McKinsey Partner: Commercial Real Estate's AI Failure Is a Strategy Problem

The commercial real estate industry is adopting artificial intelligence at a rapid pace, yet the promised financial windfalls remain elusive. According to Aditya Sanghvi, a senior partner at McKinsey & Co. and leader of the firm's global real estate practice, the disconnect stems from a flawed implementation strategy rather than the limitations of the technology itself.

While 80% of companies report utilizing AI to enhance their workflows, a staggering gap exists in the bottom line: only 6% report material financial impacts, Sanghvi notes via McKinsey data. He argues that the industry is currently trapped in a cycle of improving individual productivity through generative AI without achieving true enterprise-level productivity. This is evidenced by the fact that less than 10% of current AI deployments are 'agentic AI'—systems capable of coordinating decision-making and executing complex tasks across an organization.

The Data Barrier

A primary obstacle to this transition is the state of industry information. Sanghvi describes real estate data as "terrible," noting that it remains fragmented across a chaotic mix of proprietary spreadsheets, ledgers, and CRM platforms. This lack of standardization creates a binary outcome for AI utility; as Sanghvi puts it, "The problem is that if you have data that's 90% accurate, it's zero percent useful."

Historically, the sector has relied on robotic process automation and machine learning. However, the surge of generative AI since 2022 has led many firms to simply "slap" new tools onto existing, broken processes. Without a CEO-led business transformation to rewire these workflows, the technology remains a superficial layer rather than a structural improvement.

Closing the 'Dead Zones'

The shift toward agentic AI represents a fundamental change in operational capability. By moving beyond simple content generation to autonomous coordination, firms can eliminate systemic "dead zones" in property management. Sanghvi highlights the elapsed time between a resident reporting a leak and a plumber actually fixing it as a prime example of an inefficiency that agentic AI could solve by automating the coordination between reporting, scheduling, and execution.

The Path Forward

For the industry to realize material gains, the focus must shift from tool adoption to data hygiene and organizational restructuring. The transition requires moving away from isolated productivity wins toward integrated systems where multiple AI agents manage end-to-end business processes.

What remains to be seen is whether real estate firms can overcome the cultural and technical inertia of fragmented data. Until CEOs lead a comprehensive transformation of how data is captured and utilized, the gap between AI adoption and financial impact is likely to persist.