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Marjan Gholamalipour is business leader for the Americas West program and project management at London-based design and engineering consultancy Arup. Markus Bernhardt is a principal at the artificial intelligence strategy consultancy Endeavor Intelligence in Walnut Creek, California. The opinions are the authors’ own.
The conversation about artificial intelligence in project management is resounding. A flurry of new tools promises to help people compose emails, summarize meetings and analyze data faster. While helpful, this focus on personal productivity often obscures deeper change. To find real value, we need to look beyond the immediate tools and into the emerging systems that will change the way we govern project execution.
Deepen the integration of AI
For most project professionals today, AI appears as a personal augmentation. This is the immediate impact of tools like Microsoft Copilot, which allows us to use generative AI to write a project letter or analytical AI to create a rapid cost forecast. The result is personal efficiency. These tools make people faster, but they don’t fundamentally change a team’s underlying processes.
Marjan Gholamalipour
Courtesy of Arup
Widespread adoption of these tools is essential to building literacy, but it is only the foundation for the next and most powerful application of AI: a shift from personal efficiency to systemic intelligence. Systemic intelligence incorporates AI into the governance of a project, program or portfolio. Rather than simply speeding up individual tasks, the system provides intelligent signals that enable decision makers to act quickly and confidently. The key distinction is this: Does AI only provide data for later analysis, or does it help project managers make real-time decisions?
Focusing on what matters most
Implementing systemic intelligence requires us to go beyond the traditional dashboard. While dashboards visualize data for human interpretation (“see what happened”), systemic intelligence actively uses data to prioritize attention (“decide now”).
Markus Bernhardt
Courtesy of Endeavor Intelligence
Consider a complex capital project where the supply chain is integrated into a systemic model. An AI agent, analyzing logistics and weather data, could detect a likely 72-hour delay for a critical steel delivery. In the traditional model, this might appear later during a status update. In a systemic intelligence model, the system compares this signal to an agreed tolerance. As the delay exceeds this threshold, it automatically opens a limited decision window for the purchasing manager. The choice presented is clear and responsible: approve expedited shipping for a 15% cost increase or formally accept the schedule sheet.
In this case, the system not only provided data, but forced a timely and auditable decision. This intelligence is typically triggered by signals that fall into specific categories, such as cost forecasts, collision detection anomalies, or contractual obligations. Each signal is a machine-generated vision that, upon crossing a preset line, demands human judgment.
For experienced project managers, this approach codifies the intuition they have used for decades. We often apply implicit tolerances to our projects, intuitively knowing which delays matter and which are noise. Systemic intelligence makes these tolerances explicit. This systematic filtering frees human attention from chasing small variations from the past and effectively focuses on times when a forecast error indicates a real need for intervention.
The operational impact: moving from informing to deciding
When there is systemic intelligence, the pace of management changes. The weekly status ritual, in which teams spend hours recounting what happened last week, is transformed into a forum for decision-making. Time traditionally spent on storytelling is reclaimed and sessions focus on clearing specific decision windows that the system has opened.
Ideally, the system analyzes the cost, schedule and resource impacts of a delay, presenting the project manager with the three main mitigation options. The human role is elevated from finding problems to using tactical judgment to make the final call. This clarity reduces the need for escalation and allows for a true “eyes and hands off” leadership approach.
Common Mistakes: Why AI Initiatives Stall
When do these initiatives fail? A common pattern is the “pilot theater”. This happens when a project team runs an impressive AI pilot that generates signals, but those signals aren’t connected to any formal decision points, so nothing changes and enthusiasm dies down. Another barrier is the black box problem. When the stakes are high, a leader will rightly refuse to act on a warning if the system’s reasoning is opaque. Trust is the currency of project delivery. If the AI can’t explain its provenance, specifically which data sources it used and why, it will be ignored.
None of them are pure technological failure. These are gaps in governance, process design or data quality.
First the Foundation
Ultimately, the transition to systemic intelligence is not just about installing software. It’s a redesign of how we value human judgment. The journey doesn’t start with a massive technology investment, but with a simple question: “What’s the most important decision we have to make this week, and what data would help us make it better?”
However, such intelligence cannot exist in a vacuum. It requires a foundation of truth. If our project data is trapped in disconnected files and digital silos, no amount of AI can save the schedule
