PCL Innovates Data Management to Transform Industrial Projects

PCL Constructors Inc., one of Canada’s largest employee-owned construction firms, is advancing its efforts to standardize industrial project data. During a recent community event in Edmonton, Alberta, team members demonstrated how they are addressing the inconsistencies in technical descriptions that can stall construction processes.

Rowan Andruko, a computer science graduate on PCL’s industrial data science team, highlighted the issue of varied descriptions for identical components. “There is no industry standard for the description of these components,” he explained, showcasing two different descriptions for the same piping component — one from the engineering firm that designed the facility and the other from PCL’s fabrication shop in Nisku. Despite conveying the same information, the differences complicate data management.

Challenges in Construction Data Management

During the event, led by Built World Tech’s Zach Storms, Brian Gue, manager of data science at PCL, elaborated on the substantial challenges the construction industry faces. The massive data generated by large projects must be translated and standardized before any work can proceed. In Canada alone, investment in building construction reached $24.5 billion in November 2025, with $6.9 billion allocated to non-residential projects, including industrial initiatives, according to Statistics Canada.

Gue emphasized the importance of efficient data management, stating, “When we look down what the priorities for the country are, everything passes through a capital project funnel.” He noted that the performance of this funnel directly impacts how quickly Canada can meet its construction needs. However, productivity in the construction sector has stagnated, with a report from KPMG and the Canadian Construction Association revealing an average annual growth of just 0.4% since 1997, plummeting to a near 30-year low in 2023. This stagnation coincides with ongoing labour shortages, exacerbated by an aging workforce, as 270,000 experienced tradespeople are projected to retire between 2025 and 2034, according to BuildForce Canada.

Despite these challenges, PCL’s data science team is taking innovative steps to streamline processes. Gue described the industry’s issue as “information reconstruction from one party to the next,” where each handoff requires reinterpretation, leading to inefficiencies in large projects.

Innovative Solutions Through Machine Learning

PCL’s approach includes leveraging machine learning and artificial intelligence to enhance data classification and standardization. Andruko detailed their project, “Boyle.ai — Universal Technical Translation,” which seeks to automate the conversion of thousands of technical descriptions into a consistent format. Currently, this task is often performed manually, consuming significant time and resources.

“Material can’t be purchased until the descriptions are aligned,” Andruko noted, highlighting the urgency of their work. The team has developed a “confidence score” for each automated translation to build user trust in the system. This score provides insight into the reliability of the machine’s output. Gue’s team continually retrains their models, comparing performance to select the best iterations and discard weaker ones.

The challenge of data acquisition in construction is significant, with most of PCL’s data sourced internally. Andruko mentioned the arduous process of synthesizing data, which involves creating artificial datasets to enhance the algorithms’ learning experience, especially when historical records are limited.

Michelle Fribance, a data scientist and engineer with an arts background, underscored the importance of effective project reporting and forecasting. Her goal is to create modern and insightful reporting tools that help project leaders identify risks early on. “We want our clients to focus on things that matter, and things we need input on,” she stated, emphasizing the importance of user feedback in the early stages of tool development.

Some tools, like a system designed for geolocating devices within construction drawings, have emerged from immediate project needs. This tool allows teams to visualize equipment installations more effectively, ultimately saving time and reducing confusion about design intent.

Gue described these immediate needs as entry points for addressing broader industry challenges. “It’s usually a tip-of-the-iceberg situation,” he remarked, noting that solving one problem can lead to insights that benefit the entire sector.

PCL’s data science team operates with a startup mentality, dedicating roughly 80% of their time to prioritized business needs while reserving 20% for exploration and innovation. This structure aims to balance immediate project demands with the long-term vision of enhancing digital processes across the industry.

The journey toward digital transformation in capital projects begins with disciplined data management. As PCL continues to refine its approach to data standardization, the company illustrates how small, cross-functional teams can drive significant change within large enterprises.

In an industry where information flow significantly influences costs, schedules, and risks, PCL’s commitment to innovative solutions is a critical step toward enhancing productivity and efficiency in construction.