The short version
- People are curious and a little wary of AI. A small company that explains itself plainly stands out.
- Say what your model does, what it can't do and who checks it. Honest limits build more trust than big claims.
- Show one real task from start to finish, with a person in the loop.
- 3 posts a week is enough: one explainer, one real example, one person.
A neighbor asks what your company does and you say "machine learning." Their eyes glaze over. Your Facebook Page can do better, and these AI company social media post ideas show how.
The trick is to describe a job, not a technology. Here are 16 posts for a small team that wants customers, job seekers and locals to understand it and trust it.
What AI company social media post ideas should do
- Describe the job your model does in one plain sentence.
- Name its limits and who reviews the results.
- Show a real task with real inputs and a person checking the output.
- Skip words like "intelligent" and "transformative." Say what it saves.
Many readers are curious about how a small team uses machine learning, which is also what our AI for social media posts guide explains. Data-focused shops can look at data analytics post ideas.
Say what your model does in plain words
A short, concrete post beats a clever one. Pick a job your customer already understands.
One job, one sentence
What we build, in one sentence: software that reads [type of document] and pulls out [the three things] your team used to type by hand. A person still checks it. That's the whole idea.
Photo: A stack of paper forms next to a laptop.
Before we built it
Before our model, [customer type] spent about [number] hours a week on [task]. We watched them do it for a day. Then we built something to take the dull part.
Photo: A desk covered in spreadsheets, taken with permission.
Words we don't use
We try not to use big buzzwords. We say what the model does, how often it's right and when a human has to step in. Ask us for the plain version.
Photo: A whiteboard with crossed-out buzzwords.
Ask us what it can do
Send us a task you do over and over. We'll tell you honestly whether a model can help or whether a spreadsheet would be better. No charge for the answer.
Photo: A team member with a notepad, listening.
Be honest about limits
Where it gets it wrong
Our model is good at [task] and still stumbles on [situation]. When it isn't sure, it flags the item for a person. We'd rather show you the misses than hide them.
Photo: A screen with one flagged item highlighted.
Who checks the answers
Every result our model produces for [customer type] gets a human review before it goes out. [Name] on our team does it each morning with a coffee. It takes minutes, and it's worth it.
Photo: A team member reviewing a list on screen.
Where your data goes
We get this question a lot: where does my data go? It stays in [place], isn't shared and is deleted on [rule]. If you want the details in writing, message us and we'll send them.
Photo: A plain text graphic or a filing cabinet.
What we say no to
We turned down a project this month because a model wasn't the right answer. A simple fix was better, so we said so. Sometimes the honest answer is the useful one.
Photo: The team in discussion at a table.
Show a real task from start to finish
Walk-through
Here's a real example, start to finish: [input], then [step], then [result], checked by [name]. Total time: [number] minutes instead of [number]. Questions in the comments.
Photo: Three screenshots in a row, details hidden.
A customer's story
[Business] in [town] uses our model to [one job]. Their words: "[short quote]." Thanks for letting us share it, [first name].
Photo: A photo from the customer, with approval.
What we learned this month
This month we learned our model was confused by [odd situation]. We added [fix]. It's a small change, but it fixed a stubborn error. We keep a list of these on the wall.
Photo: The wall list of lessons.
Training data, in plain words
People ask how our model learned. Short answer: from thousands of examples that our team labeled by hand, with the customer's permission. Long answer is a good coffee chat.
Photo: A team member labeling data on a screen.
The people and the place
Meet the engineer
Meet [name], who builds our models. [He/She/They] used to be a [previous job] and still explains things like one. Ask [name] anything in the comments.
Photo: A relaxed photo at a desk.
Talk at the university
We spent Wednesday at [school] talking to students about what machine learning does in real businesses. The questions were sharper than ours.
Photo: A slide on a projector in a classroom.
We're hiring
We're hiring an engineer in [town] or remote. You'd work on [plain description] with a team of [number]. Tell us about a project you finished, even a small one.
Photo: A desk with a laptop and a notebook.
Local coffee thanks
Thanks to [cafe] for the table by the window. Half of our best ideas were written on your napkins.
Photo: A napkin sketch beside a coffee.
A simple week of AI team posts
3 posts a week
- TuePlain explainerOne job, one sentence.
- ThuReal exampleA task from start to finish.
- SatPerson or placeAn engineer, a talk or a hire.
If the writing feels stiff, how to make AI posts sound human has fixes that work for any writer.
How AI company pages lose trust
Do
- Describe the job, then the technology.
- Say when a person reviews the output.
- Explain data handling in plain words.
- Admit what the model can't do yet.
Don't
- Promise results you can't measure.
- Use hype words or stock robot images.
- Show customer data in screenshots.
- Say a person isn't needed.