Which AI is great for a real estate agent?

The Agent's AI Dilemma: Hype vs. Reality

Here's a paradox that should stop every real estate agent in their tracks: the industry's most popular AI tool is used by most agents, yet nearly half of them say it hasn't changed their business at all.

According to NAR's 2025 Technology Survey, ChatGPT leads agent adoption at 58% — nearly triple Gemini's 20% and well ahead of Copilot's 15%. On the surface, that looks like an industry that has embraced AI.

But dig into the same survey and the picture gets murkier. While 50% of agents report positive impact from AI, 46% report no noticeable business impact whatsoever. And 32% of agents have never used AI at all. Even among those who do, only 20% use it daily — 27% touch it just a few times a month.

So what explains the gap between adoption and results? The answer lies in how agents are actually using these tools.

• Only 46% of agents use AI-generated content at all, with listing descriptions as the flagship use case.
• Most usage is one-off — drafting a single email or description, then moving on.
• Few agents connect AI to repeatable workflows like lead follow-up or client communication.
• Meanwhile, PwC/ULI research shows the most advanced operators are automating entire processes — tours, leasing, resident services.

That last point matters. PwC/ULI's Emerging Trends report found that young renters "would rather deal with a good app than a person" — a signal that AI-driven communication is becoming a competitive expectation, not a novelty. Agents who treat AI as an occasional writing helper are missing where the industry is actually heading.

The core question for this article, then, isn't simply "which AI is great for a real estate agent?" It's which tool — and which way of using it — actually moves the needle on leads, listings, and client relationships. ChatGPT's dominance makes it the evidence-backed starting point, but usage statistics alone don't tell you whether it's generating clients or just generating copy.

Part of the problem is that agents have no reliable way to know what works. There's little independent data comparing AI tools for real estate tasks, and much of what exists comes from vendors selling their own platforms. That's a gap SkillzBench is working to close: it's building a free directory of AI agent skills with benchmarked results showing how much each one actually improves performance, so professionals can pick tools based on measured lift rather than hype.

The agents seeing real returns aren't the ones using AI most often — they're the ones using it most deliberately. The rest of this article breaks down where each major tool fits, and where the genuine leverage lies.

The Evidence: ChatGPT Leads, Gemini and Copilot Follow

When agents name their AI of choice, the numbers point one way. According to the NAR 2025 Technology Survey, 58% of real estate agents use ChatGPT — nearly triple the share using Gemini (20%) or Copilot (15%). That makes ChatGPT the closest thing the industry has to an evidence-backed default.

Adoption alone doesn't tell the whole story, but the flagship use case reinforces it. The most common agent-facing application is AI-generated content, used by 46% of agents, with listing descriptions cited as the key example. It's a natural fit: describing a property's light, layout, and neighborhood is exactly the kind of writing task general-purpose models handle well, and it's a task every agent repeats constantly.

The three tools' usage breakdown looks like this:

• ChatGPT — 58% of agents, the clear market leader
• Gemini — 20% of agents, a credible second choice
• Copilot — 15% of agents, meaningful but distant

Beyond usage share, the broader context matters. The same survey found only 20% of agents use AI daily and 32% have never used it at all, while 46% report no noticeable business impact. In other words, most agents are dabbling rather than building AI into their daily workflow — which means the gap between leaders and laggards is still wide open.

Here's an honest caveat worth stating plainly: no head-to-head benchmarks exist for how ChatGPT, Gemini, or Copilot perform on agent lead generation or client communication specifically. The available comparative data mostly covers document-processing platforms aimed at commercial operators, not individual agents. So while ChatGPT's adoption lead is well documented, treat tool choice for lead-gen and follow-up as a matter of testing in your own workflow rather than trusting any ranking.

What the evidence does support is the pattern of use. PwC/ULI research shows AI transforming rather than replacing agent work, with generative AI handling content creation, chatbots, and routine paperwork while agentic AI takes on predictive analytics and recommendations. Notably, young renters increasingly prefer app-based interactions over dealing with a person — a signal that automated communication is becoming table stakes for parts of the market.

For agents ready to go beyond casual prompting, agent skills — instruction packages that plug into AI agents — can automate listing descriptions, follow-ups, and the rest of the workflow. SkillzBench is building a free directory that benchmarks and security-scans these skills so you can see which ones actually lift performance, and it will offer a free MCP connector to wire the skills you choose into your agent.

