AI’s strengths fall within some categories like customer engagement, creative content in text and images, insights from unstructured data, interpreting conversations and querying large data sources including interpreting, translating, and generating code.

In our own work with AI, we have seen real estate companies gain over 10 percent or more in net operating income through more efficient operating models, stronger customer experience, tenant retention, new revenue streams, and smarter asset selection. 

I have shared five examples of how businesses can apply AI in real estate issues.

1. Sifting through mountains of leasing documentation

AI can be applied to a repository of lease documents, which can be dense and filled with bespoke terminology, making it difficult for owners of many properties to sift through and find information at scale. AI–powered tools can summarize key themes across the leases, such as how much rent is expected monthly or what market forces (such as local environmental, social, and governance compliance laws) could affect leases. Additionally, the tool can scan across leases for a particular parameter (for example, all leases with a rent price per square foot below a certain level) and generate tables of information. At that point, professionals can examine the information the AI tool has compiled.

2. Copiloting real estate interactions

AI can be used to create a powerful copilot or a variety of real estate interactions, including managing tenant requests and lease negotiation. Simple requests from tenants, such as for routine maintenance, can prompt the copilot to directly contact a building’s maintenance staff. The copilot can identify a more complex question and flag it for a specialist at a property management company. As the specialist interacts with tenants, AI can observe conversations and written responses and suggest ways to improve communication. For high-stakes moments—such as a commercial lease negotiation with an office, warehouse, or retail tenant—a gen AI tool can take in all the information about a tenant, the property, and the market and craft a negotiation transcript. If communications and calls are recorded or turned to text, the copilot can monitor these interactions at scale, providing coaching while reminding specialists to refrain from using certain terms that could incite moments of risk.3

3. Enabling visualization and creating new revenue streams 

Today, when a prospective office tenant looks at raw space on a tour or a potential resident views pictures of an apartment on a listing site, they see an empty unit or photos filled with someone else’s finishes and furniture. Virtual reality tours have helped, but these static, non customizable simulations usually only go part of the way toward showing the end user what the result could be.

Gen AI tools can help a potential tenant visualize exactly what an apartment would look like in, say, their preferred mid century modern style or in cherry wood versus walnut finishes. This data can then be fed back into a model to predict which types of furnishings and finishes work best for different customer segments, improving prospect-to-lease conversion and shaping future capital expenditure decisions.

There can also be e-commerce tie-ins: as a prospective tenant tours a unit, an app can virtually impose a variety of couches, window trims, or kitchen appliances that match a desired design style. If the prospective resident decides to buy or lease, these choices can be ordered and set up to coincide with the move-in. The resident benefits by moving into a home that already expresses their signature style, and the brokerage or apartment company benefits by reaping revenue from cross-selling.

One large furniture retailer has launched an AI–powered product visualization tool that enables users to upload a photo of a room and populate it with furniture from its catalog. A variety of businesses throughout the value chain can use this capability to create new revenue streams while deepening customer loyalty.

4. Making faster, more precise investment decisions 

Today, investment decisions are often informed through individual analysis of bespoke data pulls across sources. An investor interested in warehouses, for example, typically starts by performing a macroanalysis of markets that have attractive factors such as ports, airport locations, and high e-commerce volume. Then, they perform more granular analysis to locate areas of interest, pulling building information from local brokers or digital tools. As part of the decision-making process, the investor conducts discrete analyses to figure out how their investment hypotheses have panned out in the past.

With an AI tool that’s fine-tuned using internal and third-party data, an investor can simply ask. The tool can sort through the unstructured data—both internal and third party This multifaceted analysis can be overlaid on a list of properties for sale to identify and prioritize specific assets that are worth manual investigation.

5. Drawing architectural plans known to create desired outcomes

In website design, there are specific patterns and design choices known to generate e-commerce sales or higher click-through. Similarly, there are underlying design principles in the physical world that AI can unlock and use to draw architectural plans.

A gen AI–assisted process can introduce Internet of Things sensors and computer vision algorithms that collect data points on space use, such as how customers move through a store before purchase or when conference rooms are used in an office. This insight—along with outcome data about sales, customer loyalty, productivity, employee retention, or other areas—can then be fed to a gen AI tool. This information can be overlaid with spatial data about square footage, location, walls, furniture, and other architectural elements. The gen AI tool can then develop architectural plans that are optimized to create desired outcomes in a space. Human architects and designers can work from these plans to ensure art and emotion in the design, but with less guesswork over whether a space is purpose driven.

The idea of getting started can be daunting, but we urge executives to start simply. Our technology professionals expertise in AI development advocate for an agile approach: identify two use cases that can launch a company into taking ownership of data, deliver measurable impact quickly, and build excitement; and identify two use cases that are more aspirational, will fundamentally change the business, and take more time to deliver. This approach encourages companies to push the technology toward its full potential.

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