Here’s how machine learning can help redefine capital projects: PwC

US Capital Projects and Infrastructure Leader, PwC US

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Principal, Capital Projects & Infrastructure, Capital Projects Technology, PwC US

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Many capital project leaders are facing a complex building environment, marked by shifting costs, supply chain challenges and tighter margins for error. Our research shows that most large projects run over budget or behind schedule, often delivering less value than planned. The reason isn’t a lack of ambition. Rather, it’s that complexity has outpaced traditional ways of planning and forecasting. The next wave of project performance will come from using machine learning to inform artificial intelligence (AI) to make faster, more confident and more resilient decisions, in real time.

While AI’s potential is vast, for capital project leaders it’s quickly becoming a valuable tool for predicting what’s coming next. Machine learning, for example, analyzes data from past projects to anticipate outcomes, strengthen forecasting and improve execution. Forward-looking organizations are already using it to help reduce construction delays related to weather, scheduling, labor shortages, or materials sourcing. By embedding AI and machine learning into project management now, organizations can lay the groundwork for sustainable, resilient infrastructure, manage the energy transition, and drive smarter, more reliable growth.

Predictive modeling Smarter, faster, better: Machine learning’s impact on project outcomes

Machine learning (ML) is changing how capital programs come to life, from blueprints to breaking ground. How? Better data, connected systems, and scalable cloud platforms. Capital programs now generate rich, continuous data, from planning and design to construction and operation. That means leaders finally have the digital foundation to use machine learning in ways that move projects forward, faster. Today, ML can analyze millions of data points in seconds, turning what once felt impossible into everyday decision-making.

In turn, across the project lifecycle ML is redefining how teams plan, monitor, and deliver. Predictive modeling is at the center of that shift. By learning from past projects, these ML models can forecast what’s likely to happen next, helping teams act before challenges surface. Some common types of predictive models include classification, regression, time series forecasting, survival analysis, and anomaly detection models. For example, Random Forest and Gradient Boosting are widely used classification and regression models that can predict project risks or cost overruns. Time series models such as ARIMA or LSTM are used to forecast project schedules and resource needs.

Whether predicting cost or schedule pressures, managing resources or spotting early signs of risk, the goal is the same: to build projects that are completed on time and on budget.

Proactively flags high-risk tasks or opportunity areas, enabling teams to re-sequence work, reduce delays, and accelerate project timelines.

Uses models trained on past productivity and demand patterns to match labor, equipment, and materials to the right phases of work, thus reducing idle time and improving potential.

Surfaces early signals, such as delays, quality issues, procurement bottlenecks or commissioning risks, enabling stakeholders to plan mitigation strategies before issues escalate.

Monitors live field data using ML models to detect deviations from the plan and provide course-corrective insights.

Each of these use cases is ultimately about one thing: how ML empowers project leaders to make better, faster decisions when it matters most.

Machine learning Bridging strategy and execution: Machine learning and capital project delivery

Despite growing interest in ML, many capital project leaders still struggle to scale its value. In PwC’s Digital Trends in Operations survey, 92% of executives said their technological investments haven’t fully delivered the expected results. Translating ML’s potential into real impact requires more than just new tools. It calls for deliberate strategic updates and disciplined execution. Below are some steps that a capital project team can begin to take:

Organizations should focus ML efforts on areas such as scheduling, costs or risk where data-driven decisions can help drive measurable outcomes.

As ML is heavily reliant on structured data, it is imperative to have thorough and reliable data pulsing through project.

ML can only deliver value when paired with strong project controls and schedule discipline. This means integrating ML outputs directly into project workflows so that ML-driven recommendations are actionable and aligned with project governance structures.

Start small, but build the foundation, such as implementing project-management-as-a-service or embedded controls, to expand ML’s impact.

Tech strategy Why ML is the competitive edge in capital programs

When machine learning becomes part of how capital programs are planned, managed and delivered, it does more than boost efficiency. It also changes how leaders navigate complexity. With digital adoption accelerating, those leaning in now are both improving today’s performance and shaping the benchmark for success in the years ahead.

However, realizing this vision requires more than enthusiasm for new technology. Success hinges on aligning ML with organizational priorities, building data quality and embedding insights into execution workflows. When supported by strong governance and disciplined project controls, ML becomes more than a tool; it becomes a strategic capability that bridges the gap between aspiration and delivery.

Ultimately, the organizations that seize this moment to integrate ML into their capital programs can be the ones that define the industry standard going forward. In a sector where margins for error are shrinking and expectations are rising, ML offers not just incremental efficiency, but a transformative pathway to manage the energy transition, build sustainable, resilient infrastructure and strengthen competitive advantage.

Omar Rahman, Kapil Bhatia and Santiago Ruffo also contributed to this article.