What goes in a marketing AI policy?

Four components carry the weight: an approved-tools list built on criteria (verified data terms on the specific tier, a named owner, a recorded verification date) plus a request path; data rules naming what never enters a prompt; approval gates for public-facing work types; and ownership — a named person, a disclosure stance, an exception log, and a visible review date.

How do we govern AI-generated content?

By work type, not by blanket rule. Public-facing work — published content, paid creative, outbound at scale, regulated claims — passes a named human approver before shipping; internal drafts and research flow freely. Whether AI may publish directly to production surfaces should be an explicit, recorded decision rather than a tool default.

Who approves AI output before it ships?

A named person per work type, written into the policy — not a team or a committee, because a gate without a name is a gate nobody owns. Keep gates limited to work that genuinely warrants them; when everything needs approval, reviewers stop reading carefully and the gate becomes a formality.

How does the policy survive tool and vendor churn?

By being written tool-agnostic: entry criteria rather than brand loyalty, tiers named rather than assumed, and verification dates recorded so stale entries are visible. Add review triggers — a terms change, a new channel, an incident — alongside the scheduled review, and the document adapts instead of aging out.

How to write an AI use policy for marketing

Guide · Enterprise · 4 min read · last verified 2026-07-27

Reviewed before publication Editorial board Independent commercial review

In shortHow to write a marketing AI use policy as enablement, not prohibition: an approved-tools list built on criteria, data rules for what never enters a prompt, approval gates for public work, and a skeleton to adapt.

An AI use policy for marketing is a short written document that answers the three questions your team is already improvising answers to: which tools may we use, what may never be pasted into them, and who signs off before AI-touched work ships. Written well, it is an enablement document — it exists so people can use AI quickly and confidently, without running a private risk calculation at every step, and so the team's speed does not depend on everyone guessing the same way.

Why write it down at all

Unwritten norms tend to fail at exactly the moments that matter: the new hire's first week, the new tool everyone quietly adopts, the deadline that makes shortcuts tempting. A written policy converts case-by-case judgment into defaults, which is faster for the team and safer for the company at the same time — the point is not restriction but pre-decision.

It is also the artifact the rest of your AI governance quietly assumes. The control ladder for agents — recommend, draft, execute with approval — described in how marketing teams keep control of AI agents needs a document that says which rung each kind of work sits on. This policy is that document. Keep it to a couple of pages; a policy nobody reads governs nothing.

The approved-tools list

Write the list as criteria plus entries, not as a tribute to this year's tools, so it survives vendor churn. A tool earns an entry when: its data terms have been verified on the specific tier you use — the homework described in do AI vendors train on your data — a named person owns the relationship, and the verification date is recorded. The same product on a personal free account and on an enterprise contract can have different data postures, so the entry names the tier, not just the brand.

Include a request path: how someone proposes a new tool, who vets it, and roughly how long that takes. This single addition is what makes the policy enabling rather than prohibitive — an unlisted tool is "not yet approved," a state with an exit, rather than "banned," a state that breeds workarounds.

Data rules: what never goes into a prompt

The shortest section and the one doing the most protective work. Name the categories plainly: customer personal data, unreleased financials and product plans, credentials and keys, partner-confidential material, anything under NDA. For the grey zone, give people a portable test instead of a longer list — if sharing it outside the company would need approval, putting it in an external tool's prompt needs the same approval.

Two refinements keep this honest. First, the rules can differ by tool class: a contracted enterprise tool whose terms you have verified may be approved for material that a personal-account tool never is — the policy should say so explicitly rather than pretend all tools are equal. Second, say what to do when unsure: name the person to ask, and make asking cheap.

Approval gates for public-facing work

Anything that ships to the public passes a named human approver before it goes — the held-proposal mechanism defined in what is an approval gate in marketing AI. The policy's job is to say which work types carry a gate and which do not: published content, paid creative, outbound at scale, and anything touching regulated claims get one; internal drafts, research, and brainstorming do not. Gates on everything means gates on nothing — reviewers end up rubber-stamping what they cannot actually read carefully.

One decision deserves its own line in the policy because teams keep making it by default instead of on purpose: whether AI systems may publish directly to production surfaces. The arguments on both sides are laid out in should AI publish directly to your CMS; whatever you decide, the policy should record the decision and who made it.

Disclosure, records, and ownership

Three items that take a paragraph each and prevent most future arguments. Disclosure: decide when, if ever, AI assistance is disclosed — externally and internally — and write the stance down, because an improvised answer under pressure is worse than either policy. Records: exceptions will happen; require that they be logged with who approved them, because the exception log is the best input to the next revision. Ownership: the policy has a named owner — a person, not a committee — who fields the grey-zone questions and runs the reviews.

A skeleton to adapt

AI use policy — Marketing team — Version 1 — Owner: [name] — Next review: [date]

Approved tools

[Tool name] — [tier] — data terms verified [date] — owner [name]

To request a tool: write to [owner]; expect an answer within [period].

Unlisted tools are not yet approved for company work.

Data rules

Never in any prompt: customer personal data; credentials or keys;

unreleased financials or plans; NDA or partner-confidential material.

Test for the grey zone: if it needs approval to share externally,

it needs approval to prompt externally.

Unsure? Ask [owner] first. Asking is always the right call.

Approval gates

Public-facing work (published content, paid creative, outbound at

scale, regulated claims): approved by [name] before shipping.

Internal drafts and research: no gate.

Direct AI publishing to production: [permitted / not permitted],

decided by [name] on [date].

Disclosure

[Team stance on disclosing AI assistance, internal and external.]

Exceptions

Logged at [location] with approver and reason.

Keeping it alive

Review on a schedule and on triggers: a tool's terms change, a new channel opens, an incident happens, an exception pattern repeats. Version the document visibly and keep the old versions, so anyone can see what the rule was when a given piece of work shipped. A policy whose review date has quietly passed is a policy the team has already stopped trusting — the date on the front page is part of the promise.