If you’ve been experimenting with AI technologies in real estate, you know that AI does much more than recommend properties. It predicts market trends, optimizes investment strategies, and manages assets with laser-like precision.

Global real estate firms are no longer focused solely on boosting their profits and appealing to investors; they're diving into AI headfirst, eager to explore possibilities that were once the stuff of sci-fi. From hiring the most skilled data scientists to investing in the latest AI tools, everyone's racing to get ahead. But the true winners? They'll be the ones smart enough to select the right tools and agile enough to adapt as the tech wave continues to flow. 

Understanding the Data is the Foundation of AI Success

To truly benefit from AI's power in real estate, we must start by looking into the wealth of data at our disposal. Think about it—global real estate firms are sitting on mountains of documents for each property or tenant, from contracts to leases to insurance policies and more. Traditionally, sifting through this massive pile meant a lot of grunt work: abstracting and indexing everything manually, often with just basic OCR tech or outsourced help. And let's be honest, the old-school way isn't just tedious; it's also prone to mistakes, stuck in rigid categories, and totally misses the "unknown unknowns"—those golden nuggets of insight we don't even know to look for.

But top-notch data abstraction isn’t just a nice-to-have; it’s essential. It lays the groundwork for GenAI excellence. Global real estate firms that keep their data clean, well-structured, and accurate are already ahead in the GenAI game. But the true innovators have learned to leverage AI not just for insights but to enhance the quality and precision of their data, setting a new standard for what AI can achieve in real estate.

AI Use Cases in Real Estate

While AI use cases are endless, there are some very specific ways that GenAI can help global real estate firms improve their processes – before, during, and after document abstraction. This means that with GenAI, real estate firms can improve the quality of their data, and answer questions they've never been able to ask before. Here are some examples:

Data Enhancement

Lease Anomaly Detection: Find errors, inconsistencies, or missing documentation before they become a problem. For example, the wrong company name may be listed somewhere in a contract, there may be an address discrepancy between two documents, or a section may be missing from a sprawling 300-page contract. It’s like having an eagle-eyed assistant ensuring everything's in tip-top shape.

Abstraction Prediction Enhancement: AI abstraction tools and services generally have an 80% accuracy rate. But what about the other 20% of the data? Automatically connect a specialist AI tool known to excel in areas where the main tool is weak, thus achieving a perfect 100% accuracy mark every time. For example, if the main tool can answer 80 out of 100 question-types accurately, but fails to adequately answer the last 20, a different AI engine can be tapped to fill the gap.

Text Companion: Need a document in French or a quick summary of a lease agreement's termination clause? AI is like your personal translator and summarizer, ready to clarify and condense information with just a few clicks.

Data Analysis

GenAI Search Across Documents: Use simple chat to find information across your projects. Ask questions such as, “Which properties have the highest management fees?” or “Tell me about the roof coverage at 123 Main Street?” and get answers instantly, no digging through files required.

Risk and Compliance: Facing new regulations? AI acts as your strategic advisor, calculating potential risks and compliance needs across your portfolio. For example, consider a new law that requires ramps at all entry doors in commercial facilities over a certain size is adopted by roughly half the states in the US, with penalties ranging from $150K to $1M. AI can provide a liability report across all your properties. You can ask, “Where is adding the ramp my responsibility vs the tenants?” or “How many doors in total?” or “What is my risk based on property size, location, and penalty amount?” 

Choosing the Right Tools

Getting AI in real estate right isn't a one-size-fits-all journey. As a  CTO or CDO, you will need to explore a wide range of diverse tools, each designed to tackle different challenges within real estate management. 

There are many good LLM tools out there, each with its own strengths and weaknesses. Whether it's selecting a general LLM for broad applications or opting for specialized tools like building a workflow with RAG and graph indexing for specific tasks, the key is to match the right tools with the firm's strategic goals. 

Building a Strategic AI Roadmap

Embarking on an AI initiative demands careful planning. As a CTO or CDO, you must determine whether to build vs buy, which technologies to use, and how to implement.  You will need to decide whether to develop the AI project in-house, purchase various point tools, or opt for a subscription-based data and AI platform with infrastructure that can adapt to fit the company’s needs and resource constraints, including incorporating new data and AI stack.

Key considerations include:

Costs/TCO
When planning your AI budget, understand both one-time and recurring costs from previous projects. Setting up a tool isn't just about the upfront expense; it often involves hiring pricey DevOps engineers to get everything up and running. Plus, there's the upkeep to think about. AI tools may need regular updates, and adapting to these changes can be costly, especially if it means bringing in new skills or team members. A commercial data and AI platform with fixed fees can eliminate 90% of these costs. Calculating the TCO during the evaluation phase can help you avoid unpleasant surprises down the road.

Time to Deployment
The traditional route of deploying a new AI tool can be a slow slog involving heaps of red tape, complex stack configurations, and exhaustive testing — often taking weeks with an in-house DevOps team. In contrast, a data platform streamlines this to a matter of minutes and a single click. This ease of use frees data scientists from tedious admin tasks and empowers engineers, even those without advanced DevOps skills, to efficiently carry out their projects. For many organizations employing a data platform, tasks that used to stretch over a week can now be wrapped up in just a few minutes.

Security
Should you run AI models on private clouds or on-premises versus public clouds? Private setups offer enhanced data control and reduce vulnerability, keeping sensitive information securely within your environment. Public clouds, while scalable and cost-effective, may demand rigorous security protocols like robust encryption and strict access controls to safeguard your data. It is vital to evaluate which model aligns best with your specific security needs and compliance requirements.

 Flexibility and Adaptability
AI moves fast. Today's cutting-edge solution could be tomorrow's old news, meaning the methodologies, tools, and architecture you choose today might soon be outdated. Often, the ideal choice doesn't yet exist! Take the safe route by opting for solutions that can adapt and scale as new technologies emerge. Commercial data platforms offer seamless access to the newest tools in the industry, removing the hassle of frequent upgrades and the headaches of migration. They make staying current with the latest advancements as easy as possible.

Summary 

As AI gains traction in global real estate firms, your focus should be on choosing solutions that meet current needs and are both scalable and adaptable. AI is about turning massive heaps of data – such as textual documents – into gold mines, making smarter investment decisions, and redefining customer interactions in the real estate market. 

Learn more about how global real estate firms can integrate an enterprise data platform to develop and deploy applications utilizing the industry's best-in-breed tools, with security and privacy controls at the center of the solution.