Artificial intelligence’s purpose in commercial real estate is not to eliminate workers but rather to remove systemic delays and inefficiencies, says Aditya Sanghvi, senior partner at McKinsey & Co. and leader of the firm's global real estate practice, on this week’s Walker Webcast. 

Sanghvi pointed to a very common example found in almost every multifamily building across the nation. 

Say it’s early morning, and a pipe breaks in a unit, with water puddling on the floor. A resident will call management, which will log the request and call their plumber. After a few days, the resident might escalate if it hasn't been fixed. After another follow-up from management, the plumber may then fix the leak.

Every single step in this value chain is simple and quick, but there's a lot of elapsed time between steps, Sanghvi said. 

“There are dead zones that happen in almost every single one of the real estate processes, and those dead zones are ones that the residents feel,” he said. “You could have a team of [digital] agents and humans that move way faster and produce better outcomes.” 

Sanghvi said AI is not a particularly new concept, as it has been around for decades in industrial applications through robotic process automation and machine learning. But since 2022, three major developments have changed its trajectory: generative AI, agentic AI and multi-agent meshes, where multiple AI agents “talk” to each other, pass along information and coordinate decision-making. 

There’s an interesting paradox within the implementation of AI, however. Its adoption rate is incredibly high, but its actual return on investment? Not as impactful as one may think, he said. 

Sanghvi said 80% of companies reported using AI in some capacity to improve their workflow, but only 6% of companies reported material financial impacts from AI deployment. 

If everyone is largely using the same AI models, what’s the gap preventing companies from unlocking the full potential of AI? 

“The gap is actually the approach,” Sanghvi said. “First and foremost, when people deploy AI, they're largely deploying generative AI, not agentic AI. Less than 10% of deployments are agentic AI. Generative AI improves individual productivity, but it doesn't improve enterprise productivity.” 

The second reason for this gap is that CEOs are not taking enough ownership of their AI initiatives. 

“Most CEOs delegate AI to the head of IT,” Sanghvi said. “If you do this, you're already dead in the water. This is a business transformation and has to be led by the business and has to be done with the same rigor of any other transformation that a CEO would lead.” 

The third reason — and in real estate this is the most important reason — is data.

Sanghvi said AI agents are not yet at the place where they can take data that's not clean and make it clean, and won’t be for a while. They operate on shared context and data that is accurate and usable — which is precisely the issue.  

A Gartner report found that 60% of AI efforts were abandoned because of data quality. It wasn't the problem with the models. It was the problem with the data. 

“The problem is that if you have data that's 90% accurate, it's zero percent useful,” Sanghvi said.  

Data in real estate is “terrible,” he said, because all the players — from property managers to owners and developers — don’t speak to each other in an efficient manner. This includes proprietary spreadsheets, ledgers and CRM platforms, all of which don’t typically connect. 

“Tenants’ names sometimes don't match what’s in the property management system, which renders that data unusable,” Sanghvi said. “Real estate has not spent the time to put the right data governance in place. It’s very fragmented … a lot of CEOs still think about tech as a cost to be managed, rather than a value driver for the business.”

So where’s the opportunity for real estate and how should CRE be implementing this technology?

The answer, Sanghvi said, is where the biggest inefficiencies are. That’s why artificial intelligence is perfect for the rental housing segment, for example. 

In rental housing, such as multifamily and build-to-rent assets, a lot of the work to get a lease signed is about coordination. However, communication among property management, vendors, ownership, investors and residents is almost always flawed, he said. 

Agentic AI streamlines this coordination, easily handling work orders, scheduling vendors, creating investor reports, following up with residents and learning how to improve itself. In a multi-agent setup, AI can make decision-making proactive instead of reactive, as it’s historically been, Sanghvi said. 

“What agentic allows you to do is you have all these different agents that then can do very specific things,” he said. “One agent will actually build an entire profile of that person. Another agent will figure out what is the churn risk of that person based on information like amenity usage or number of kids that they have, and then it figures out the right move. This is basically a super intelligence for the renewal process.”

Moving forward, Sanghvi said companies need to stop viewing AI as a massive companywide rollout, or on the contrary, as something so small that it won’t make any meaningful difference to the business. 

Instead, Sanghvi advised companies to pick two or three items that agentic AI can really focus on, rewire workflows around AI — not just slap it onto an existing process and expect it to work — and focus on change management. People will need to be motivated to embrace change, which all starts with encouragement from the executive level. 

“You need to focus on having a business-led approach to data, not IT,” he said. “You need to have data accountability, data definitions, data quality measures and focus on data as the most important thing, because it is the most important thing.” 

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This article was produced in collaboration between Walker & Dunlop and Studio B. Bisnow news staff was not involved in the production of this content. 

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