A Generative AI Policy for Marketing Teams That Protects the Brand Voice

For marketing leaders who want their team to move faster with AI without putting the brand at risk.

My training includes crisis and issues management and reputation management, and at BBDO Levant I managed mandates in legally sensitive environments. One thing that work teaches you is that reputational problems usually start small: a line nobody checked, a claim nobody could support. AI makes it faster to produce good work, and also faster to produce those small mistakes.

What is a generative AI policy for marketing?

A generative AI policy is a short set of shared rules for how a team uses AI tools: what for, with which data, under whose review and how readers are told. For a marketing team, its main job is to protect three things at once: client and company data, the accuracy of what gets published and the brand’s voice. In my experience, the longer a policy gets, the less of it people remember.

Why teams need a policy now

People are already using AI, often without clear guidance. In BCG’s 2025 survey, only 25% of frontline employees said their leaders gave them enough AI guidance (BCG, June 2025).

Google’s own guidance is a useful reminder of the risk. It explains that generative models don’t retrieve facts, so their output can contain errors, and it asks publishers to manually fact-check and review AI-generated content before publishing, including titles, meta descriptions and image alt text (Google Search Central, updated October 2026).

No policy vs a two-page policy

	No shared policy	A short, shared policy
What’s allowed	Each person decides alone	Approved and off-limits uses are listed
Client data	Depends on individual judgment	Clear rules on what never goes into AI tools
Accuracy	Errors caught late, if at all	Facts checked against sources before publishing
Brand voice	Drifts toward generic phrasing	A person owns the final voice
Confidence	People hide their use or avoid AI	People use AI where it helps, openly

What the policy should cover

Area	What to decide	Example rule
Approved uses	Tasks AI can help with	First drafts, variations, summaries, resizing assets
Off-limits uses	Tasks AI must not do	Final legal or medical claims, crisis statements, quotes attributed to real people
Data	What may never be entered	Confidential client information, unreleased results, personal data
Brand voice	How output stays on brand	Voice guide and examples supplied; a person edits every piece
Fact-checking	What gets verified	Every number, name, date and quote, against the original source
Review and sign-off	Who approves what	Scaled to risk: a caption differs from a product claim
Disclosure	When and how AI use is shared	Agreed with clients and in line with platform rules
Images and rights	Sources and labels	Licenses checked; AI-generated images labeled
Ownership	Who maintains the policy	One named owner, reviewed every few months

Protecting the brand voice

Voice is where AI output most often drifts. A few habits help:

• Give AI the brand’s voice guide, plus examples of writing the brand would publish and writing it wouldn’t.
• Keep a short list of words and phrases the brand never uses.
• Make a person responsible for the final voice of every piece. AI can draft, but it doesn’t own the sound of the brand.
• Read important pieces aloud before they go out. Generic phrasing is easier to hear than to see.

How to write your first policy, step by step

1. Ask the team how they use AI today. Be clear that the point is to learn, not to catch anyone out.
2. List the approved and off-limits uses based on what you hear.
3. Agree on the data rules with your legal or privacy contact.
4. Decide who reviews what, scaled to risk.
5. Fit the key rules on one page.
6. Test the draft with the people who’ll follow it, and adjust.
7. Name an owner and set a review date a few months out.

A policy works when it’s part of the workflow. Cover it in training and build the checks into the review step. I wrote about setting up that step in how to build AI into a marketing team’s workflow, and about deciding where AI belongs in the first place in mapping how your team works.

Common mistakes to avoid

• Writing the policy without the team. Rules written far from the work tend to miss how AI is actually used.
• Banning everything. Strict bans push AI use out of sight, which is where the risk grows.
• Making it too long. People remember a page, not a manual.
• Forgetting images and video. Rights and labeling questions come up just as often as text.
• Never updating it. A policy written for last year’s tools will have gaps today.

What agencies should add

Agencies work with many clients, each with its own rules. An agency policy needs a clear default, plus a simple way to record each client’s preferences: whether AI may be used on their work, what disclosure they expect and which data must stay out of AI tools. The account lead is usually the right owner for that record.

Where I can help

I help marketing teams and agencies put AI governance and brand safety in place: a short policy people actually follow, voice safeguards and review steps that match the level of risk.

Final thought

Ask your team how they’re using AI today, then write down the five rules you’d want everyone to follow tomorrow. That’s the core of your first policy.

Frequently asked questions

Sources

1. Boston Consulting Group, “AI at Work 2025: Momentum Builds, But Gaps Remain,” survey findings press release, June 26, 2025. Read on bcg.com
2. Google Search Central, guidance on using generative AI content, updated October 1, 2026. Read on developers.google.com

Related articles