Replace a broad promise with a usable answer
A useful AI-use policy answers the questions a buyer would ask before sharing information or approving work. Where is AI involved? What information enters the workflow? Who reviews the result? Who handles a correction? Start with those questions and connect each answer to an actual process.
In Search Engine Journal on 18 September, Greg Jarboe argues for concrete public accountability documents. His search-visibility interpretation is commentary, not proof of a ranking effect. The practical opportunity is simpler: buyers should be able to inspect the meaning behind a claim such as “human reviewed.”
Map AI use before writing policy language
Choose one service and list its stages: intake, research, drafting, design, checking, approval, delivery, and maintenance. Mark where AI assists, where a person decides, and where an external tool receives information. The map will reveal whether the same policy can accurately describe every stage.
A research assistant reading public documents is different from a system processing confidential client files. Treat those as separate entries. “We use secure AI” conceals the questions that matter: which tool, which account configuration, which data, which access permissions, and which retention arrangements have actually been verified?
Keep an evidence record behind each public claim
| Buyer question | Evidence to check |
|---|---|
| Who checks my deliverable? | Named role, review checklist, approval record |
| What information reaches external tools? | Workflow map and current tool configuration |
| Can I request a correction? | Contact route, owner, and correction procedure |
| What happens when the workflow changes? | Change owner and policy review trigger |
Do not promise that a provider never retains data or never uses it for training because you remember a product announcement. Check the service, plan, settings, and applicable agreement. If the answer is still unknown, resolve it before processing that category of client information or making a public assurance.
Write at the level a buyer can evaluate
Here is fictional example wording: “We use an AI assistant to prepare a first draft from approved project notes. The project lead checks factual claims and scope before the draft reaches the client. Client approval is required before publication.” This explains a sequence. It should only be adopted by a team that actually follows it.
Then specify the boundary. The same team might allow automated formatting of approved copy while reserving price changes and public claims for separate approval. Describe that difference without listing every internal implementation detail. A buyer needs to understand the commitment and know how to question it.
Make human review an action
A reviewer needs something concrete to inspect: source support, required exclusions, approved brand language, and the intended recipient. “Look over the output” is too vague to explain what the review protects. Keep a lightweight record of the checks and the decision, especially when a deliverable contains consequential claims.
Google's content guidance encourages accuracy, quality, relevance, and useful context about how content was created. It does not turn a disclosure sentence into a substitute for those qualities. Use disclosures where they help the audience understand the work.
Maintain the policy with the workflow
Assign one owner and a review trigger: a new provider, a changed account setting, a new data category, or a revised approval process. Keep old versions internally so the team can understand which promise applied at a given time. Provide a working contact route for questions and corrections.
This is an operational writing framework, not a claim that JQ has audited another company's controls. If your team cannot yet answer the questions consistently, a workflow assessment can identify the gaps. The public page should be the readable outcome of that work.
Link Map
Sources and editorial review
Sources reviewed on 21 September 2026. The practical workflows and illustrative examples are JQ AI SYSTEMS analysis unless explicitly attributed.
- Search Engine Journal: Your Brand Needs an AI Accountability Document (18 September 2026; Greg Jarboe, commentary).
- Google Search Central: Using generative AI content (Standing documentation, checked 21 September 2026).