Embrace AI in Commercial Real Estate & Stay Ahead of Your Competition

inMotion Real Estate Media is a commercial real estate marketing agency that provides creative solutions to help drive results for ambitious CRE firms


Artificial Intelligence (AI) is quickly becoming a defining term of this decade. While it has existed since the twentieth century, the majority of people hadn’t heard or cared much about it until very recently. Now, professionals in the commercial real estate industry are sitting around the table having serious discussions about how this new technology might make or break their futures. 

The potential is great, as research firms like McKinsey & Company report seeing gains of over 10 percent in net operating income through enhanced operating models, customer experience strategies, and asset selection practices. There are also risks, however, especially for CRE organizations that get on board with generative AI without the right strategy. This article aims to demystify it all so you know and have what you need to be competitive in 2024 and beyond.

A Quick Crash Course In Generative AI

Important conversations like these should always be well-informed. You’ve undoubtedly heard about generative AI by now – but do you really know what it is or how it works? Let’s set the stage with an executive summary:

What Is Generative AI?

Generative AI is a subset of artificial intelligence (AI) that focuses on creating new and unique content, as opposed to traditional AI which relies on analyzing existing data. It involves using algorithms and machine learning techniques to generate original ideas, images, music, text or other outputs. It essentially mimics the human creative process by using probabilistic models and deep learning to produce new content based on input data.

The Many Forms and Faces of AI

ChatGPT’s success has not gone unnoticed by other tech giants, with Google and Microsoft both making strides in their own AI-powered chatbot development. The formerly mentioned company recently released its highly anticipated Gemini AI in the beginning of 2024. Microsoft is a financial backer of OpenAI but also has its own assistive AI bots, namely Copilot AI and Azure AI Bot. These products work the same way as GPT-4 but are developed, trained, and managed by their respective owners.

At the crux of an AI model’s usability is the tool it’s built into. As mentioned before, ChatGPT isn’t a language model in and of itself, but rather connected to GPT-4. Similarly, Gemini AI and Copilot AI maintain their own neural networks from which they draw their knowledge.

The actual products that most commercial real estate professionals use today – such as ChatGPT, Jasper AI, and Copy.ai – act as a gateway between AI models and end-users. Interfaces can be configured to one or more models and deliver output for specific applications and contexts. For example, a chatbot tool and document writing tool might both get their output from GPT-4 but are set up to deliver that information differently.

Language models can be further trained and customized based on a business’ unique requirements and use cases. This involves adding information to the data a bot has already been trained on to expand its knowledge base and improve its responses. In commercial real estate, that might look like training a chatbot to understand industry-specific terminology and market trends, or familiarizing it with a company’s own internal processes.

In any case, AI is believed to get smarter with time as it’s fed new questions and information to learn from. The more data and feedback the model receives, the more accurate and effective it becomes in delivering relevant and helpful responses.

Use Cases for Generative AI In Commercial Real Estate

The potential applications for generative AI in commercial real estate are plentiful. Just like in other business markets, people have begun experimenting with the technology to see where and how it can make their day-to-day operations easier – and they’re finding many potential avenues of value. CRE is specifically expected to benefit from AI in four different domains, which McKinsey & Company refers to as ‘the four Cs’. (Communication, Collaboration, Critical Thinking, and Creativity)


Generative AI’s biggest area of value – both in commercial real estate and outside of it – lies in creation. This specific type of artificial intelligence is different from others in that it’s capable of making things rather than simply analyzing and processing them. Large language models aren’t sentient but can eerily mimic human imagination when prompted the right way. They’re even better at producing materials based on pre-existing data.

Here are a few examples of how this could be applied in CRE:

Document Writing

Legal contracts like leases and purchase agreements make up a significant portion of the commercial real estate industry. Traditionally, they would be drafted by hand, by people with better things to do than write out standard legal language. Generative AI models have enough understanding of language patterns to produce entire documents from scratch. CRE companies can even equip them with contextual knowledge to personalize the agreements for different clients or properties.

Property Descriptions

Similar to legal documents, property descriptions are often written by humans and follow a standard format. However, with generative AI, these narratives can be personalized and tailored for each individual property and target audience. Executed at scale, this can significantly reduce the time and resources spent on developing property listings.

Visual Staging

Commercial real estate agents know that there’s more to a property than what meets the eye. The challenge is reminding buyers, who, with big decisions to make, don’t always see the bigger picture.

