The Best AI Use Case in Project Controls Is the One That Changes a Decision Before It Becomes a Delay

By: Steven Mauss, CEO of Knowledge Relay, Inc.

The energy industry does not need more AI demonstrations. It needs fewer late surprises.

Every Work Management and Project Controls leader knows the pattern. A schedule looks manageable until a near-critical activity slips. A resource constraint looks temporary until it affects multiple work fronts. A change seems contained until it lands in the forecast after the decision window has closed. A work package appears ready until a missing prerequisite, material issue, permit, engineering input, or qualified-resource constraint prevents the crew from starting as planned.

By the time the problem is visible in a standard report, the organization may already be managing the consequences instead of managing the risk.

That is the real opportunity for AI in Project Controls: not to create a more impressive dashboard, not to remove human accountability, but to identify signals early enough for experienced teams to make a different decision.

The best AI use case is the one that changes a decision before it becomes a delay.

What should AI do in project controls?

AI in project controls should help organizations use current and historical data to identify patterns, forecast likely outcomes, prioritize emerging risk, evaluate alternatives, and support accountable human decisions.

For energy utilities and operators, that can include:

• Forecasting completion dates and outage duration.
• Estimating the probability of delay.
• Identifying critical path and near-critical path exposure.
• Highlighting at-risk activities, work packages, and milestones.
• Forecasting cost performance, estimate at completion, CPI, and SPI.
• Identifying labor demand, resource constraints, and competing work.
• Analyzing variance drivers across projects, sites, or fleets.
• Running what-if scenarios for scope, resource, schedule, and execution changes.
• Recommending options for schedule optimization, resource leveling, and risk mitigation.

That list is intentionally practical. These are not “AI ideas.” They are Project Controls decisions that organizations make every day, often with incomplete or late information.

Where Work Management fits

Project Controls provides an integrated view of plan, forecast, performance, and risk. Work Management provides the execution conditions behind that view: whether work is sufficiently planned, resourced, released, and ready to proceed.

When these disciplines are connected, AI can look beyond milestone status and ask more useful questions:

• Which near-critical work packages are at risk because prerequisites, materials, permits, engineering inputs, or qualified labor are not ready?
• Which deferred or emerging work orders could affect the outage plan if they are not resolved within the next work window?
• Which work-package constraints are likely to consume schedule float or create competing resource demand?
• Which execution signals should trigger a schedule, cost, resource, or leadership escalation before the impact is visible in the forecast?

This does not replace Project Controls. It makes Project Controls more actionable by connecting predictive insight to the readiness and execution realities that determine whether the plan can be achieved.

Predictive is not the same as automatic

There is an important distinction between predictive intelligence and automation.

A predictive system can tell a planner that a work package is becoming high-risk because of logic changes, resource demand, float consumption, actual-progress trends, prerequisite status, material availability, planning readiness, or patterns from comparable work.

That does not mean the system should automatically change the schedule or reprioritize work.

In a high-consequence environment, accountable people should review the evidence, assess operational context that may not be represented in the data, evaluate alternatives, and make the decision. The AI’s role is to help them see sooner, focus faster, and compare options more consistently.

This is the operating model Knowledge Relay believes in: governed data, explainable insight, and human accountability.

It also aligns with NIST’s AI Risk Management Framework, which is intended to improve organizations’ ability to incorporate trustworthiness considerations into AI systems. For Project Controls leaders, that means defining the use case, data boundaries, review process, reliability expectations, and escalation path, not simply testing an algorithm.

The four questions that turn an AI pilot into a Project Controls capability

Too many AI initiatives begin with technology selection. A better starting point is four operating questions.

1. What decision are we trying to improve?

“Use AI for outages” is too broad.

A useful question is more specific: Which near-critical activity groups and associated work packages have the highest probability of delaying outage critical-path completion in the next 14 days, which readiness constraints are driving the risk, and what mitigation options are feasible given available qualified resources, materials, and operating windows?

That question has a decision owner, a time horizon, required inputs, potential actions, and a measurable outcome.

Other high-value questions may include:

• Which projects are most likely to exceed their approved cost forecast, and what variables are driving the change?
• Which resource constraints will affect the next work window?
• Which scope changes are most likely to consume remaining schedule float?
• Which vendor, contract, or work-package signals should trigger earlier leadership attention?
• Which work packages are planned for the next work window but are not execution-ready, and what is the projected effect on critical-path or near-critical-path activities?
• Which recurring work-management constraints, such as late material availability, incomplete prerequisites, permit delays, or qualified-labor shortages, are creating the largest schedule and cost exposure across projects, sites, or outages?

AI becomes useful when it is tied to a decision someone is responsible for making.

2. Do we have trusted data and the right business context?

This is where many promising pilots fail.

Project Controls data often lives across Primavera P6, SAP, Maximo, Asset Suite, budget systems, cost systems, document repositories, resource-management tools, outage reporting, and spreadsheets. Each source may be useful. None is automatically complete enough to support a predictive operational decision on its own.

For AI-enabled decision support, the goal is not simply to combine schedule and cost data. It is to connect the plan to the conditions required to execute the work.

That means bringing together Project Controls information; activities, logic, float, milestones, forecasts, budgets, changes, and resource plans, with relevant Work Management information, such as work-order status, work-package readiness, planning completeness, material availability, prerequisite status, permits, hold points, labor qualifications, priority, and actual field progress.

The integration matters because a scheduled activity may show as planned while the related work is not yet executable. Conversely, field conditions may reveal a constraint early enough for Project Controls to reforecast, sequence work differently, adjust resources, or elevate a decision before the risk becomes a delay.

