from past cycles

Such capabilities can significantly accelerate schedule development, allowing planners to devote more time to refinement and strategic oversight rather than repetitive and time-consuming construction tasks.

4. Real-Time Identification of Workable Activities and Resource Matching
Nuclear outages are frequently subject to emergent conditions, such as equipment unavailability, radiation protection constraints, or unforeseen resource conflicts. In these dynamic situations, AI can provide the following benefits:

• Continuous evaluation of operational constraints
• Identification of activities that are unblocked and ready to proceed
• Automated matching of tasks to available and appropriately qualified personnel

This capability enhances flexibility within the schedule and helps to minimize unnecessary idle time during critical outage periods.

5. Enhanced Assistance with Scope Collection
Scope growth and the emergence of late scope items have long been recognized as sources of unpredictability within nuclear project execution. AI can support scope management by:

• Analyzing historical work data
• Reviewing trends in equipment reliability
• Comparing planned versus actual scope realization from preceding cycles
• Highlighting discrepancies or missing scope elements

By improving the accuracy and completeness of scope collection, AI can reduce late-breaking work and foster more predictable outage outcomes.

6. Machine Learning for Delay Probability and Critical Path Updates
Industry experts have emphasized the urgent need for dynamic, AI-powered critical path management. Machine learning models can:

• Continuously reassess the probability of delays throughout project execution
• Update the critical path considering real-time performance data
• Offer early warnings regarding shifts in project trajectory

This advancement positions Project Controls to move decisively from reactive to predictive management paradigms.

7. Identifying Variance Drivers: Explanations for Delays and Cost Overruns
AI is not limited to simply reporting that a variance has occurred; it can also expose the underlying causes, such as:

• Contractor productivity challenges
• Shortages in critical resources
• Failures in equipment or systems
• Regulatory holds and compliance issues
• Adverse environmental conditions

By furnishing timely and actionable explanations, AI supports the implementation of corrective measures and facilitates institutional learning for future project cycles.

8. Automatic Consideration of Labor, Regulatory, and Equipment Constraints
The management of constraints, including those related to workforce, regulations, and equipment, is recognized as one of the most significant bottlenecks in nuclear scheduling. AI can automatically assess:

• Availability of specialists and required resources
• Radiation protection boundaries and requirements
• System configuration and tagging protocols
• Lifting and scaffolding logistics
• Procedure dependencies
• Regulatory compliance requirements

Such comprehensive constraint evaluation ensures that schedules are not merely theoretically optimal, but also practically feasible in the context of operational realities.

9. Predictive Modeling for Cost Overruns and Vendor Performance
Financial management within nuclear outages is subject to considerable variability. AI empowers project teams to forecast cost overruns by:

• Analyzing previous project behaviors
• Evaluating vendor performance over time
• Reviewing historical variances in budget and schedule
• Integrating market conditions and inflationary trends

Improved forecasting accuracy supports effective budgeting and fosters proactive management of vendor relationships and performance.

10. Automated Work Order Review for Material and Resource Completeness
One of the primary sources of delay within nuclear projects is the submission of incomplete or erroneous work packages. AI can address this issue by automatically reviewing incoming work orders to:

• Flag missing or incorrectly specified materials
• Identify incorrect resource assignments
• Spot incomplete steps or conflicting instructions
• Verify staging of required tools and equipment

This process reduces the need for rework and ensures work packages are truly ready for execution when scheduled.

Conclusion: A Force Multiplier for Excellence
AI is not a replacement for the skill and experience of nuclear professionals. Instead, it is a tool capable of enhancing the safe, dependable, and efficient operation of nuclear plants.

By focusing on these ten core capabilities with AI, nuclear power plant operators can navigate the complexities of modern energy generation with unprecedented precision.

Read the unabridged version of the original article published on LinkedIn here or contact us to learn more!