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58% of surveyed U.S. property-management and real-estate professionals said they use AI in their jobs. That figure comes from an online survey of 3,662 people conducted in May and June 2026 through IREM’s membership and AppFolio’s contact database; it measures reported use by professionals, not the share of real-estate firms that have adopted AI. The distinction matters: trying an AI writing tool is not the same as integrating a dependable, governed system into company operations.
What the 58% figure does—and does not—tell you
The IREM and AppFolio survey findings reported by Florida Realtors describe individual respondents’ work practices. The online sample included U.S. property-management and real-estate professionals recruited through IREM’s membership and AppFolio’s contact database. It is evidence that workplace use is widespread among those surveyed, not a census showing that 58% of companies have adopted AI, or that most have moved beyond experimentation.
A different 58% appears in the National Association of REALTORS®’ 2025 Technology Survey: that was the share of respondents who named ChatGPT among the AI tools they used. It was not an adoption rate. The survey also named Gemini (20%) and Copilot (15%). Those results describe tool choice within that survey, not market share across real-estate firms.
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“Adoption” can mean several different things: an employee occasionally using a public chatbot, a team testing a vendor product, an AI feature embedded in an existing platform, or an AI-supported workflow operating in production with company data and oversight. The headline statistic speaks to the first of these more directly than the last.
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How agents and property managers are using AI
Writing and customer communication
For REALTORS® who use AI, common tasks include drafting listing descriptions, creating social-media posts, and preparing emails or follow-ups. In the NAR REALTORS® Technology Report, 75% of AI users reported using it for listing descriptions, 56% for social-media posts, and 52% for emails and follow-ups. These are reported tasks among AI users, not percentages of all agents or firms.
Property managers surveyed by IREM and AppFolio also described using AI to draft replies to leasing inquiries, prepare presentations, and process invoices. These examples show how AI can assist with recurring office work without necessarily making decisions or communicating independently.
Use is not the same as routine use
NAR’s 2025 Technology Survey shows a range of individual usage patterns. Among respondents, 20% said they used AI daily, 22% weekly, 27% a few times a month, and 32% had not used it in their business. In the NAR REALTORS® Technology Report, 23% of respondents reported daily use. These figures come from different NAR survey publications and should not be combined into one trend line.
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Why reported use does not guarantee business results
In NAR’s 2025 Technology Survey, 17% of respondents said AI had a significantly positive effect on their business, 33% said the effect was moderately positive, and 46% reported no noticeable impact. The NAR REALTORS® Technology Report separately says 55% of its respondents reported a positive effect. The samples and survey snapshots differ, so the figures should be read as separate findings rather than a direct year-over-year comparison.
Commercial real estate offers another view of the gap between interest and operational integration. In Deloitte’s 2027 Commercial Real Estate Outlook, 92% of surveyed CRE respondents were researching or piloting AI, while 8% said they had integrated AI solutions. Deloitte surveyed 950 C-level executives and their direct reports at CRE owners and investment companies with at least US$250 million in assets under management across North America, Europe, and Asia Pacific; fieldwork took place in June and July 2026. This is a senior-leadership sample at larger companies, not a measure of every real-estate business.
Deloitte also reports that nearly half of surveyed real-estate organizations had agentic AI running in some live production workflows. That description refers to a different deployment measure from the research-or-pilot versus integrated-solutions figures; it should not be treated as evidence that nearly half of firms have broadly integrated AI across their operations.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →One additional company-associated survey claim comes from Keyway’s 2025 State of AI Adoption in Real Estate article: 58% of respondents expected to purchase new AI software within the next year, and 76% reported significant data-infrastructure gaps. The article does not establish a probability sample of the industry, so these results are useful as a reported signal, not as a representative industry-wide estimate.
