Stop asking AI to tell you what your clients care about.
Give it what your clients already told you.
I use AI constantly, and I still think this is one of the easiest ways to make marketing worse. You open a chatbot and type, “What are the top five problems homebuyers are dealing with right now?”
The answer will sound reasonable.
It may even be accurate at a broad level.
It still has no idea what the buyer sitting in your car said after the showing yesterday.
Your client language is source material
In a class, I asked a room of real estate professionals how many of them record their client conversations.
Very few hands went up.
That surprised me because those conversations contain the exact information people keep trying to manufacture later.
A seller might tell you she is nervous about showings between two and four because her baby naps then.
A buyer might say he expected $300,000 to buy a completely different kind of house.
A relocation client might care deeply about whether she can keep the same grocery routine after moving.
A seller might be irritated that all of the work put into a house does not translate into the sales price she expected.
Those are client problems.
I would rather start with one of those sentences than a demographic profile that says a hypothetical buyer values “convenience” and “peace of mind.” The sentence gives me something I can respond to.
They have language attached to them. They have context. They have emotion. They usually contain a question you can answer.
When I ask AI to generate “buyer pain points,” it has to predict a useful answer from patterns in its training and available context. When I give it a transcript, it can work from the problem a real buyer already described.
Google’s current Search guidance reinforces the value of that source material. Its people-first content guidance asks whether content contains original information, reporting, research, or analysis. Its 2026 generative AI search guide specifically calls for unique, non-commodity content. (Google Search Central, Google’s 2026 generative AI guidance)
Your client’s exact concern is much closer to non-commodity information than a generic list of “five buyer challenges.”
Give AI a better job
I like AI as an organizer, editor, researcher, and pattern finder.
After a showing, I can record what happened and ask AI to help me identify the strongest idea.
After a coaching call, I can ask which question deserves a full article.
After a month of conversations, I can ask what themes are repeating.
For a blog post, I can use the transcript as the starting source and then ask AI to identify factual claims that need external research. I can verify those claims, add links, and develop the article without losing the original point.
That is a completely different job from “tell me what I should think.”
I also like asking AI questions that force it to stay close to my material:
What did the client say they were worried about?
Which part of my explanation answered that worry?
What assumption did I make that needs verification?
What part of this story is useful without exposing private client information?
What related question would a reader reasonably ask next?
Those prompts keep the work anchored.
They also make it easier to anonymize case studies responsibly because you can separate the useful decision from the identifying details.
Generic input creates predictable output
There is growing research behind the concern that AI-assisted language can start to converge.
A 2026 study published in Nature Human Behaviour analyzed more than 880,000 texts and found that LLM writing assistance was associated with lower linguistic diversity. The researchers reported that rewriting could preserve meaning while narrowing variation in writing style. (Nature Human Behaviour)
Another 2026 paper examining creative outputs across several years of large language models found evidence that output diversity was decreasing over time, though that study is a preprint and should be treated as preliminary. (arXiv)
That does not mean AI makes everything generic automatically.
It means the input and the workflow matter.
If fifty real estate professionals ask the same model the same broad question, they should expect overlap.
If one of those professionals gives the model transcripts from a week of real buyer and seller conversations in a specific market, the starting material is already different.
That is the workflow I want.
This does require basic privacy judgment. Get the permissions required for recording where you live and work, protect confidential information, and anonymize examples before publishing them.
The next time you are tempted to ask AI what your clients care about, search your own records first.
Look at your texts.
Look at your consultation notes.
Listen to the voice memo from the car.
Pull the transcript.
Your clients have probably already answered the question.
Related reading: How to Build an AI Business Brain for Your Real Estate Business; How to Find the Gap Between What You Know and What You Post.
