Generative AI Brand Guidelines: What Belongs in Writing

A marketing team can have an AI policy and still leave someone stuck at 4 p.m. with a simple question: “Can I use this tool for this task?” The document exists, but the answer is buried somewhere nobody wants to search.

That is where generative AI brand guidelines need to be practical. They should tell people what they can do without asking, what needs a human check and what information cannot go into an AI tool. This guide gives you the section list for a document of about two pages. You should be able to use it as a working skeleton and write the first version in an afternoon.

Key Takeaways

• Keep the policy short enough for marketers to use during daily work.
• Write permitted uses as tasks rather than building the document around tool names.
• Separate human review, restricted information and prohibited uses.
• Give approved tools a clear status and an owner who keeps the list current.
• Add one escalation route so unclear cases do not become automatic refusals.

What is the document actually for?

A generative AI usage policy for marketing should help a person decide what they can do without asking someone else. That makes it a working document for daily tasks rather than a complete statement of the company’s legal position. Every section should pass one test: Will this help someone decide while they are working? If the answer is no, then the material probably belongs somewhere else.

A wider generative AI governance process can cover risk management, procurement, technical controls and organizational responsibilities. The marketing policy can point to those processes without copying them.

Marketing teams use generative AI in small moments. Someone summarizes public research. Someone creates a first draft from an approved brief. Someone turns approved copy into several versions. The policy should tell each person where the line sits. A useful opening can be short:

Purpose: Use approved AI tools for approved work while protecting company information, customers and intellectual property.
Applies to: Employees, contractors and anyone using AI for company work.
Owner: The person responsible for keeping the policy current.

That is enough context to get into the rules.

Section one: What may you use it for without asking?

Start with permitted tasks. This gives the team a clear starting point and reduces the guessing that comes from a policy made almost entirely of prohibitions. Write the list around work rather than tool names. Tools change quickly, while tasks are easier for employees to recognize. A marketing team might allow:

• Research summaries from public material
• First drafts from approved briefs
• Rewriting or versioning approved copy
• Summarizing internal material in approved tools
• Resizing or adapting approved creative
• Brainstorming campaign angles
• Turning approved material into internal notes
• Formatting or organizing information
• Routine marketing ops automation using approved workflows

The policy should also say when permission changes. “Research” is too broad if it could mean public market research or confidential customer research. A simple permission table keeps the section usable:

This is where generative AI brand guidelines become useful. The person should be able to scan the list and keep working.

Section two: what always needs a human before it leaves?

The policy needs a clear human review rule for work that can affect customers, reputation, legal exposure or public claims. The reviewer should be named by role so the rule survives staff changes.

A simple rule works well: Generative AI can assist with the work, but a responsible person owns the final output. That person checks the parts that matter for the task rather than treating human review as a box to tick. Put these cases on the mandatory-review list:

• Claims about products, performance or results
• Content containing a person’s likeness or identity
• Customer-facing content produced at scale
• Legal, regulatory or compliance-sensitive material
• Material where facts or sources affect the decision
• Content that could affect a customer’s rights, money or access

The reviewer might be a marketing lead for ordinary campaign copy, a legal or compliance role for regulated claims or an information security role for sensitive data questions. These are your automation guardrails. They should tell people exactly when an automated workflow stops and a person takes over.

The policy should also define the review standard. Check facts, source material, rights, audience and whether the output makes a claim the company can support.

Section three: what never goes in?

This section should answer the question employees have before they paste something into an AI tool: Can this information go into this tool?

Keep the list concrete. It can include customer personal data, unreleased financial or commercial information, confidential third-party material, credentials, secrets and material the company does not have permission to share.

Then add the practical rule: if the person does not know whether information is approved for a particular tool, they stop and ask. A useful policy can divide information into three buckets:

The exact categories should match the company’s existing information classification rules. The AI document should point to those rules rather than create a second classification system.

This is where model governance reaches daily marketing work. The policy needs to show who decides whether a model or tool can handle a category of information and what happens when its terms change.

That matters for large language models as well as AI features built into existing software. A policy covering only standalone chatbots can miss AI functions already sitting inside the marketing stack.

Section four: which tools are approved and why?

Make one short table with three statuses: Approved, Conditionally approved and Not approved. Then give the reason for each status.

