Artificial intelligence is rapidly evolving from experiment to enterprise strategy for real estate companies, and REITs are well-positioned to lead if they treat AI as a strategic business initiative and not as a tech novelty. That was the central takeaway from a Nareit-hosted webinar this week, “Practical AI Strategies for REIT Leaders,” moderated by Nareit’s John Jones, SVP of government relations, and featuring RSM’s Robbie Beyer, leader of the firm’s data science and AI practice, and Nate Ruey, a trusted advisor to real estate clients on risk, data, and technology.
Jones opened the discussion by highlighting that “more than half of public companies now mention AI in their SEC filings, and over 40% of S&P 500 firms reference it on earnings calls,” a signal that AI is now a mainstream corporate priority, and one that’s “taken the real estate industry and the REIT ecosystem by storm.” REITs have an added advantage, he said, because the industry spans 14 property sectors, giving it a broad view into how technology is changing different parts of the built environment.
Beyer cautioned that a common misstep among real estate organizations is assuming that personal use of tools like ChatGPT translates directly to enterprise success, and “magically this is going to work.” Instead, he said, AI should start with strategy: “What are we actually trying to accomplish? What are the main value levers in our business? And how are we going to apply AI to support those?”
RSM, which has committed $1 billion to accelerating AI capabilities both internally and for clients, is seeing AI applied in three high-impact areas for real estate:
- Boosting productivity (copilots and chat tools that save time across thousands of tasks);
- Improving tenant service and predictive maintenance via AI agents; and
- Building “enterprise intelligence” by integrating data from building management systems and other platforms to accelerate investment, pricing, and portfolio decisions.
To illustrate the opportunity, Beyer walked through a use case many REITs will recognize: optimizing tenant retention and space pricing. By consolidating internal and external data, he said, companies can use AI to identify tenants at risk of churn, surface the reasons for dissatisfaction, draft tailored outreach, and at the same time, compare market signals to inform rent adjustments. With AI-powered dashboards, “operations can quickly jump in and engage” before revenue walks out the door.
Investor expectations are pushing the need for AI adoption. Jones pointed out that institutions now want “deeper transparency, faster insights, more tailored communication—and they want it yesterday.” Beyer added that AI can help by enabling more robust investor portals and “chat with your data” experiences that let investors get authoritative answers without waiting on an analyst to assemble reports.
Partnerships are becoming a key accelerator of AI success. Beyer noted that many REITs lack deep in-house data science or AI engineering teams and capabilities. The most effective initiatives combine the REIT, a services partner that understands both real estate and AI, and the underlying technology providers. That three-way collaboration, he said, “has really driven better outcomes” than trying to implement AI in isolation.
Speakers stressed that none of this works without getting the data house in order. Real estate organizations are often sitting on “rivers of data” spread across property management, finance, operations, and budgeting systems, Jones noted. Beyer said AI can now speed up data cataloging and integration, potentially cutting development time by 20-30%, but the goal is still the same: create a single, trusted layer of data and business rules so AI models return accurate results. Ruey added that strong data governance also reduces the classic “garbage in, garbage out” problem.
Security and responsible use were also front and center. As AI adoption grows, Ruey said, organizations need to balance innovation with protection—using encryption, access controls, vendor due diligence, and frameworks like NIST’s AI risk guidance to guard against data leakage or immature side projects that introduce risk.
In closing, Beyer said real estate is likely in the “second or third inning” of AI adoption—past the hype, and now in the phase where tangible business value is showing up. Future progress will depend on leadership’s ability to set the tone and foster a culture that embraces experimentation.
RSM is at the forefront of the AI conversation in real estate, helping REITs and other organizations move from exploration to execution.
Through practical insights and strategic guidance, they’re equipping organizations to turn AI potential into performance. Recent thought leadership includes:
- Real Estate Industry Demand for Optimization: This insight outlines how real estate firms can gain competitive advantage by modernizing systems, cleaning data, and integrating platforms so they’re ready to adopt advanced technologies.
- AI for CXOs: Achieving Enterprise AI Adoption: A leadership-focused guide that helps C-suite executives turn AI ambition into execution by aligning strategy, governance, use cases, and talent across the enterprise.
- AI Governance and Strategy Risk Assessment: A governance-first risk assessment service that evaluates an organization’s AI risk posture, prioritizes controls, and builds a roadmap for scaling AI responsibly and strategically.