AI is no longer just an internal productivity tool for brokerages. In 2026, buyers, sellers, and renters are using AI platforms to research neighborhoods, compare properties, estimate costs, and prepare questions before they contact an agent.
A March Barron’s report cited a survey of 1,000 US adults that found 82% had used AI platforms for real estate insights. That shift raises the stakes for agents: AI is changing how consumers prepare for a transaction, even as many real estate professionals are still figuring out how to make the tools pay off in their own businesses.
The latest agent-side benchmark still shows a gap between use and measurable impact. In the National Association of Realtors’ 2025 Technology Survey, 46% of surveyed NAR members said they use AI-generated content, including listing descriptions. AI use is also regular: 20% use the tools daily, 22% weekly, and 27% a few times a month.
The payoff is less settled. Nearly half said AI had no noticeable impact on their business, while 17% reported a significantly positive impact.
The gap is between use and impact
The survey findings do not suggest agents should pull back from AI. They suggest agents and brokerages need to be more specific about where AI belongs in the business.
Most agent use remains concentrated in low-risk, content-heavy work. ChatGPT was the most-used AI tool among surveyed members, followed by Gemini and Copilot. Listing descriptions, social posts, email drafts, newsletter ideas, and follow-up templates are natural starting points because they are easy to review and useful for agents who need to move faster.
That work has limits. Faster marketing copy does not automatically improve response time, CRM follow-up, pricing strategy, or client retention.
Firms are facing the same ROI problem
The same pattern is showing up at the firm level. The 2025 Global Real Estate Technology Survey found that 88% of investors, owners, and landlords have started AI pilots, and 92% of occupiers are running corporate real estate AI pilots. Yet only 5% of CRE occupiers said they had achieved all their program goals.
The same problems can slow residential brokerages, especially when client records, lead activity, transaction notes, and listing data sit in separate systems. Without cleaner inputs and agreed-upon workflows, AI stays limited to tasks agents can easily check on their own.
Where AI can help agents right now
The best use cases are the ones agents already know they should do but struggle to do consistently: responding to new leads quickly, following up after showings, turning listing prep into a repeatable process, and keeping past clients warm.
For example, one agent might ask ChatGPT for a listing description from a few property details. Another might use a repeatable template with MLS-verified facts, seller-approved features, brokerage style rules, and a final compliance review.
That setup takes longer upfront, but it is easier to train, review, and repeat. It also helps teams avoid relying on each agent’s individual prompting habits.
Where human review still has to stay
The problem is not that AI is too risky for real estate. The risk rises when agents use AI for claims that need verification.
Pricing, market interpretation, client advice, legal compliance, and negotiation strategy carry higher stakes than a social caption or newsletter draft. AI-generated work can introduce inaccurate property details, unsupported market claims, stale data, or language that raises fair housing concerns.
Client-facing AI output should be checked against MLS data, brokerage policy, and any applicable legal or compliance guidance before it reaches a buyer or seller.
What brokerages should do next
Brokerages may get more from training than from adding another AI platform. Training should be task-specific: listing descriptions, follow-up sequences, CMA prep, showing-feedback summaries, and transaction timelines.
Leaders should also define which uses are approved, which need manager review, and which require verified data before reaching a client. The real AI divide in real estate may not be between users and nonusers. It may be between agents who treat AI as a writing shortcut and teams that build it into the work they already need to do more consistently.