On clearer priorities and making more room for relationships and business development.
“Being busy doesn't always translate into productive.”
They already used AI. The workday still felt scattered.
Freddie and Maria, two real estate agents at Keller Williams Cary, were already using AI before they worked with Whitmore. Freddie named ChatGPT and NotebookLM among the tools he had tried. Emails, texts, and documents were familiar places to start.
The harder part was organizing the work around those messages. Deciding whom to contact took attention. Property details accumulated across texts and emails. Ideas for handing work to a showing agent had been discussed, but had not become a process someone else could follow.
In their September 24 interview, the two agents described how working with Whitmore helped them bring AI into that everyday operating work. The change reached across business planning, client outreach, property tours, and the handoffs between people. Maria’s distinction between being busy and being productive runs through the whole story.
A more manageable day. More capacity for clients.
10–15 hours
Freed up over a five-day week
Calculated from Freddie’s personal estimate of 2–3 hours per day. Self-reported, not time-tracked; assumes five working days and applies to Freddie only.
<3 minutes
To create a client property hub, Freddie reported
Freddie’s example of creating a hub for a client. Self-reported, not independently timed or a guaranteed delivery time.
On keeping goals, priorities, client context, and branding in a shared business brain.
“Not having to start from scratch every time we're trying to prompt it.”
Know who to contact. Keep the conversation.
Asked where the time went before, Freddie pointed to “Not knowing who to reach out to in any given day.” Choosing the next conversation and remembering the previous one were part of the work before any outreach happened.
He described a clearer routine: daily contact priorities, a way to track whom he had already contacted, and the ability to speak a conversation summary to an AI agent that sends the notes to the CRM. He credited Whitmore with helping streamline that outreach process.
The value is in the continuity. A conversation can become a useful record while it is still fresh, and the next round of outreach can start with a clearer picture of what has already happened. Freddie linked this part of his working day to the largest improvement in his own time.
A property search with one place to land.
Freddie described creating a website for an individual client’s property search. The hub brings selected homes, maps and street views, supporting documents, and property rankings together in one place.
Clients can rank properties before and during tours. The information that previously traveled through separate texts and emails becomes something they can return to as they compare homes and decide what to see.
He reported creating a hub in less than three minutes. That is his example, rather than a promised turnaround. The more useful change is what the hub makes possible: a coherent place for each client to explore the properties and keep their preferences with the information.
A showing agent should know what good looks like.
Freddie and Maria had already talked about how they wanted showing-agent handoffs to work. AI helped them turn those ideas into materials they could use again: a written standard operating procedure, a NotebookLM training video, and a checklist.
They described a handoff prepared the night before a showing. The procedure sets expectations; the video explains the work; the checklist gives the agent something concrete to follow. The process asks for notes, completed checklist information, photos, and videos to come back through Google Drive.
That makes the handoff easier to explain and the returned work easier to assess. Maria emphasized the capacity that comes from documenting a process once and being able to repeat it. Expectations can travel with the assignment, rather than depending on another explanation from scratch.
On documenting and replicating the showing-agent process with a written procedure, training video, and checklist.
“Creating all that now has freed a lot of our hours.”
Useful ideas need a way into the working day.
Whitmore’s role was to help the agents move from experimenting with AI to implementing useful workflows around their own responsibilities. That included business context, outreach routines, and practical guidance on connecting the work to their tools.
Freddie described the value of help with making implementation more effective and being more careful about access. He also brought his own initiative to the work, building and tuning a Facebook-group lead scanner himself. Practical adoption gave him room to keep developing ideas around the business he knows.
Maria also described using AI around business receipts and email. Those smaller administrative tasks sit alongside the more visible client-facing improvements. Together, the examples show AI becoming part of the way the work is organized.
His personal estimate, chiefly from organized outreach, tracking prior contacts, and dictating CRM notes. Self-reported, not time-tracked.
“I'd say that it has freed up around two to three hours a day.”
More attention for the relationships behind the business.
Freddie’s estimate concerns his own day. Maria described a similar improvement in capacity without putting a separate number on it. Her emphasis was on clearer priorities and being able to spend more attention on business development.
For these two agents, becoming AI-native meant bringing AI into the work that connects one task to the next: the context behind a plan, the notes after a conversation, the information before a tour, and the instructions behind a handoff.
Their experience gives another business a practical place to start. Find the recurring decisions, scattered information, and repeated explanations that consume attention. Those are concrete problems to bring to an implementation conversation.