The reason matters because tool quality is only one part of the decision. Data handling, retention, access controls, contractual terms and account ownership can affect whether a tool belongs on the approved list. For each approved tool record:

• Tool name
• Approved use
• Data it may handle
• Account type required
• Approval owner
• Review date

For conditional tools, state the condition. A tool might be acceptable for public research but not for confidential customer information. Do not freeze the list forever. Assign one owner and set a review date. The policy should explain how a new tool gets assessed and how employees are told when a status changes.

This matters when AI features appear inside software the team already uses. Existing approval of a platform does not automatically mean every new AI feature is approved for every type of company data.

Section five: what should the policy say about disclosure?

Disclosure needs its own section because internal disclosure, client disclosure and public disclosure answer different questions.

• Internal disclosure tells colleagues how AI was used when that information matters to the work or review. 
• Client disclosure covers situations where a client agreement, project rule or applicable requirement calls for disclosure. 
• Public disclosure concerns what an audience should be told about AI involvement in content or experiences.

The policy should state who makes each decision and where the decision is recorded when a record is needed. Disclosure rules can differ by market, channel and use case. Current advertising industry guidance takes a risk-based approach to deciding when AI involvement should be disclosed rather than treating every AI-assisted action in the same way.

That means the document needs a trigger rather than a vague instruction. State when disclosure is required, who decides and where it appears.

For client work, the contract may add another requirement. For public content, local advertising or consumer rules may apply. The policy should point people to the relevant process instead of trying to settle every legal question itself.

This is also where the AI brand safety policy connects with the working document. The policy should give the marketing team a clear route for handling sensitive public-facing uses while leaving detailed legal interpretation to the appropriate function.

Section six: Where do I go when the policy does not answer?

Every policy needs one escalation route. Give employees one named role, team or channel and a response expectation. The route should handle questions such as:

• A new AI tool is not on the approved list.
• A project needs restricted data in an approved workflow.
• A client has asked for a different disclosure approach.
• An output raises a legal, privacy or rights concern.
• The policy is unclear about a new AI feature.

Do not create five different doors for five different questions unless the team genuinely needs them. A marketing employee should know where to start. The escalation owner also needs a backup. 

If the only person who can approve an exception is away, then the policy quietly becomes a ban. The simplest rule is: If the policy does not cover the case, ask here. Do not guess.

What should you leave out?

This is the section that keeps the policy short. Leave out the history of generative AI, explanations of how large language models work, full tool tutorials and long legal interpretations. Those topics may matter, but they do not help a person decide whether they can paste a brief into a tool at 4 p.m.

Also leave out rules that belong in another process. Security controls should live with security. Vendor procurement should live with procurement. Detailed legal wording should live with legal. Model testing and technical risk assessment can sit inside the wider governance process.

The AI policy should link to those documents rather than copy them. Use this policy-cut test before publishing:

1. Does this sentence help someone decide?
2. Does it tell them what action to take?
3. Does another policy already own this rule?
4. Will the sentence probably change within a quarter?
5. Can the same decision be explained in fewer words?

If a sentence fails the first two questions, then cut it or move it. A broader AI risk framework can cover organizational risk management while an employee-facing policy can focus on the decisions marketers make during everyday use. That separation keeps the working document short without pretending the wider governance work does not exist.

Who should sign it and how does it stay alive?

The people who sign the policy should have authority over the rules it contains. For a marketing-facing document, that will usually mean a marketing owner plus the functions responsible for the risks covered by the policy.

Depending on the company that may include legal, privacy, security, procurement or an AI governance owner. The signatures should confirm ownership of the rules rather than turn the document into a ceremony. Put these fields at the bottom:

• Policy owner
• Approving roles
• Effective date
• Version number
• Next review date
• Change log
• Escalation contact

Set a review cadence that matches how quickly the team’s AI use changes. The right cadence depends on the organization and its risk profile. The important part is giving someone a scheduled moment to check tools, data rules, disclosure requirements and escalation paths.

That is also a useful signal of AI adoption maturity. A team that gives the document an owner, a review point and a way to communicate changes has a working process around the policy. When the policy changes, tell people what changed. A short message with the changed rules is more likely to reach the person using generative AI than an instruction to reread the entire document.

The document skeleton you can copy

If you are starting from zero, build the first version in this order:

That is the skeleton. The supporting documents can carry the detail.

The Read

I think the structure of these policies is starting to become familiar, while the actual rules will stay company specific. The tools, data, contracts and legal obligations differ too much for one universal document to work everywhere.

If there is no policy today, write the permitted-use section first. Tell people what they can safely do without asking. Then build the restrictions around that working list. The best first version is the one a marketer can open at 4 p.m. and understand in a few minutes.