Guide: AI-Generated Content Labelling Workflow Check
A practical guide for content teams, comms, HR, legal and publishing workflows using AI. This guide helps teams define simple labelling rules for AI-generated and AI-assisted content. It is not legal advice.
AI-Generated Content Labelling Workflow Check
A simple check for teams using AI to draft, edit, summarise, design or publish content before the final label becomes a last-minute panic.
Do not only label the content, you have to know the path it travelled through.
What this guide helps with
This guide helps teams define simple labelling rules for AI-generated and AI-assisted content. It is not legal advice, it acts as an operating check for the messy place where drafts, edits and approvals actually happen.
Why now
AI-generated content rules are moving from abstract policy into daily publishing, comms, HR, marketing and product work. Teams need simple rules before people start guessing differently across every channel.
The pattern
The pattern is that AI use starts early and labelling questions arrive late. If the workflow does not capture AI use as it happens, the final reviewer has to guess what happened after the document has already travelled through five hands.
The check
Start with real examples: LinkedIn posts, website copy, HR emails, training material, product descriptions, reports, images, videos, voiceovers or customer messages. A generic “AI content” policy is hard to apply but a list of actual content types makes the labelling question practical.
Does AI create the first draft, rewrite a paragraph, summarise a meeting, generate an image, translate text or polish tone? The label decision changes depending on where AI enters. A heavily edited human document is not the same as a fully generated image.
Create simple internal categories such as AI-generated, AI-assisted and human-edited. Avoid legal poetry as much as possible because people need language they can use quickly. For example, “AI-assisted” may mean AI helped draft or rewrite, but a human checked and owned the final version.
Do not leave labelling to whoever uploads the final file. Name an owner for each content type; for example, marketing owns public campaign copy, HR owns employee communications, legal reviews higher-risk claims and product teams own user-facing release notes.
For low-risk internal notes, you may not need much. For higher-risk public, legal, HR, financial or reputational content, keep a short note: AI used, purpose, reviewed by whom, final label decision. This should not be a complex cathedral, it is a receipt.
The tricky part is not the pure AI output, it is the edited version. If AI text is rewritten by humans, if an AI image is modified, or if AI output is pasted into a deck, decide what rule applies. Mixed content is where most confusion lives.
After the first ten pieces of content, review what confused people. Did they know what to label? Did reviewers disagree? Did the rule create too much admin? Adjust the workflow before it becomes another policy nobody reads.
Quick examples
| Situation | Better question |
|---|---|
| AI writes a full social post | Likely needs clear internal review and may need external labelling depending on jurisdiction, context and risk. |
| AI suggests three headline options | This may be AI-assisted rather than AI-generated, but the team should still know whether it matters for that channel. |
| A human rewrites AI-generated training material | Who owns the final version, and how much of the AI path needs to be recorded? |
| AI creates an image later edited in Canva | The output path matters because visual AI content often carries different transparency and reputational risks. |
The Satire
It's called a workflow, not a whodunit.
Related Vieews paths
Chaos scenes spot the contradiction. Signals name it. Guides give you the next simple move.
Chaos
The Blue Blob and the Labelled Picture
The discovery scene that started this thread.
Signal
AI Labelling Is A Workflow Problem, Not Just A Compliance Thing
Use the signal when you want the pattern named clearly.
Playbook
Context Map
Use the heavier structure when this thread needs more depth.
Useful context
This guide is deliberately practical. It does not try to replace legal review. It helps normal teams stop treating AI labelling as a sticker emergency.
These are Vieews, not bibles. Use them as lenses, not legal advice, investment advice, HR policy, or a replacement for doing your own investigation. If a line makes the spreadsheet uncomfortable, excellent: ask one more question, tug on that thread, and do not get fired.
