Four in ten agents have caught an AI error before a client saw it
41% of agents have caught a factual error in AI-generated material before it reached a client, and 5% have had one get through (Inman/Lofty AI survey). Those two numbers belong together. The first says the tools are wrong often. The second says the checking mostly works. The question for you is what happens in the month you are too busy to check.
The errors are routine, and everyone expects them
This is not a fringe worry. 49% of agents expect AI to frequently produce factual errors (Inman, brokerage AI adoption), and only 6% have high confidence that AI-written text is accurate (Inman AI power users). Agents are using the tools anyway: 62% pay for a general AI assistant (Inman/Lofty). The industry has settled into a position of use it, but do not trust it.
That is reasonable for a listing description you will read twice before posting. It is less reasonable for a monthly content habit, where the whole point is volume and the whole risk is the piece you did not read twice. 52% of agents say AI made their content better or more consistent (Inman/Lofty). Consistent is the operative word. Consistent output needs consistent checking, and checking is the part that falls off first when the week gets long.
Three kinds of error, ranked by what they cost you
The first kind is harmless. A clumsy sentence, a wrong season, a stock phrase you would never say. These are embarrassing and nothing more. Fix them when you see them, and do not lose sleep over the ones you miss.
The second kind is a wrong number. A general AI assistant does not know last month's median sale price in your ZIP. Ask it, and it will give you a confident figure from somewhere, or from nowhere. Put that figure in a video with your face on it and you have made a public claim about the local market that you cannot source. For an agent trying to be the local authority, that is the worst error available, and it is the most common one general tools produce. Market update videos only work if the data is licensed and local, not generated.
The third kind is a compliance error. 17% of brokerage leaders reported a disclosure, fair housing or MLS compliance issue arising from AI material, and 19% issued new guidance or discipline because of it (Inman/Lofty). This one deserves its own section. If you run a brokerage, that line item is yours, and there is a retention case for fixing it.
Why Fair Housing is the error you cannot afford
General AI tools are trained on an enormous amount of real estate marketing copy, and a great deal of that copy describes neighborhoods and buyers in ways that have been illegal for decades. The tool does not know that. It knows what sounds like a listing description. Ask it to write warm, inviting copy about a home or an area and it will reach for phrases that describe who lives there or who the home is for, rather than what the property is.
The pattern is what matters. Anything that characterizes a neighborhood by the people in it, or frames a property as suited to a particular kind of household, is a risk, regardless of how friendly it sounds. The safe description talks about the property: square footage, condition, features, distance to specific places, the numbers on the sale. It does not talk about who belongs there. If a sentence would need you to picture a person to make sense, cut it.
This is a pattern a tool can be built to screen for, and a general chat assistant is not built for it. A one-line prompt reminder helps and does not fix it. The fix is a review step, by a person or by a purpose-built screen, before anything renders with your name on it. The agent's guide to AI real estate video marketing goes through what that review should cover.
A checking routine you will keep
Three checks, in order. First, every number. If you cannot name the source for a figure, delete it or replace it with a figure you pulled yourself. Second, every description of a place or a person. Read it for the pattern above, not for individual words. Third, every claim about you. AI tools will cheerfully invent an award, a years-in-business figure or a specialty. Make sure the bio matches reality.
The honest problem is time. You already spend an hour a day on marketing, and a review step adds to it. So decide now what you will do in the week you cannot review. The options are to not publish, to publish unreviewed, or to use something that reviews for you. The middle option is how the 5% got there.
There is also a trust point. 22% of agents have declined to use AI for client-facing work over trust, and 29% have held back from entering client information over privacy (Inman/Lofty). Both are sensible instincts. Keep client data out of general tools entirely. For public content, the question is not whether to use AI but who is responsible when it is wrong. If the answer is only you, build the review into the calendar, not into your good intentions.
Where MirriFi fits
Every market figure in a MirriFi video comes from licensed local data, every script passes a Fair Housing screen before it renders, and if anything in a finished video is wrong, MirriFi fixes it. That is the review step done for you, in your face and voice, from one recording session of about fifteen minutes. If you are producing your own content and have a checking routine that holds, keep it. If you want to see the output before judging the process, see it on your own face, no card, no call.