AI adoption in real estate is accelerating. Across the industry, organizations are deploying tools to read leases, triage maintenance requests, and support investment decisions. But in many cases, these efforts remain fragmented—focused on improving individual steps rather than transforming how work actually gets done.
Forward-looking leaders are now shifting from tools to workflows. Instead of layering AI onto existing processes, they are starting to rethink entire domains—such as leasing, operations, and asset management—and redesign them end to end with humans and agents working in partnership. That shift will define the future of real estate.
The potential impact is significant. Estimates suggest that AI could unlock up to $550 billion in value across the real estate value chain. Capturing that value will depend less on deploying technology and more on reimagining workflows, prioritizing human judgment, and scaling new operating models.
In this video Explainer, McKinsey’s Alex Wolkomir, Ankit Kapoor, and Vaibhav Gujral discuss where AI is already creating tangible value in real estate, what separates genuine transformation from piecemeal experimentation, how the domain approach is reshaping entire workflows, and what leaders need to do to capture the full opportunity.
This interview has been edited for length and clarity.
What is the opportunity for AI in real estate?
Alex Wolkomir: The opportunity is immense, and it has the potential to really reshape how the industry works. The McKinsey Global Institute analyzed labor productivity and AI applications for tasks across the real estate value chain and estimates $430 billion to $550 billion in value creation. That’s really transformative.
Ankit Kapoor: I would say the biggest opportunity for AI in real estate to create value is not task automation, it’s not solving use cases here and there. It’s actually thinking about domain transformation. When you think about deploying a series of coordinated agents that pull data, draft outreach, summarize findings, and update systems, that’s what we mean by transforming a domain.
Vaibhav Gujral: We’re still in the very early innings of seeing the value of AI in real estate. We’re probably in the first or second innings, but even in these early innings, we’re seeing a tremendous amount of value hitting the P&L [profit and loss] of real estate organizations. We’re seeing this on both the revenue and cost sides of the equation.
What separates experimentation from transformation?
Vaibhav Gujral: Companies that are seeing real transformation are doing three things that others are not. The first is that they’re linking the AI developments and investments they’re making to real P&L impact. So they’re able to measure the reduction in leasing time, the reduction in cost, and improvements to responsiveness for maintenance requests.
Ankit Kapoor: Having senior business ownership is critical. AI should not be treated as another tool, but rather as a core strategic initiative with real investment and a cross-functional mandate across the business.
Alex Wolkomir: In terms of early lessons, usage is not an outcome. Success using AI is not everyone turning on the tool or everyone having an agent. Success is actually being able to answer, “Do we convert leases at a higher rate, did we get faster turnaround on a maintenance ticket, and did we reduce vacancy days?”
A lot of leaders look at the wrong metrics of success. I may have an AI tool that 95 percent of people open and think is cool, but is there actual value from its use?
What is a domain approach?
Alex Wolkomir: When you think about a domain approach, enterprise is too big, and use cases are too small. Enterprises are complex and not really feasible to transform all at once, and use cases are tiny steps that don’t connect. Domains are full workflows, like taking a lead from the first call all the way through signing a lease, that you can actually redesign end to end.
Ankit Kapoor: Attacking real estate at the domain level really allows you to drive transformative change across KPIs. When you coordinate agents across an entire workflow instead of solving one step, that’s when you start to see 10, 20, or 30 percent improvements in outcomes like net operating income [NOI], operating costs, and cycle times.
Vaibhav Gujral: Organizations that make a real impact are doing end-to-end redesign of workflows. They’re not just picking up specific use cases or activities and deploying copilots. They’re looking at the whole body of work performed and asking fundamental questions about whether that work needs to be done and how it can be automated.
A simple example is around financial reporting. We’re seeing organizations go all the way from aggregation of data to compiling of that data to providing bespoke reporting to various stakeholders, which cuts between 60 percent and 80 percent of the time that goes into that process. That is a full-on redesign of a workflow as opposed to an automation of a specific task.
Where is AI creating value today?
Vaibhav Gujral: We’re seeing tangible value across several domains, especially where there is high human intensity and repetitive tasks. The first is on the front end, around leasing and revenue-oriented activities. The second is property operations and maintenance, like scheduling technicians. And the third is in back-office functions, like investment operations and financial reporting, which require a lot of paperwork or manual reconciliation.