Where AI Wins for Agents: Listings, Communication, and Paperwork

Ask any top-producing agent where AI actually earns its keep, and the answer rarely involves robots replacing them. It involves three unglamorous tasks: writing listing copy, answering routine questions, and pushing paperwork through the pipeline. That's where the tools shine — and where agents see measurable time savings.

Listing descriptions are the proven entry point. Nearly half of agents — 46% — already use AI-generated content, with listing descriptions cited as the flagship application, according to NAR's 2025 Technology Survey. A well-prompted model can turn a property's features into three distinct listing variations in minutes, freeing the agent to focus on showings and negotiation.

Client communication is the second high-value zone, and the demographics are shifting fast. A PwC/ULI industry report found that young renters would rather deal with a good app than a person — a preference that's pushing the most advanced operators toward fully AI-enabled leasing, automated tours, and chatbot-driven resident services. Agents serving younger buyers and renters should read that as a clear signal about response-time expectations.

Paperwork is where the scale gets dramatic. Transaction automation platform Rexera, built on Amazon Bedrock with a multi-model approach combining Meta's Llama and Anthropic's Claude, now processes 5 million pages and facilitates more than 5,000 transactions monthly. Its vendor-reported results include a 99% reduction in manual document review, 4 hours saved per transaction, and accuracy above 95% — though these are self-reported metrics from the company's own case study and should be treated accordingly.

The pattern across all three use cases is consistent: AI handles the repetitive layer, humans handle the trust layer. NAR puts it explicitly — technology should "enhance, not replace" the trusted human expertise at the heart of every transaction. Clients agree: 82% report positive or very positive responses to technology in their transactions.

For agents ready to go deeper than copy-paste prompting, purpose-built agent skills can automate listing descriptions, follow-up sequences, and document workflows end to end. SkillzBench is building a free directory that benchmarks and security-scans those skills, plus a free MCP connector to wire them into the AI agent you already use — worth watching if you want your AI stack to do more than draft paragraphs.

The takeaway: start with listings, add chatbot-assisted communication for younger clients, and explore document automation as your volume grows. The agent stays in the relationship; the AI stays in the busywork.

Your 4-Step Implementation Playbook

You don't need a month-long AI strategy to get value from AI — you need a sequence. The agents seeing results started small, proved the value, and expanded deliberately, and you can do the same with four steps.

Step 1: Start with listing descriptions. This is the most proven entry point in real estate: 46% of agents already use AI-generated content, with listing descriptions as the flagship application. Draft with ChatGPT (the tool 58% of agents use), then edit for accuracy and local flavor. Expect to spend an afternoon learning prompts, not weeks.

Step 2: Add AI-assisted follow-ups for younger clients. The preference shift is real — PwC/ULI's Emerging Trends report found that young renters "would rather deal with a good app than a person." For prospects in that demographic, AI-drafted messages and chatbot-style responsiveness can be a differentiator. Keep it for routine updates and scheduling, not negotiation.

Step 3: Keep a human in the loop. NAR's position is explicit: AI should "enhance, not replace" the trusted human expertise at the heart of every transaction, and its AI guidance addresses data bias, privacy, and unclear rules. The client side supports this too — 82% of agents report positive or very positive client responses to technology in transactions, but that goodwill depends on you reviewing everything before it reaches a client. Never send AI output unchecked.

Step 4: Set a realistic budget. Most agents don't need enterprise spend. NAR's survey shows the largest group of agents — 34% — spends $50–$250 per month on technology, which comfortably covers a general AI assistant plus one or two specialized tools. Anything beyond that should earn its place with measurable results.

Once the basics are running, agent skills can automate the rest of the workflow — listing drafts, follow-up sequences, document handling — chained together through an agent. The catch: published research found roughly 26% of agent skills carry at least one vulnerability, so vetting matters. That's the problem SkillzBench is building for: a free directory of skills with security scanning, version pinning, and sandbox benchmarking that shows each skill's measured Lift, plus a free MCP connector to load your chosen skills into any agent. It's in pre-launch now — join the waitlist to be first in when it opens.

• Week 1: Draft and refine your first AI-written listing descriptions.
• Week 2–3: Template your client follow-up messages and test response rates.
• Week 4: Review what worked, set your monthly budget, and explore vetted agent skills.

The agents reporting real business impact — the 50% who see positive results — aren't using fancier tools than everyone else. They followed a sequence like this one and kept the human relationship at the center.