AI-powered 3D design is already being leveraged to imagine what a space will look like once it’s built out. By inputting building plans and specifications, AI can create stunning 3D visualizations that showcase the potential of a property. This not only helps buyers envision the space but also allows agents to showcase potential improvements and renovations.

Customer Engagement

It doesn’t matter how well sales agents know their market – success in every domain of real estate relies on knowing and being able to connect with your client. Luckily, generative AI can play a major role in powering customer engagement, and it has the capacity to do so on a very personal level.

Answers to Questions and Personalized Recommendations

Commercial real estate agents have a lot on their plate, from the technical processes involved in buying or selling a property to the relationships they must cultivate with clients. Everyday questions from prospective buyers are important but realistically fall near the bottom of the priority list. Firms that want to be able to do it all must either possess the ability to bend time or invest in additional in-house admin staff. 

Generative AI-powered bots offer a smarter solution. These aren’t the kind of chatbots you’re used to seeing on customer service websites – they’re conversational in nature and can render unique responses based on the specific conversation at hand. Businesses don’t have to sacrifice the customer experience for efficiency because these bots can handle a high volume of inquiries while maintaining a personal touch. 

To illustrate further, let’s say a customer is interested in a particular property and has questions about the neighborhood. The AI could respond with information about the area’s schools, crime rates, and amenities. It could even pull data from a variety of sources to provide a more thorough response than what an agent may be able to remember off the top of their head.

The value is made even greater by recommended listings based on the customer’s preferences and behaviors. If, for example, they mention being a dog owner, the bot could suggest properties with large yards or nearby pet-friendly parks. It could also gather data from previous conversations to learn more about the customer’s preferences and personalize its responses accordingly.

Enhanced Virtual Property Tours

While it’s always ideal, there are times when in-person property tours are not possible. The space may already be occupied by a tenant who’s unwilling to facilitate walk-throughs, or the building may not even be completed yet.

Traditionally, the only alternative option agents would have to show prospective buyers what a space looks and feels like is through pictures or basic VR derived from those pictures if they’re lucky.

AI-enabled computer vision technology is changing the game by stitching together images, videos, and other data sources to create a fully immersive experience for buyers from anywhere in the world. This application goes hand-in-hand with visual staging; advanced VR software can superimpose furniture, decor, and other elements onto the 3D visualization, giving buyers a better sense of scale and flow within the property.


Seizing great opportunities in commercial real estate is a matter of being competitive. And in order to be competitive, you need to be quick and concise. AI is both of those things and so much more, offering potential applications in everything from data analysis to decision-making.

Managing and Parsing Through Documentation

Computer vision gives AI the ability to ‘see’, or interpret the content of a document like a human would. The most meaningful difference for businesses, however, is that AI can scan and analyze enormous amounts of data at once, whereas humans are limited by time and energy.

This type of technology can be particularly useful for property owners who have a large portfolio of leases to manage. Instead of spending hours manually reviewing each lease and looking for specific information, AI makes it possible to analyze all the leases at once and generate organized tables or reports. This can save valuable time and resources while also reducing the risk of human error.

AI tools can also help in parsing through documents for keywords or phrases, making it easier to find information related to compliance with regulations or specific clauses in a contract.

Making Investment Decisions

In the world of commercial real estate, time is money. The longer a property sits on the market, the less profitable it becomes. That’s why being able to quickly and accurately analyze data is essential for making investment decisions. Traditional methods of analyzing data, such as manual analysis and individual data pulls across various sources, can be time-consuming and may not provide a comprehensive view of the market. Machine learning algorithms have the power to speed things up without missing a single detail. In fact, AI tools are notoriously better at identifying trends on a large scale and identifying potential areas of interest that human analysts easily overlook.

Some investors already use AI to help identify properties with the highest potential for return on investment by inputting criteria such as location, property type, and budget. This is similar to how a search engine uses algorithms to provide the most relevant results for a query. However, AI can take this process further by analyzing market trends, historical data, and even social media sentiment surrounding a particular property or location.


The last C of prospective value offered to the CRE sector by AI is coding. A more advanced and increasingly tangible application for commercial real estate companies, generative development tools put the expertise of seasoned software developers into the hands of non-technical employees. That’s game-changing for small to mid-sized companies who may not have the resources or budget to hire a full team of experts.

