How AI Is Being Used in Real Estate by Brokerages Today

For real estate brokerages, artificial intelligence has moved past the pilot stage and become the operating system they work from. Delta Media Group's Real Estate AI & Leadership Survey traces that three-year shift: in 2024, leaders were still working out AI's role inside their brokerages, and in 2026, 97% report agent use, with nearly all holdouts planning to adopt it. Since 2024, brokerage executives have rated AI as increasingly vital, and their 12-to-18-month outlook pushes that rating higher still, signaling the direction more clearly than any one-year snapshot could. The words people use in the field point to the same change: as Delta CEO Michael Minard describes AI, it is “quickly moving from something brokerages use to something they are beginning to build their businesses around”. It captures the transition at the center of this piece, from occasional agent experimentation to a core system shaping the brokerage. Small brokerages are the one place where uptake still trails the broader industry.

Marketing and content creation as the entry point for AI in brokerages

Every infrastructure build needs a cheap, visible place to start, and marketing was that place for brokerages. Content creation paid back quickly and demanded no changes to a brokerage's day-to-day operations, making it the natural first step. The 2026 Delta survey identified blog content, email campaigns, listing descriptions, and social media posts as the applications brokerages turn to most often. The RPR 2026 AI adoption survey shows the same pattern among individual agents, who point to drafting listing descriptions, producing social posts, and composing emails or newsletters as the top three tasks where AI has lent a hand. A year earlier, in 2024, the skew was sharper still: Delta reported that close to three-quarters of the AI initiatives brokerage leaders planned sat in marketing and social channels, leaving scant room for anything else. That early concentration plants a strain that later parts of this piece revisit directly. Producing content is voluminous work, yet it carries little leverage for closing deals, which meant the headline adoption figures were bound to run ahead of the business-impact figures that came later.

The second wave: AI moving into CRM, back-office, recruiting, and training

Since then, brokerages have taken AI beyond marketing and embedded it in the everyday operating systems of the business. CRM, workflow automation, back-office work, talent acquisition, and coaching agents have shifted from trials to active growth. Delta's 2026 survey found that a majority of brokerage executives expect to broaden AI use to improve CRM and automate workflows, handle admin and back-office tasks, plus recruit agents, train them, and coach performance. Administrative use makes the acceleration clearest: fewer than a quarter of leaders in 2024 anticipated front-office or administrative support applications for AI, yet by 2026 the share was over twice as high, marking two years of intentional movement toward making AI central to operations, not off to the side. The top planned area for growth is CRM improvement, where 55% of brokerages' leaders intend to add AI for workflow automation, indicating that cultivating leads and managing pipelines, not generating content, is where they see the next major productivity advance. Recruitment and performance coaching mark another outward step, as AI moves past client-facing work into overseeing and developing the brokerages' own agents. A parallel consolidation trend is also emerging: the share of brokerage leaders favoring integrated marketing platforms that combine AI and automation has climbed to a three-year high in the Delta series, suggesting brokerages are opting for fewer, more comprehensive partners rather than assembling individual point tools.

Agentic AI: the qualitative break from content generation to task execution

Agentic AI stands apart from the technologies discussed earlier: its purpose is to get things done, not merely generate text. Given a direction, it can move through the required steps independently instead of needing someone to supply prompts and review the result. For the first time across its three annual editions, the 2026 Delta survey included questions on agentic AI, underscoring its recent entry into brokerage discussions. Already, half of brokerage leaders intend to bring in or scale up tools for agentic AI in 2026, placing it among immediate priorities, not something to revisit later. Generative tools require a person to initiate and assess the output; agentic tools carry out a sequence of steps and deliver a completed outcome. This distinction alters the placement and focus of human supervision. The PwC and Urban Land Institute's Emerging Trends, Real Estate 2026 report frames this divide as a market moving at two speeds: generative AI has gone mainstream, while agentic AI is just beginning to achieve wider adoption. The next section's example sits right in that space between the two speeds.

Compass's agentic AI assistant for agent workflows

Compass's AI Assistant shows what happens when a major brokerage actually builds the shift from text-generating systems to action-taking ones. Rather than merely producing copy, the system was built to execute meaningful work on behalf of agents. In July 2026, Compass International Holdings embedded AI Assistant within its Home Platform to let agents accomplish work through conversational commands, arriving mere weeks after that platform debuted. By tapping into an agent's roster, deal flow, promotional efforts, and to-dos, it can refresh buyer files, compose outreach messages, set up property alerts, book reminders, and oversee closings. Beyond simply carrying out assignments, the tool provides tailored morning summaries and highlights "Likely to Sell" prospects using each agent's personal information, pivoting away from passive chore handling in favor of forward-looking deal insight. Compass first designed its AI tool chiefly to help create marketing copy and similar materials, but AI Assistant takes a more ambitious turn toward the role of a real aide, with voice features that let agents dictate notes on the road between appointments. Compass put equal weight on adoption and development, training corporate staff in regional offices to coach agents on AI Assistant because it sees the hardest part as persuading them to make tools like this part of their work. That training has significance outside Compass's launch. It points to a broader adoption challenge the wider industry has yet to solve.