What real-estate firms are missing
Reliable outputs and accountable review
In the IREM and AppFolio survey, accuracy and reliability were the leading reported barriers to AI use, followed by training. A polished answer can still contain an invented listing feature, a wrong figure, or an unsuitable response to a tenant. Generated facts, calculations, tenant communications, and screening-related outputs need a named reviewer who can check them against authoritative records and correct or reject them.
That responsibility does not disappear when a calculation is automated. As WatchPoint Commercial Real Estate managing director David Barrow put it in the Florida Realtors report, “Liability is still there for property managers, whether AI does the calculations or a human.”
Staff training and clear rules
Only 8% of respondents in the IREM and AppFolio survey reported having a formal written AI policy, and 26% reported formal AI training. These are respondent-reported figures; they do not establish the prevalence of policies or training across all firms.
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A useful policy should make practical decisions clear to staff:
- Which AI tools are approved, and who approves new ones.
- What information may be entered, including rules for client, tenant, financial, and other sensitive data.
- Which tasks are allowed, restricted, or prohibited.
- What needs human review before it is sent, published, recorded, or used to make a decision.
- Where to report a questionable output, data exposure, or unexpected system action.
Usable data and integration
AI features are only as useful as the systems and information they can access. Deloitte identifies uneven data foundations and legacy processes as obstacles to moving from pilots into production. A tool that cannot reliably retrieve current, traceable property or transaction information may produce fluent but outdated or unsupported answers. Keyway’s reported infrastructure-gap result points in the same direction, with the sampling caveat described above.
Before connecting AI to a workflow, a firm should know where the underlying records come from, who owns them, how often they are updated, and whether an output can be traced back to a source. Integration also needs a clear way to correct, pause, or remove the system without losing control of the underlying process.
Governance for consequential workflows
Deloitte warns that AI agents operating without sufficient guidance may act in ways that are operationally biased or inconsistent with policy. It highlights tenant screening and pricing as areas that require care. The legal consequences depend on the system, use, facts, and applicable law; a general-purpose AI policy cannot replace review of a specific high-impact workflow.
For any system that can affect access to housing, pricing, financial decisions, or customer treatment, governance should specify what the system may do, which data it can use, who can access it, what actions require approval, and how activity is logged and monitored. People responsible for the workflow should be able to stop or override the system when it behaves unexpectedly.
Evidence that the tool is worth keeping
Usage is not itself a business outcome. NAR’s 2025 results show that many respondents did not notice a business impact, while the later report presents a separate, more positive snapshot. Firms need their own baseline to determine whether a tool improves the work that matters to them.
For a limited pilot, compare results with the existing process: staff time, response time, error and correction rates, service quality, customer satisfaction, and financial outcomes. Review those measures regularly, including the cost of the tool and the time people spend checking its work. Expand only when the benefit is repeatable and the controls remain effective.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical checklist before expanding AI use
- Inventory the systems. List approved AI tools, embedded AI features, pilots, and third-party services that process company information. Include the workflow, users, data involved, and vendor or service provider.
- Set data and use rules. Define what staff may enter, which tasks are permitted, and which uses require additional approval or are off-limits.
- Train the people doing the work. Teach staff how to check outputs, protect sensitive information, follow the rules, and escalate errors or unexpected behavior.
- Assign review responsibility. Name the person accountable for checking outputs before they affect clients, tenants, listings, records, or business decisions.
- Assess higher-impact workflows first. Review systems used for screening, pricing, or other consequential decisions for data quality, potential bias, access controls, audit logs, monitoring, and a workable stop or override process.
- Measure against a baseline. Track quality, errors, time, service, and financial results—not just logins, generated content, or the number of pilots.
- Scale selectively. Keep a workflow in pilot or discontinue it if reliable data, meaningful review, or measurable value is missing.
The people-and-process side remains essential even when the software is capable. NAR Deputy Chief Economist Jessica Lautz said in the association’s September 2025 release: “Technology continues to be a powerful force in real estate, driving efficiency and marketing innovation. But at the heart of it all remains the trusted relationship between the agent and client.”
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