AI for Real Estate Brokers: Key Tools and Lessons from the REACH Carolinas Summit

The Data Risk Nobody Talks About Enough

Jerimiah Taylor, co-founder and CEO of BrokerBot and a 2026 NAR Tech Innovator of the Year, opened with a warning that landed hard in the room.

The core problem: when agents paste documents, client data, or internal policies into consumer AI platforms, those models can retain that information and potentially train on it. Future users of the same platform could, in theory, receive outputs that include your brokerage’s internal policies, client details, or association intellectual property.

That’s not a hypothetical. It’s a structural feature of how many large language models work.

BrokerBot’s approach to this is direct:

• It does not retain user data
• It distributes queries across multiple LLMs rather than relying on one
• It prevents those models from training on user inputs

This is the kind of architectural decision that matters for brokers who handle sensitive transaction data, client financials, and proprietary workflows.

NAR has also responded to this gap. In March, the association published two customizable AI policy templates to help brokers and associations establish responsible, ethical, and secure guidelines for AI use.

Where AI Is Actually Solving Broker Problems

Beyond data risk, the summit highlighted three practical use cases where AI tools are being deployed right now.

The Rollout Lesson Brokers Keep Learning the Hard Way

Several brokers at the summit shared stories of expensive tech rollouts that went sideways—not because the tools failed, but because the communication around them did.

Eddie Wilder, broker-in-charge at ERA Wilder Realty, described a moment when an employee assumed a new tech investment meant his job was at risk. His son’s response reframed the entire situation: “We’re not looking to get rid of you. We’re looking at 10x-ing you.”

That single conversation changed the team’s dynamic.

The lesson isn’t complicated. When you introduce AI tools without explaining the intent, people fill the silence with fear. Brokers who communicate clearly—this is about capacity, not replacement—tend to get faster adoption and less resistance.

What the REACH Program Signals for the Industry

BrokerBot, MaxHome, and PropMedia are all part of NAR’s REACH program, run by Second Century Ventures. The fact that these tools were presented at a broker summit—not a tech conference—is worth noting.

NAR has participated in 38 in-person broker gatherings so far this year, with 70 planned by year-end. The Carolinas summit was part of a deliberate push to bring AI conversations directly to broker-owners rather than waiting for them to seek it out.

That’s a meaningful shift. It suggests the industry is moving from passive awareness of AI to active, structured adoption—with guardrails.

The Practical Takeaway for Brokers

Your exposure is probably larger than you think. Agents use AI with client data, internal documents, or proprietary policies are creating risk you haven’t accounted for.

The right AI tools are built with data architecture in mind. Before adopting any AI platform, ask directly: does this model train on my data? Does it retain inputs? Who else could access what my team uploads?

Start with low-risk, high-value use cases. After-hours support, document compliance checks, and photo disclosure are all areas where AI adds clear value without requiring you to hand over sensitive judgment calls.

Communication is part of the implementation. The technology is only half the rollout. How you explain it to your team determines whether it sticks.

The brokers who walked out of Myrtle Beach with the most useful perspective weren’t the ones who got excited about AI. They were the ones who got specific about where it fits—and where it doesn’t.