AI-powered valuation and market intelligence as back-office infrastructure

When it comes to pricing support, AI has turned into ordinary back-office kit rather than a novel competitive edge. What brokerages now need are pricing tools that deliver reliable figures quickly and in volume. Automated valuation models working alongside market intelligence driven by AI answer that demand. Take Zillow's Zestimate: a neural network underpins it, learning from MLS listings, tax records, public filings, and data users submit, and the 2026 accuracy numbers Zillow currently claims cover homes both on and off the market. Nearly half of brokerage leaders expect to broaden AI's role in analyzing markets and valuing properties, and about as many anticipate extending it to forecasting, business intelligence, and analytics for prediction, showing that valuation tools are shifting from a consumer feature on listing sites to a foundation for brokerage planning decisions. Summarizing documents and reviewing contracts fall within one growth area, pushing AI's administrative utility past pricing to shape how deals operate. The point here is not about picking the best valuation product. As valuation and analytics become baseline infrastructure for any brokerage, a direct question follows: if all firms access comparable tools, what truly drives competitive advantage?

AI and competitive dynamics among large brokerages, mid-sized firms, and independents

According to Propmodo, the outcome is more complicated than either the consolidation or democratization thesis expected on its own. Each one got part of it right. By 2026, mid-tier brokerages eliminated the technology lag that once characterized their segment, catching up entirely to industry giants. Among the largest commercial players, size still purchases advantages beyond the reach of smaller rivals: CBRE has pledged in excess of a billion dollars toward computing infrastructure, while JLL channeled hundreds of millions into numerous proptech ventures via JLL Spark, its initiative launched in 2017 and financed continuously from June 2018. Cushman & Wakefield demonstrates that heavy investment alone doesn't dictate success, combining a Microsoft collaboration focused on AI tools alongside internally developed technology featuring a custom large language model. The smallest brokerages are the ones truly slipping, those fielding 10 agents or under showing the steepest refusal to adopt across the sector, and Delta's analysis pins the last holdout of genuine AI non-users almost entirely in that group. Still, size is not the only dividing line that matters now. Propmodo's coverage, citing ViewEO, shows the Brooklyn commercial outfit Terra CRG beating out the five biggest firms on AI recommendations in that market, the edge coming from the fact that everything it puts out in public funnels toward one clear answer: who really has Brooklyn commercial real estate expertise. Tim Rodland, who started Rodland Real Estate, frames the trade-off independents have carried for years as picking between holding their size or attaching to a bigger organization, and casts RoRo as the backbone designed to help independents grow without taking on a franchise banner. All together, the field is taking on a barbell form, with the heavy hitters at one pole stacking up advantages from their own data while niche shops at the other pull ahead in AI-driven recommendations, leaving the middling generalists pinched between them from both sides.

The gap between AI adoption and business impact for agents

So far, these systems have not produced widespread, trackable gains for the agents who use them, and the gap needs a direct explanation, not a dismissal. Agents have mainly put these tools to work on frequent chores that do little to change outcomes. That explains the widening divide between how many agents use these tools and how much business value they see. The 2025 Technology Survey from NAR showed that just 17% of agents said AI has meaningfully helped their business, while nearly half said they have seen little or no difference. Speeding up a listing description does not make the sale happen sooner. Agents cite concrete, fixable obstacles: about two-thirds call the learning curve their top hurdle, with cost coming next. Compass tackles this head-on by preparing regional office staff to show agents how its AI tool works, counting that instruction as a legitimate expense instead of assuming agents would pick it up alone. The infrastructure argument does not hinge on each agent already drawing maximum benefit from AI at this moment. What matters is that brokerages are constructing the underlying systems, treating today's limited impact as a shortfall in helping agents adopt the tools rather than proof of the technology's limits.

The compliance and fair housing risks that governance has not yet caught up to

Systems like this draw on relationship records, deal pipelines, pricing inputs, and agent conduct to suggest or initiate next steps automatically, creating legal risk that many brokerage oversight programs still have not resolved. Fair housing rules bar discriminatory treatment in presenting, promoting, or steering homes to buyers and renters, so a tool that writes listing copy, selects ad audiences, or shapes valuation estimates operates within that regulatory zone even if the brokerage never intended that use. A valuation engine built from past transaction data may carry forward the same distortions found there, including patterns left by biased mortgage and appraisal systems, leaving any brokerage that uses it for pricing guidance exposed to bias in the training set. Agentic tools create a similar risk: if software prepares follow-up messages, books showings, or prioritizes leads, the brokerage remains accountable for every step it takes, even if the tool acted without an agent involved. Both the mid-sized and major brokerages covered here have invested heavily in rolling out AI and teaching agents how to leverage it. Yet very few have established comparable systems to review those algorithmic suggestions, their recipients, or the underlying rationale. This gap between rapid rollout and lagging oversight represents the gravest open threat facing brokerage AI right now.