How AI and digital twins are transforming project management in Canada

Project management rarely attracts the same level of attention as artificial intelligence, renewable energy or biotechnology. Yet behind many of Canada’s largest infrastructure, healthcare, transportation and technology projects, are several innovations. Over the past year, many Canadian organizations have embraced digital twins, artificial intelligence and data-driven delivery models, changing how complex projects are planned, monitored and executed.

Faced with rising project complexity, labour shortages, supply-chain uncertainty and pressure to deliver value more quickly, project leaders are seeking new approaches that move beyond traditional schedules, spreadsheets and status reports. The result is an emerging generation of project management tools that are more predictive, data-centric and, probably, ‘intelligent’.

The rise of the digital twin

One of the most significant developments has been the adoption of digital twins for major public infrastructure projects.

A digital twin is a dynamic virtual representation of a physical asset, process or project. Unlike a static model, it continuously incorporates data throughout a project’s lifecycle, creating what many practitioners describe as a “single source of truth” for decision-making.

Ontario has become a leader in this area. The province is actively testing digital twin technologies on complex infrastructure projects including hospital developments, transit expansions and the redevelopment of Ontario Place. Infrastructure Ontario and its partners are evaluating how virtual modelling can help identify design conflicts, reduce delays and improve coordination between project stakeholders long before construction begins. The technology also has safety benefits. By accurately mapping underground utilities and existing infrastructure before work starts, project teams can reduce the risks associated with unforeseen site conditions. This lowers the likelihood of costly rework while improving schedule predictability.

Digital twins can continue operating after project completion, supporting asset maintenance, operational optimisation and lifecycle management. This creates a seamless connection between project delivery and long-term asset performance.

A recent white paper produced by the Future of Infrastructure Group and Arup argues that digital twins could improve capital and operational efficiency by between 20 and 30 percent if adopted widely across Canadian infrastructure projects. The report identifies digital twins as a solution to persistent challenges such as cost overruns, fragmented communications and project delays. Infrastructure projects typically involve multiple organizations, contractors, consultants and regulators. Each stakeholder often operates with their own systems and datasets. Digital twins help integrate information into a unified environment, improving transparency and enabling more informed decision-making.

Ontario’s Ministry of Transportation has also established a roadmap linking Building Information Modelling (BIM) and digital twin technologies, with a long-term objective of integrating digital twins into future infrastructure contracts.

Artificial intelligence enters the project office

If digital twins provide visibility, artificial intelligence provides insight. AI is now being incorporated into project management offices (PMOs), where it is helping organizations move from reactive reporting to proactive decision-making. Rather than simply documenting what has already happened, AI systems can identify emerging patterns and forecast future outcomes.

Applications include schedule optimization, resource allocation, risk forecasting, change impact analysis and portfolio prioritization. AI can analyse large volumes of project data significantly faster than human teams, enabling earlier identification of potential problems. For example, a project management system may detect subtle indicators suggesting a schedule delay several months before conventional reporting methods would identify the issue. Similar approaches can be used to highlight potential budget overruns, resource bottlenecks or stakeholder concerns.

The long-term objective is the “AI-augmented PMO.” Traditionally, project management offices have focused heavily on governance, reporting and standardization. AI is enabling a shift toward decision intelligence, where project teams receive data-driven recommendations rather than simply historical reports. In this environment, project professionals spend less time compiling information and more time interpreting insights, engaging stakeholders and making strategic decisions. AI becomes an analytical partner rather than a replacement for human expertise.

This approach aligns closely with Canada’s broader artificial intelligence strategy, which emphasizes practical deployment of AI across industry and government to improve productivity and competitiveness. The federal government’s recently announced AI for All strategy seeks to expand AI adoption across multiple sectors of the economy.

This shift is driving the adoption of common data environments, integrated information platforms and digital engineering approaches that allow project participants to work from shared datasets. Such systems reduce duplication, improve consistency and make it easier to identify emerging risks. The trend is particularly relevant for complex infrastructure programmes, where delays frequently arise due to poor information flow between stakeholders. Better data integration means fewer surprises and stronger governance.

A shift toward predictive project management

Perhaps the most important change occurring in Canada is the move from retrospective reporting to predictive management. For decades, many project reviews focused on answering a relatively simple question: what happened? Modern project management technologies instead ask: what is likely to happen next?

Digital twins provide a real-time picture of project status. AI analyses patterns and future risks. Integrated data environments create transparency across organizations. Together, these innovations enable project teams to anticipate problems before they occur rather than reacting after issues emerge.