Agentic AI Could Unlock US$550 Billion in Real Estate Globally

Agentic AI is transforming the real estate industry by compiling fragmented tools into integrated domain workflows. Research from McKinsey & Company indicates this transition could create up to US$550 billion value globally, but challenges such as data governance and organizational inexperience remain.

Leading real estate organizations are moving beyond isolated automation toward agentic AI to redesign entire workflows. This shift aims to capture billions in economic value by integrating humans and autonomous agents into core business domains.

McKinsey & Company indicates that this shift is occurring because the industry is moving from fixing small and separate tasks to managing entire business workflows from start to finish. 

Instead of using AI in single isolated steps, which often results in minimal financial impact, organizations are demanding to connect every part of a process to maximize efficiency.

"When you think about deploying a series of coordinated agents that pull data, draft outreach, summarize findings, and update systems, that is what we mean by transforming a domain," says Ankit Kapoor, Partner, McKinsey & Company.

AI adoption in the real estate industry is accelerating as organizations seek to improve productivity and operational efficiency. McKinsey & Company analyzed labor productivity across the value chain and estimates that AI could create between US$430 billion and US$550 billion in value.

While most companies use tools for specific processes and tasks such as reading leases, triaging maintenance requests, or supporting investment decisions, these efforts often remain fragmented and isolated, generating less market value market or ROI. To address this problem, leaders are moving their focus from individual tools to agentic comprehensive workflows. Instead of layering technology onto existing processes, executives are restructuring entire domains, such as leasing, operations, and asset management. 

The objective, according to McKinsey Partners, is to redesign entire business workflows and then let people work in close collaboration with AI agents.

The potential impact is significant, but capturing this value depends less on the deployment of technology and more on reimagining how work is performed. In a recent discussion, Kapoor, along with Alex Wolkomir and Vaibhav Gujral, Partner and Senior Partner, McKinsey & Company, explain that this technology is creating the most tangible value on three primary aspects: unlocking new value, redefining roles, and reshaping how organizations operate.

How is AI Generating New Value in the Industry?

All industries are beginning to realize the impact of agentic AI on profit and loss statements. In the real estate sector, Gujral says that value is already appearing on both the revenue and cost sides of the equation. Organizations that achieve real transformation do so by linking their technological investments to measurable financial impact. 

“Success is not defined by the number of employees using a tool, but by specific outcomes,” says Gujral. He adds that these metrics help reduce leasing time, improve maintenance responsiveness, and optimize vacancy days.

Value creation is particularly evident in three primary domains characterized by high human intensity and repetitive tasks, says Guraj: 

• The first is the front end, involving leasing and revenue-oriented activities. By moving from a traditional nine-to-five model to a 24/7 engagement model, companies can capture more leads and prevent leakage. 
• The second domain is property operations, where agents triage tickets and assign technicians more efficiently. 
• The third involves back-office functions, such as investment operations and financial reporting.

Redefining Functions and Redesigning Processes

The company proposes the adoption of the “domain approach” to optimize entire workflows, which means that enterprises must drive their AI adoption based on real key performance indicators (KPIs). 

In this approach, organizations must first determine which manual tasks are needed and can be automated. Domains represent “full workflows that can be redesigned from end to end.” For example, in financial reporting, a company can automate the entire cycle from data aggregation to the delivery of bespoke reports. Gujral says that such a redesign can reduce the time spent on these processes by 60% to 80%. 

This reduction is possible because coordinated agents eliminate the manual effort typically required to move information between different systems. McKinsey says that when agents are coordinated across an entire workflow, organizations often see improvements of 10%, 20%, or 30% in net operating income and cycle times. 

What Will be the New Human Labors?

As AI agents assume responsibility for manual coordination and data processing, human roles within the organization are expected to shift. Gujral says that the primary question is not which roles will be eliminated, but how existing roles will evolve. If two-thirds of an activity is performed by automated tools, the human role becomes one of evaluation, review, and ensuring accuracy and trust.

Kapoor explains that in investment functions, professionals will spend less time developing standardized reports or building models to detect signals. Instead, their focus will shift toward judgment-based decision-making, capital allocation, and maintaining relationships. 

These "moments that matter" represent the human element that technology cannot replace. 

Wolkomir emphasizes that in critical situations, such as a maintenance emergency, tenants require the empathy and responsiveness of a person rather than an automated system.

Future Outlook and Barriers

Despite the potential for value creation, several risks and barriers remain. Kapoor identifies the lack of clean, governed data infrastructure as a primary technical hurdle. “If an AI agent operates on erroneous data, it will produce erroneous outcomes,” says Kapoor. “There is no substitute for a solid data foundation.”

Gujral points to three additional risks: organizational inertia, treating AI as a simple information technology project rather than a change in the operating model, and concerns regarding trust and safety. 

Organizations must thus establish clear guardrails and data governance to move forward confidently. Furthermore, Wolkomir warns of a "race to the bottom" if every company uses the technology in the same way. Companies must design their voice and chat interactions to reflect their unique brand personalities.