Developing Client-Facing Tools

Client-facing tools such as virtual tours and interactive floor plans give potential buyers or tenants a more immersive and personalized experience when exploring properties, and can be customized to match a company’s desired branding messaging using code. Out-of-the-box solutions may have decent feature sets, but the ability to tailor them to specific organizational needs is invaluable.

Streamlining Internal Processes

AI can also improve operational efficiencies by automating tasks that are repetitive, time-consuming, or prone to human error. For example, AI-powered property management solutions can analyze data and generate insights on tenant behavior, helping companies make more informed decisions about leasing, pricing, and marketing. Today’s market of generative software solutions is remarkably customizable – utilizing a combination of AI and code, companies can create bespoke dashboards that fit their internal workflow seamlessly.

Recognizing and Reconciling the Risks of AI In Commercial Real Estate

All novel innovations face roadblocks as they mature, and generative AI is no exception. This particular technology faces issues revolving around data sourcing, management, and privacy. All three have the potential to impact the practicality of deployment, as artificial intelligence is effectively the sum of its training data. Without proper measures in place to mitigate these concerns, the commercial real estate sector risks deploying something that creates more negative disruption than good.

High-Stakes Decision Making

There’s a significant ethical question to be considered when implementing computer-driven decision-making in any business. Can we really trust artificial intelligence to make high-stakes decisions that could significantly impact a company’s bottom line? What happens when something goes wrong and the AI makes a costly mistake? Who is ultimately responsible for these decisions, the AI or its human creators? These are important questions to consider, especially in industries like commercial real estate where large financial investments are at stake.

The only real solution to date is diligence. As AI continues to ‘take the reins’ of everyday business operations, companies will need to carefully and continually monitor output quality and accuracy.

Transparency and Accountability

To expand upon the worry about leaving high-stakes decisions to AI, it’s important to recognize that monitoring is about all businesses can do for quality control right now. Users aren’t privy to the data large language models are trained on, nor do they have disclosure of how outputs are determined. Most developers are out of the loop as well. Every tool is the product of its owning company’s intellectual property, and they’re not always willing to divulge details.

This creates a liability concern for commercial real estate organizations that implement AI into their business processes. If and when an AI-driven decision results in negative consequences, who is ultimately responsible? Is it the company that implemented the technology or the developer of the AI tool?

The answers to those questions remain largely unanswered. In fact, most places around the world don’t yet have any regulations for the use of AI technology, and because it’s so new, there isn’t much legal precedent to go on. Companies hoping to seize exciting new opportunities need to remember that every application comes with risks. It’s a matter of balancing the potential benefits and the potential consequences on a case-by-case basis.

Security / Data Privacy

Because AI uses input data as a means to continuously learn and improve, it’s hard to guarantee that the information users include in prompts is safe. Developers make an effort to exclude potentially sensitive data like personal information, but this doesn’t always work. Inaccurate or incomplete datasets can lead to biased results and potential privacy breaches.

This is especially concerning in the commercial real estate industry, where sensitive financial and personal information may be exchanged through AI tools and platforms. Companies must be diligent in ensuring the security and privacy of their data, as well as implementing robust measures to protect against cyber attacks.

Ethical Considerations

Large language models are the product of their training data. For most models on the market today, a majority of that data comes from the internet. It’s surely a great resource in terms of its vastness and diversity, but it also means that the data is not always reliable or free from biases. This raises ethical concerns about AI perpetuating societal biases and discrimination.

For example, a language model trained on text from online forums may learn and replicate harmful language or biases towards certain groups of people. This can have real-world consequences, such as perpetuating discrimination in job hiring or loan approvals. 

To address this issue, companies must be transparent about the data used to train their AI tools and actively work towards reducing biases in their models. This can involve diversifying training data sources and implementing checks for bias detection during model development. It might also involve implementing ethical guidelines and principles to guide employees’ use of AI in the workplace.

The potential value offered by AI to the commercial real estate industry is great. Even more excitingly, it’s within reach as of 2024. All that stands in the way is a proper action plan. Companies in CRE cannot afford to arrive late to the party with this technology. A countdown is on to get acquainted with and get on board with AI as the rest of the world continues to discover what it’s capable of.


Founded in 2006, inMotion Real Estate Media is a commercial real estate marketing agency that provides marketing and creative solutions to leading real estate companies.

Our team is comprised of award-winning designers, expert web developers, and real estate marketing professionals. Together, we have a clear understanding of the marketing needs of the real estate industry and the expertise to create solutions to serve these needs.

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