Before modeling, organizations need to know:

• Which source is authoritative for each data element.
• Whether data definitions are consistent across sites, projects, and functions.
• How historical snapshots are preserved.
• Whether the schedule logic, status data, cost information, and work-readiness information are sufficiently reliable.
• What operating context and business rules must be included to interpret the signal correctly.

As we have written previously, the biggest obstacle to AI success in project controls is not the AI. It is the data foundation and domain context around it.

3. Can the result be explained and acted on?

A high-confidence forecast that does not explain its drivers may create more resistance than value.

The planner, project manager, outage manager, work-management leader, or executive needs to understand what is changing: which activities, work packages, constraints, trends, dependencies, resources, assumptions, or data-quality issues are contributing to the risk signal.

They also need options.

A useful Project Controls AI output does not stop at “red.” It helps answer:

• What changed?
• Why does it matter?
• How confident is the forecast?
• Which readiness or execution conditions are contributing to the risk?
• What options are available?
• What are the likely tradeoffs among those options?
• Who needs to review or approve the action?

This is how a prediction becomes decision support.

4. Can we measure whether the decision improved the outcome?

An AI project should be judged by operational impact, not by the number of models deployed.

For a schedule-risk use case, measures might include lead time between risk identification and escalation, forecast accuracy, mitigation cycle time, critical-path stability, avoided delay, and the percentage of near-critical work released with required readiness conditions in place. For cost forecasting, measures might include forecast accuracy, variance detection lead time, and the time required to reconcile estimates.

The important point is to define measures before the model is celebrated.

Why Project Controls and Work Management are the right AI starting point for energy utilities

Project Controls sit at the intersection of the plan, the work, and the decision. Work Management turns that plan into executable work and captures the conditions encountered during execution.

Project Controls connects schedule, budget, scope, resources, risk, contracts, work packages, progress, and performance. Work Management connects the practical conditions that determine whether those work packages can proceed: planning quality, readiness, materials, permits, engineering inputs, labor, priorities, and field feedback.

Together, they create a closed operational loop:

1. Project Controls establishes the integrated plan, forecast, and risk view.
2. Work Management confirms whether the planned work is ready and executable.
3. Execution progress and constraints return to the integrated data environment.
4. AI identifies emerging risk, explains the drivers, and evaluates feasible mitigation options.
5. Accountable leaders, planners, schedulers, work-management personnel, and operations teams make and execute the decision.

This is why AI value is not limited to a more sophisticated forecast. The real value is earlier, better-grounded intervention, before a readiness issue becomes a missed milestone, a resource conflict, cost growth, or an outage delay.

It also makes Project Controls one of the most dangerous places to apply AI casually. A schedule recommendation that ignores operating constraints, an inaccurate resource forecast, or an unexplained risk score can erode trust quickly.

The answer is not to avoid AI. It is to deploy it with the same operational discipline used for other critical decision systems.

The Department of Energy is advancing an AI innovation ecosystem for science, energy, and security. The opportunity for utilities is not simply to follow the technology trend. It is to put AI to work against high-value, well-defined operating problems where the quality of the decision can be measured. U.S. Department of Energy: AI Innovation Ecosystem.

A practical maturity path for AI-ready Project Controls and Work Management

The fastest path to meaningful AI value is rarely a single large program. It is a staged capability build.

Start with automated, trusted metrics

Connect the systems teams already use. Automate recurring data extraction, reconciliation, historical retention, and high-value metrics across Project Controls and Work Management. In addition to schedule and cost performance, establish reliable measures for work-package readiness, prerequisite status, material availability, planning completeness, backlog health, field progress, and constraint aging.

Establish a reliable Data Depot that reduces manual reporting and gives leaders a consistent view of performance and execution readiness.

Build a governed Project Controls and Work Management “One Truth”

Expand the data foundation into a Lakehouse that connects the approved plan with the operational information required to execute it. Incorporate the structured, semi-structured, and unstructured information needed to understand projects and outages in context. Govern definitions, lineage, validation, access, calculations, and the relationship between schedule activities, work orders, work packages, resources, materials, constraints, and actual progress.

Apply AI to a narrow, high-value decision

Choose a decision with material impact, a defined owner, enough historical data, a clear action path, and measurable results. A strong early use case is identifying near-critical work packages that are unlikely to be execution-ready in time, and evaluating feasible mitigation options before the related schedule exposure becomes a delay.

Schedule risk, forecast completion, outage-delay probability, resource constraints, cost forecast drivers, work-package readiness, and constraint resolution are often strong candidates.

Scale from insight to increasingly automated outcomes

As data, trust, workflows, and governance mature, organizations can extend AI into more advanced scenario analysis, readiness forecasting, work-package prioritization, constraint management, recommendations, schedule building, work-package development, budgeting, resource assignment, and risk-register support.
The progression matters. Starting with a reliable operating foundation improves both the performance and the credibility of later AI capabilities. For the data-foundation side of this journey, read: The Foundation Is Not the Finish Line: How “One Truth” Turns AI into Measurable Plant Performance.

The Knowledge Relay perspective

The future of Project Controls is not a future where people are removed from the decision. It is a future where Project Controls and Work Management operate from connected, governed information—and where experienced people are no longer forced to manage complex energy projects and outages through fragmented data, manual reconciliations, and late indicators.

Knowledge Relay helps energy and nuclear organizations build AI-ready Project Controls and Work Management environments: integrating enterprise systems, automating metrics and reporting, creating a governed One Truth, and applying predictive analytics to the decisions that affect schedule, cost, resources, readiness, risk, and execution.

The question for leaders is not simply, “Where can we use AI?”

Rather “Which decision, if made earlier and informed by both the plan and the readiness of the work, would most improve execution?”

If your organization is ready to identify that use case and build the data foundation to support it, contact Knowledge Relay to begin a practical maturity conversation.