A LinkedIn personal brand content plan can start with three useful posts for one audience. Choose a recurring problem you understand, explain one part of it clearly, and keep a record of what readers ask next. You do not need to settle your entire public identity before writing something helpful.
Seth Godin's November 7, 2026 essay, “Start,” provides a timely prompt for this exercise. He argues that tools have reduced many practical barriers to beginning, while fear still holds people back. The plan below is our application of that idea to LinkedIn writing, not advice or an endorsement from Godin.
Define the promise your readers can recognize
A topic is a category. A promise explains why someone should return. “I write about AI” is a category; “I explain how small teams can test AI workflows without losing control of quality” is a more specific promise.
Use this sentence as a private planning tool:
I help [audience] understand [recurring problem] using [experience or perspective I can actually offer].
It does not need to become a public tagline. Its job is to help you choose what belongs in the next post. A designer, job seeker, researcher, operator or founder can use the same exercise with different evidence.
Plan three different contributions
Choose one real problem and approach it from three directions. For an illustrative AI-workflow creator, the problem could be deciding when a draft is ready for human review.
| Post | Reader's question | Your contribution |
|---|---|---|
| Explain | What makes a draft risky to trust? | A short distinction with a concrete example |
| Evaluate | When should a person intervene? | Your reasoning, including its limits |
| Apply | What can I try in my workflow? | A small checklist the reader can adapt |
This is a suggested sequence, not a tested formula for reach. It avoids repeating the same announcement three times and gives you several ways to discover what the audience needs.
Write from an evidence inventory
List the material you already have: a question someone asked, a mistake you can describe without exposing a client, a decision you changed, or a public source you can explain. Mark each item as personal experience, sourced fact or hypothetical example.
That label makes drafting easier. An experience can support “I noticed.” A source can support “This report describes.” A hypothetical can support “Consider a team that…” Moving carelessly between those forms is how a plausible AI draft becomes a false personal story.
Draft the first post before polishing the whole plan
An original illustrative opening might be:
Before adding AI to a workflow, decide who can reject its output.
A faster draft is useful only if someone can recognize when it is wrong.
For a first experiment, I would write down:
• One task the model may attempt. • One kind of error that stops the process. • One person who reviews the result.
The right threshold will depend on the task. What would you need to see before trusting an AI draft in your own work?
Replace the hypothetical framing with your own experience only if you have it. Specificity is useful; invented certainty is not.
Keep a learning record
After publishing, save the post and note the questions it attracted. Separate evidence from interpretation: “Two people asked about review ownership” is an observation; “Everyone wants an AI governance course” is a much larger inference.
Use that record to choose the next question. A quiet post can still reveal that your example was unclear or your audience definition was too broad. A popular post can attract people who have no interest in the work you want to be known for.
Use Shapeit's editor to prepare the first draft. Save your audience profile and past posts in Studio when you want future drafts to build on the same promise. Godin's essay is the prompt to begin; your evidence and readers determine what is worth continuing.