How AI automation can fit into construction workflows: McKinsey
Why this matters
The integration of artificial intelligence into construction workflows marks a pivotal juncture for institutional commercial real estate, with implications extending beyond operational efficiency to capital allocation and risk management. McKinsey’s framing of AI adoption as a strategic choice between building proprietary solutions or purchasing existing technologies underscores a broader tension in the sector: balancing innovation with cost control and scalability. For institutional investors and fund managers, this signals a potential inflection point in construction productivity—a historically stubborn bottleneck in CRE development timelines and cost structures. If AI-driven automation can meaningfully streamline project delivery, reduce labor dependencies, or improve accuracy in cost forecasting, it could reshape underwriting assumptions and risk profiles for new developments and renovations. Moreover, lenders and capital providers may begin to factor AI integration into their assessments of borrower resilience and project viability, especially as construction cost inflation and supply chain disruptions persist. The sector’s cautious but growing embrace of AI also reflects a broader recalibration of technology adoption in CRE, where the choice between in-house innovation and third-party solutions will influence operational agility and competitive positioning. Ultimately, how AI is deployed in construction workflows will be a bellwether for the pace and nature of technological transformation in US commercial real estate.
Editorial analysis · AI-assisted
As artificial intelligence continues to shake up the building industry, choosing how to build versus how to buy solutions is important, said an expert for the consulting firm.
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