Alex Wolkomir: When I think about different domains where there is real value, a few come to mind. One is facilities and maintenance, where you can triage tickets, assign the right people, and resolve issues more quickly. Another is leasing, where moving from a 9-to-5 model to a 24/7 engagement model allows you to capture more leads and prevent leakage. In asset management, for example, all the rich data and documents come from properties that you can start to master for more scalable decision-making.
How will roles evolve in an AI-powered organization?
Alex Wolkomir: When considering the role of people in an AI-powered, agentic organization, I really think about those human moments that matter. If I have two feet of water on my floor at 3 in the morning, I probably want a person who will be responsive and empathetic, and I probably don’t want AI.
Looking for direct answers to other complex questions?
Ankit Kapoor: AI over time is really going to shift the types of roles that people engage in, from coordination and manual work to judgment-based decision-making, and working with tenants at moments that matter.
Let’s take an example in investing. Right now, people spend a lot of time aggregating data, developing standardized reports, monitoring data on dashboards, and building models to detect signals. We think AI is going to augment and potentially replace a lot of that work, so what people spend time on is going to shift toward exceptions, capital allocation decisions, and maintaining relationships.
Vaibhav Gujral: A lot of the debate we’re hearing is about what roles will be eliminated. I think the right question to ask is how existing roles will evolve. If you envision a particular team with a manager and a number of individuals performing an activity today, if two-thirds of that activity is performed by AI-enabled tooling, the role fundamentally changes. It becomes much more about evaluation, review, and ensuring accuracy and trust versus managing manual work.
What are the biggest risks and barriers?
Ankit Kapoor: If you build an AI agent on top of erroneous or dirty data, it is going to take an action based on that data, and you are going to have an erroneous outcome. There is no substitute for a clean, governed data infrastructure.
How agentic AI can reshape real estate’s operating model
Vaibhav Gujral: There are three primary risks: the first is massive organizational inertia and risk aversion. If you wait for error rates to go down and don’t experiment, you won’t build conviction or improve outcomes. The second is that organizations are approaching this as an IT project or an AI project versus a fundamental change in the operating model. And the third is about trust and safety, being proactive around establishing the right guardrails and data governance.
Alex Wolkomir: If everybody has the same way to do leasing or maintenance or interaction with AI, we are in danger of a race to the bottom. Where does the strategic differentiation come in? What are those moments that matter? How do you intentionally craft human moments to be different? And even in the AI itself, can you do things in a different way? Brands have different personalities. So how do you design voice, contextual, and chat-based interactions in different ways?
How should organizations get started?
Alex Wolkomir: First, it’s about deeply understanding all the different core processes. We tend to think about this in terms of what I call domains, or big workflows. So think about things like the experience of leasing, what it means to take a maintenance ticket all the way from opening to closing, and how you have to take those pieces and deeply understand the process itself, not just the technology.
Ankit Kapoor: What some real estate organizations think of as their differentiated approach to operational excellence may become table stakes across the board. So the bar really rises for everyone. The new competitive advantage will start to shift toward the internal trace data organizations capture that is unique to them and can differentiate their AI systems from those of others. What sort of continuous improvement feedback and human-in-the-loop learning does a company implement? And how often are people actually using these?
What will the future of real estate look like?
Alex Wolkomir: You could see a world where platforms emerge in a way that hasn’t been possible in the past. What I mean by that is, right now there is a very fragmented ecosystem of folks providing capital: owners, operators, and technology providers that they use. But does that start to look very different in a world where we know capital is preferring those who not just own but also operate themselves and have that operational expertise?
Vaibhav Gujral: For corporations, tenants, and residents within real estate assets, the experience is going to get significantly better. Corporations will get solutions that are much more tailored to their needs. Retailers will have a better understanding of where their store should be located within the mall and what types of traffic are most likely to flow and drive revenue at the store.
The other customers of the real estate industry are the investors and capital providers. By and large, for the operators that are operating at scale and making the right investments, returns are expected to increase.
Ankit Kapoor: The real estate industry is going to look vastly different in the future. The winners are not going to be those with the flashiest demos or who use the most technology. It’s going to be the firms doing the hard work under the hood and resolving issues before they require escalation.
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