The architecture, engineering, and construction (AEC) sector has long grappled with stagnant productivity. While manufacturing saw a 90% improvement in productivity between 2000 and 2022, construction managed a mere 10% increase. However, a new report from McKinsey & Company suggests that agentic AI could be the catalyst that finally breaks this deadlock, projecting a massive financial opportunity for firms willing to fundamentally redesign their workflows.
According to the report, "How AI is reshaping the future of the AEC industry," AI and automation technologies could unlock approximately $228 billion in annual value for the US AEC sector by 2030. In Europe, the impact on the construction sector alone is estimated at $126 billion. These are not incremental gains; they represent a structural shift in how projects are delivered and monetised.
Automating the Nonphysical Work
The core of this transformation lies in the automation of knowledge and coordination tasks. McKinsey estimates that up to 50% of nonphysical work in architecture and engineering, and 39% in construction, is susceptible to automation.
This encompasses everything from drafting proposals and generating estimates to identifying constructability issues and managing supply chain logistics.
Unlike previous digital tools that functioned as isolated software applications, agentic AI can be embedded directly into core systems across the entire project lifecycle. These agents can orchestrate work across design, procurement, planning, and field execution. They excel at processing messy, unstructured information—such as engineering drawings, specifications, and field observations—meaning firms do not need perfectly pristine datasets to begin extracting value.
The report identifies specific high-impact workflows where automation will deliver immediate gains. Areas such as invoicing, data entry, and equipment inspections are expected to change the most by 2030.
However, the real value lies in the more complex workflows where AI can reduce coordination friction and accelerate decision-making. For instance, when a field superintendent discovers a late engineering change that affects prefabricated components, agentic systems can compare the issue against the latest 3D models, procurement records, and construction schedules within minutes, rather than the days currently required for manual coordination.
The Great Divide: Leaders vs. Followers
The report clearly delineates the market into two emerging camps. On one side are the leaders: firms that are actively redesigning their core delivery tasks and embedding AI into their end-to-end workflows. On the other side are the followers: companies that treat AI merely as a surface-level productivity tool, using it for isolated tasks without altering their fundamental operating models.
McKinsey warns that the advantage will rapidly shift away from these slower-moving incumbents. Value will increasingly accrue to technology vendors, AI-native startups, and forward-thinking clients who leverage these tools to demand faster delivery and lower costs. For AEC firms, the message is unequivocal: control the workflow and the underlying proprietary data, or risk commoditisation.
Institutionalising Experience
One of the most profound impacts of agentic AI will be its ability to institutionalise experience. Currently, the success of a complex construction project often hinges on the judgement of a few veteran professionals who have "seen this movie before." This reliance on individual expertise creates significant risk and inconsistency.
AI can capture lessons from prior projects—analysing schedules, requests for information (RFIs), and change orders—and integrate that knowledge directly into daily workflows. A junior planner can be alerted to a sequencing risk that only a seasoned scheduler would typically spot. By embedding accumulated experience within the system rather than relying solely on human memory, firms can deliver projects more consistently and price their services with greater confidence.
Strategic Shifts for Project Delivery
We see this as a critical inflection point for project delivery professionals. The focus must shift from simply adopting new software to completely reimagining how work gets done. The near-term gains will come from streamlining repeatable workflows where fragmented information currently causes delays and rework. In the medium term, the advantage will belong to firms that can transform their project data into reusable institutional assets.
The McKinsey analysis identifies more than 150 workflows across 25 AEC-related domains with varying degrees of AI and automation potential. This granular breakdown is crucial for firms seeking to prioritise their transformation efforts. Rather than attempting a wholesale overhaul of operations, leaders should identify which workflows would be best led by humans supported by agents, led by agents with humans making critical decisions, or fully automated. This nuanced approach allows for incremental value capture whilst managing implementation risk.
Ultimately, AI will connect design, planning, logistics, and site execution into a cohesive, automated operating system. Firms that successfully navigate this transition will not only improve their margins but will also establish defensible workflow ownership that is difficult for competitors to replicate. The competitive landscape will reward those who can orchestrate these complex systems effectively.
Takeaway
• Move beyond surface productivity: Using AI solely for isolated tasks is insufficient; firms must redesign end-to-end domains to capture meaningful value and avoid ceding ground to AI-native competitors.
• Data is the new competitive moat: The ability to leverage proprietary project data to train agents and institutionalise experience will be a primary driver of future profitability.
• Prepare for shifting value pools: As AI automates routine tasks, value will migrate toward firms that control client relationships, complex workflows, and the ability to charge for outcomes rather than billable hours.
• Address the knowledge bottleneck: Implementing AI to capture and distribute the expertise of veteran professionals across the organisation will reduce risk and improve project consistency.
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All content reflects our personal views and is not intended as professional advice or to represent any organisation.
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