Key topics covered in this article:

• Artificial intelligence is becoming a meaningful growth opportunity for real estate companies, helping improve efficiency, reporting, portfolio management, and investor communications.
• High-impact uses of AI in real estate include automating reporting and analysis, enhancing tenant service, enabling predictive maintenance, improving energy management, and strengthening risk assessment.
• Successful AI adoption depends on clean, reliable, and connected data, along with strong governance, security, and a practical implementation strategy tied to business needs.

For many real estate developers and owners, artificial intelligence still sounds abstract. It’s often grouped together with automation, analytics, or software upgrades, making it hard to tell what actually qualifies as AI and why it matters now. However, AI is far less futuristic than it sounds.

For real estate companies, AI is emerging as a major growth opportunity. Many are still in the early stages of adoption, but AI is starting to move from experimental to expectation. Now, industry leaders are starting to decide what role AI should play in the business and how to build a strategy around it. Understanding the landscape is the first step.

The Business Case for AI in Real Estate

Research estimates that AI could generate about $34 billion in efficiency gains for the real estate industry in the next four years. That figure reflects cost savings and time reductions that are already showing up in areas like reporting, operations, and portfolio management.

Pressure to adopt AI is also coming from investors. Many now expect faster access to information, clearer explanations of performance, and more consistent reporting across portfolios. AI helps businesses meet those expectations by improving how data is pulled together and analyzed. As a result, AI is becoming part of how real estate companies compete for capital instead of a back-office or personal-use tool.

High-Impact Applications for Real Estate

AI is creating tangible gains for real estate, and more advanced capabilities are expected in the near future. So, where are real estate companies seeing the biggest impact right now?

Productivity Gains

Productivity gains are the most common entry point. Real estate leaders are using AI to automate many time-intensive tasks. For example, some are using it to speed up certain processes like pulling information from multiple platforms or properties to assemble reports. Some are using it for basic analysis across spreadsheets or investment portfolios. The benefit is faster turnaround and improved accuracy, especially when manual entry has been involved in the past. This may help reduce labor costs and free up staff to focus on more strategic work and client relationships.

Tenant Service and Predictive Maintenance

At the customer service level, virtual assistants and chatbots can easily track and respond to leasing questions and maintenance requests. This has the potential to drastically improve response times and reduce the labor costs. Predictive maintenance tools analyze building and equipment data to identify potential issues before failures occur.

Energy Management

To take it a step further, AI can improve energy efficiency within a building or an entire portfolio. For example, AI systems can monitor and adjust building systems like HVAC and lighting in response to building usage and weather changes. They also help with tasks like time-consuming sustainability reporting.

Risk Assessment

AI is excellent at detecting patterns. For example, it can review rent payment patterns and maintenance requests to identify tenants who may be at risk of leaving. Then the operations team can follow up or even direct AI to send a customized message to investigate further.

AI can also be used to compare property performance and other market data. It may flag locations where rents are falling behind the market or expenses are out of line with similar properties. This allows operations and asset management teams to step in earlier, using AI insights alongside their existing reviews rather than replacing them.

Enterprise Intelligence

Some real estate companies are using AI across the organization rather than in isolated departments. This kind of adoption depends on having data that can move between systems, including property management platforms, accounting software, and internal reporting tools. Companies operating at this level have usually spent years modernizing systems and, in some cases, building internal AI capabilities.

The payoff is not uniform across the industry. It tends to be stronger in segments with high labor automation potential. This includes lodging and resort properties; brokerage and real estate professional services firms; and healthcare REITs.

Advanced Applications

A smaller group of companies is experimenting with advanced applications. These include sentiment analysis of earnings calls and stakeholder conversations, identifying weak points in discussions, and preparing for negotiation or lender questions. Adoption in this area remains limited and typically depends on mature data environments and strong governance.

The Readiness Gap

All of these examples share a common requirement: they depend on clean, reliable data. AI tools do not create information from scratch; they work with whatever data they are given. If lease terms are recorded differently in each system, rent rolls are out of date, or maintenance notes are inconsistent, AI will reflect those problems.

In real estate, “good data” usually means that property, tenant, and financial records are accurate and current, and that key terms are defined the same way across the organization. It also means that core systems (property management, accounting, leasing, and building operations) share information rather than operating on their own. When data is incomplete or buried in manual spreadsheets, it is difficult for AI to add value to the business.

For leadership, this is a strong place to start asking questions and engaging with staff members. What’s the confidence level in the data used to run the business today? Where are the biggest gaps? What would it take, over the next year or two, to make that data more reliable and easier to use? Addressing those questions often does more for AI readiness than any single tool selection.

Implementation Considerations

Most real estate companies are approaching AI incrementally. Adoption typically begins with a limited number of processes tied to existing workflows rather than broad transformation efforts. Projects linked to reporting efficiency, operating costs, or tenant service tend to gain traction more quickly.

Security and governance are a critical part of the discussion, especially where tenant or financial information is involved. Risk management is key. Advisors are encouraging businesses to pay closer attention to access controls, vendor oversight, and internal guidelines as AI is more widely used.

Note: AI is a powerful tool that augments human capabilities, not a replacement for human judgment, creativity, and accountability. It is not always accurate, and it can make mistakes. Data shared through public AI systems and chatbots may be accessible to outside sources. 

Looking Ahead

AI is not replacing real estate expertise. Instead, it is becoming another lever to help real estate companies grow to meet the needs of their tenants and investors. Looking ahead, the leaders who invest in data integration and system modernization today will be better positioned to use AI as a competitive advantage tomorrow. To learn more about AI and its impact on real estate, contact Jennifer French or Ryan Paul, Partners leading PBMares’ Real Estate team.