Agentic AI is becoming the next competitive advantage in construction
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Artificial intelligence is entering a new phase in the architecture, engineering and construction (AEC) industry. According to McKinsey’s latest research, the next wave is not about AI copilots that answer questions or draft documents. It is about agentic AI, where autonomous software agents perform tasks, make recommendations and coordinate work across entire projects with human oversight.
McKinsey believes this evolution could generate up to $228 billion in annual value for the US AEC industry and a further $126 billion across Europe by 2030. The consultancy argues that firms capable of integrating AI into end-to-end workflows, rather than isolated tasks, will be best positioned to improve productivity and compete for increasingly complex projects.
For an industry facing persistent labour shortages, rising costs and growing infrastructure demand, the implications extend well beyond efficiency. Agentic AI has the potential to change how projects are designed, managed and delivered.
The industry’s productivity challenge has created an opening for AI
Construction remains one of the world’s largest industries, with McKinsey estimating global output reached approximately $15 trillion in 2025 and could grow to $22 trillion by 2040. Yet despite its scale, productivity growth has lagged behind many other sectors.
According to McKinsey, construction productivity increased by only around 10% between 2000 and 2022. Manufacturing, by comparison, improved productivity by approximately 90% over the same period.
Several structural factors explain this gap. Construction projects generate vast amounts of information across design teams, contractors, suppliers and owners, yet much of that data remains fragmented across different systems and organisations. Decisions often rely on manual coordination, repeated data entry and disconnected communication, creating delays and increasing the likelihood of costly errors.
Generative AI has already helped reduce some of this administrative burden. Engineers can summarise technical reports, project managers can produce meeting notes automatically and designers can accelerate early-stage concept development. These applications save time, but they typically support individual tasks rather than improving how entire projects operate.
McKinsey argues that the next opportunity lies in connecting these activities through agentic AI.
Agentic AI shifts from assisting people to coordinating work
Unlike traditional generative AI, which responds to prompts, agentic AI is designed to pursue objectives by completing sequences of tasks with limited human intervention.
Within an AEC project, multiple AI agents could operate simultaneously. One agent may review contract requirements, another could validate engineering drawings against project specifications, while others monitor procurement schedules, identify supply chain risks or flag potential programme delays before they affect construction.
Instead of employees manually moving information between departments, AI agents can exchange data directly while keeping project teams informed when human judgement is required.
McKinsey estimates that AI could automate roughly 50% of nonphysical work performed in architecture and engineering and around 39% of comparable activities in construction. Importantly, the consultancy does not suggest replacing technical professionals. Rather, automation is expected to reduce repetitive administrative work, allowing engineers, architects and project managers to spend more time on design decisions, client engagement and risk management.
This distinction matters. The greatest value is unlikely to come from generating reports more quickly. It will come from reducing project delays, improving coordination and helping teams make better decisions using consistent, real-time information.
Data quality will determine who benefits most
The technology itself is only part of the equation.
Agentic AI depends on access to reliable, structured and connected data. Many engineering and construction firms possess decades of valuable project information, but much of it is stored in disconnected software platforms, spreadsheets or document repositories that limit its usefulness.
According to McKinsey, organisations should focus first on building strong data foundations before attempting to deploy sophisticated AI systems across the business. Poor-quality data will simply produce poor-quality recommendations, regardless of how advanced the underlying models become.
Governance is equally important.
Engineering decisions carry legal, contractual and safety implications that require human accountability. AI may recommend actions or identify risks, but experienced professionals remain responsible for approving designs, interpreting regulations and managing project outcomes.
Academic research on enterprise AI adoption reaches a similar conclusion. Organisations that generate sustained value from AI typically combine technical investment with clear governance, workforce training and redesigned operating models. Technology alone rarely produces lasting competitive advantage.
The firms that redesign workflows will move ahead
The organisations most likely to benefit from agentic AI are not necessarily those investing the most in software. They are the firms willing to rethink how work moves across the business.
Instead of introducing AI into isolated departments, leading organisations are beginning to identify entire workflows that can be connected. Design reviews, procurement planning, scheduling, compliance, reporting and risk management become parts of a coordinated system rather than independent activities.
This approach also creates a compounding advantage. Every completed project generates additional operational data that improves future AI performance, making recommendations more accurate and workflows more efficient over time.
For many firms, the immediate opportunity is not full autonomy. It is identifying high-value processes where AI can remove repetitive work while employees retain control of technical and commercial decisions.
According to McKinsey, the transition to agentic AI will occur in stages rather than overnight. Companies that establish robust data governance, invest in workforce capability and integrate AI into core business processes today are likely to be better prepared as autonomous systems become more capable.
The AEC industry has spent decades searching for meaningful productivity gains. Agentic AI will not solve every challenge, but it represents one of the clearest opportunities yet to improve how projects are planned, coordinated and delivered. The firms that view AI as an operating model rather than another software tool are likely to set the pace for the next generation of construction and engineering.
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