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AI is already part of many real estate agents’ routines, especially for drafting listing descriptions, social posts, and follow-up emails. But agent use is not the same as a brokerage-wide rollout: the available National Association of REALTORS® (NAR) figures measure member responses, not enterprise deployment across firms. For brokerages, the opportunity is to apply AI to a specific, low-risk task while putting human review, data rules, and clear accountability in place.
Where real estate agents are using AI
In NAR’s 2026 survey of its members, 23% reported using AI daily and 25% weekly. Among respondents who use AI, the most common reported tasks were listing descriptions (75%), social media posts (56%), emails and follow-ups (52%), market summaries (30%), and document review or summarization (27%). These are agent-reported uses, not measures of how many brokerages have integrated AI across their operations. NAR’s 2026 survey release and its REALTORS® Technology Report provide the member-focused context.
Content and communication come first
The leading tasks have a shared characteristic: they produce a first draft or summary that an agent can check before using. AI can help generate a starting point for property copy, adapt a social post, or organize a follow-up message. It does not verify that a home has a particular feature, that a statement complies with advertising rules, or that a message suits a client’s circumstances. Treat the output as draft material, not as independently verified property or transaction information.
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NAR’s broker coverage describes agentic systems that can draft or send messages, update CRM records, schedule follow-ups, and trigger workflows. Those capabilities are distinct from an agent using a tool to write a draft: a configured system may take actions without a person initiating each one. The more autonomy a workflow has, the more important it becomes to control its approved data, permissions, review points, and ability to stop or correct an action. NAR’s June 2026 broker coverage discusses both the potential and the risks.
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What the adoption figures do—and do not—show
NAR’s 2026 results indicate regular individual use among surveyed members, but they do not establish the adoption rate for all real estate agents, all brokerages, or real estate firms globally. They also do not show how many firms have integrated AI enterprise-wide. The figures therefore support a practical conclusion—AI is being used for recognizable agent tasks—rather than the claim that most firms are either fully deploying it or missing out.
For context, NAR’s 2025 survey reported daily AI use by 20% of respondents and weekly use by 22%; 46% said AI had made no noticeable impact on their business. These are findings from the prior year’s survey, not proof that usage rose by a specific amount or that AI caused a change in business outcomes. Survey methods and question comparability would need to be examined before drawing a direct year-over-year conclusion. NAR’s March 2026 policy coverage includes the earlier survey context.
Why agents adopt technology—and what gets in the way
In the 2026 NAR survey, respondents most often cited saving time (81%) and improving the client experience (71%) as reasons for adopting technology. Those are goals, not demonstrated AI-specific results: a brokerage should measure whether a particular AI workflow actually saves time or improves service rather than assuming it will.
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Risks brokerages need to manage
AI output can sound confident while relying on unsuitable data or making an unsupported claim. NAR’s broker guidance identifies several areas that deserve explicit controls:
- Data provenance and MLS compliance: A workflow may rely on stale or incomplete property information. MLS rules may not be machine-readable, so a tool’s ability to process data does not establish that it may use or publish it.
- Fair housing: Automated recommendations or messages can produce discriminatory patterns, including unintentionally. Review both the content and how automated decisions or targeting affect people.
- Accuracy and advertising: A generated description may invent a feature, make an unapproved claim, or omit a required disclaimer. Verify property facts and applicable advertising requirements before publication.
- Licensed work: Do not let a system independently perform tasks that require a licensed practitioner. NAR Chief Data Officer David Conroy warned, “Things can get really dicey when your AI tool is acting in similar ways as a licensed activity.”
- Privacy: NAR’s March 2026 guidance cautions against entering client information, financial details, or transaction documents into unapproved consumer tools. Use only tools and data handling practices approved for the information involved.
NAR broker guidance recommends evaluating tools, requiring human review before AI output is released, establishing risk-management and enforcement plans, and publishing an AI use policy. Its standard for output is direct: “Nothing produced with AI should go out without human oversight.”
A practical way to introduce AI in a brokerage
Start with the problem, not a product. NAR contributor Troy Palmquist frames the useful question as: “What specific problem am I solving?” A brokerage can apply that question through a small, controlled workflow:
- Choose one repetitive, low-risk task. For example, test AI-generated internal meeting summaries or a first draft of a social post, rather than giving a tool authority over client communications or transaction decisions.
- Define permitted inputs. Write down what information staff may provide and what must stay out, including client, financial, or transaction information unless the tool and use have been approved.
- Set the review and release rule. Assign a person to verify facts, tone, fair-housing implications, and required disclosures before content is sent, published, or used to update a record.
- Name an accountable owner. Decide who approves the workflow, handles errors, maintains the policy, and can pause the tool if it behaves unexpectedly.
- Measure outcomes against the existing process. Track time saved, correction or error rates, and client experience. Keep the workflow only if it improves the chosen task without introducing unacceptable risk.
How to evaluate an AI tool for real estate work
Assess tools against the workflow and the brokerage’s controls rather than relying on broad claims about AI capability. NAR’s reporting supports evaluating practical concerns such as task fit, data handling, review, system compatibility, accuracy, fairness, cost, and the learning burden.
- Job to be done: Does the tool solve a defined problem, and can the result be checked by the person responsible for it?
- Customer and transaction data: What information will the tool receive, and is its handling approved for that information?
- Auditability and approval: Can staff see what the system did and review or approve an output before it is released or an action is taken?
- Workflow fit: Does it work with the brokerage’s CRM, MLS-related processes, and existing operations without creating disconnected records or duplicate work?
- Quality and safeguards: Can staff catch inaccurate claims and discriminatory language, and can the system be constrained from acting beyond its permitted role?
- Total burden: What are the full costs, setup needs, and staff learning demands relative to the time and quality gains observed in the trial?
NAR’s reporting describes technology already used alongside AI—including MLS, e-signature, showing-scheduling tools, comparative market analysis tools, drone photography and video, and CRM systems. AI should therefore be assessed as a component of an existing operations stack, not presumed to replace it. The available sources do not rank individual vendors.
What to make of claims about firm-wide adoption
A widely repeated claim that 45% of firms are experimenting with AI and 9% have enterprise-wide deployment was attributed to a Keyway and The Appraisal survey dated August 19, 2026. The underlying survey and an official methodology could not be verified from accessible primary evidence, so those figures should not be treated as established adoption statistics. NAR’s member surveys offer a clearer measure of individual use, but they do not answer how many firms have deployed AI across the enterprise.
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