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I is reshaping real‑estate workflows, and the shift appears permanent. Even though some predict a short‑lived bubble, trade‑show halls are full of vendors touting AI for everything from marketing automation to lead generation and administrative support. The promise is simple: free agents from time‑consuming tasks so they can focus on client relationships.
Malte Kramer, CEO of Luxury Presence, insists that brokerages must adopt AI or risk falling behind in the next few years. Yet the technology’s effectiveness hinges on careful vetting. A 2025 NAR survey shows 68 % of agents use AI, but only 17 % report a major positive impact, highlighting a gap between adoption and results. Consumers are even more engaged, with 82 % using AI tools—primarily ChatGPT (67 %) and Gemini (54 %)—to research homes.
Brokerages are investing heavily. SERHANT secured $45 M in equity to expand its AI‑powered S.MPLE platform, while The Real Brokerage credits its $50 B sales volume to AI tools like Real Wallet and other productivity boosters, even swapping its CEO for an AI stand‑in on an earnings call. Many firms are hiring AI specialists and allocating budgets to future‑proof their operations.
When implemented correctly, AI delivers measurable gains. Luxury Presence’s AI marketing specialist produced 50,000 blog posts, qualified 70,000 leads, and executed 15,000 SEO tweaks in just 60 days. One client cut marketing spend by 80 %, tripled website traffic, and increased engaged leads by 50 %. Success stems from a “human‑in‑the‑loop” model: AI handles bulk tasks while experts oversee quality and compliance, achieving a 95 % approval rate for generated content.
Repetitive, dull, or risky tasks are ideal for automation. Nikki Greenberg, a real‑estate tech strategist, notes that tools like Elise AI’s call centers and chatbots have transformed property management. Other solutions—Scout’s predictive lead‑gen, Purlin’s Fair Housing‑compliant marketing suite—have earned praise, though they still rely on human oversight to avoid bias and inaccuracies.
Data synthesis is another strength. Ravi Kantha of SERHANT’s NYC team built a proprietary database that aggregates property, buyer, and market data from 150 sources. AI organizes this information with 90 % accuracy, enabling hyper‑targeted marketing. When Kantha’s team personalized AI‑generated campaigns, response rates jumped from single digits to 37 %, generating qualified luxury‑home meetings.
However, many vendors overstate AI’s capabilities. Kramer observes that most “AI tools” are merely OpenAI‑based assistants with generic prompts, offering superficial value. Ashley Stinton of NAR REACH warns that the buzzword can drown out genuine innovation. Lead‑gen and CRM add‑ons that merely inject AI copy into existing platforms produce bland, brand‑agnostic content that fails to resonate.
Authenticity matters. Real‑estate clients, especially in affluent markets, quickly spot generic AI output and often reject it. Kantha’s experience shows that spammy AI text yields only 8–9 % response rates, mostly negative. Customization—training AI on specific brand voices and client data—is essential. Kramer stresses that a generic prompt yields generic answers; only tailored training ensures leads hear the agent’s authentic tone.
Transparency is critical. Thorne cautions that misrepresenting AI as human can damage reputations and careers. If clients discover they were interacting with a bot, trust erodes. The industry must balance automation with genuine human connection, which remains irreplaceable. Kramer notes that community events and personal relationships are core to a broker’s success—AI cannot replicate that trust.
NAR identifies 179 tasks in a transaction; ChatGPT can handle about 110 (60 %). The remaining 40 % still require human judgment, especially for high‑stakes negotiations and emotional support. AI should handle “$20 an hour” chores, freeing agents to focus on “$300 an hour” client interactions.
Before adopting AI, brokerages should follow a four‑step vetting process: assess vendor talent and engineering depth; request clear explanations of underlying algorithms beyond ChatGPT prompts; review data privacy and compliance measures; and conduct case studies or pilots. Peer validation is valuable; NAR REACH highlights companies like Scout and Perlin that train on industry‑specific data to meet Fair Housing standards.
Legal and ethical risks persist. AI hallucinations—confidently incorrect statements—can lead to Fair Housing violations or misleading property representations, especially in AI photo staging. Kramer advises treating AI like an employee: it can generate work, but humans must verify accuracy and compliance.
Investment decisions should align with business size and goals. Kantha’s team spends $125 k on a data platform, justified by high ROI. Most agents should start small, leveraging tools like ChatGPT for quick wins, then scale to more complex projects. Thorne notes that AI can accelerate progress from zero to 80 % rapidly, but the final 20 % requires human oversight to ensure factual accuracy and brand alignment.
Looking ahead, AI is poised to make real‑estate operations far more efficient, granting agents more time for family and reducing stress. Kramer predicts that correctly deployed AI will dramatically improve productivity. Thorne envisions autonomous “AI agents”—virtual employees trained to perform specific tasks flawlessly—freeing agents to concentrate on relationships and sales.
Adoption will be gradual, reflecting real‑estate’s historically slow tech uptake. The key is to view AI as a teammate, not a replacement, and to maintain ongoing training and oversight. Agents who ignore AI risk losing competitiveness, while those who let it replace client interaction risk alienating their base. The challenge lies in balancing automation with authentic human service.
