Generative AI in Marketing: Practical Use Cases

What generative AI is good at in marketing

Generative AI produces new text, images, audio or video from an instruction and examples. In marketing that makes it good at five kinds of work:

• • Drafting: a first version of something a person will finish.
• • Varying: many versions of one idea for testing or for different channels.
• • Summarising: long material turned into short, usable notes.
• • Restructuring: the same content reshaped into a new format.
• • Synthesising: patterns found across many inputs, such as reviews or survey answers.

It is weak at knowing facts about your business it has not been told, judging what matters commercially, and knowing when it is wrong. Every practical use case pairs its strength with a person covering its weakness.

Twelve use cases, with their checks

Generative AI use cases in marketing

Use case	What AI does	What a person checks	Risk
Summarising customer reviews	Groups praise and complaints, counts themes, pulls quotes	Spot check quotes against source; decide what to act on	Low
Research synthesis	Organises competitor ads, search queries or survey answers	Nothing invented; conclusions justified	Low
Content briefs	Drafts a brief from queries, audience notes and goals	Angle and priorities	Low
Meeting and call notes	Summarises sales or customer calls into objections and language	Accuracy of summary; personal data handling	Low to medium
Ad copy variations	Produces headline and description options within limits	Claims, policy, brand voice	Medium
Email drafts	Drafts campaign and nurture emails	Facts, offers, tone, compliance	Medium
Social post adaptation	Turns one message into platform specific versions	Tone per platform; no overclaiming	Medium
Blog and article drafts	Drafts from a brief and sources	Accuracy, originality, usefulness, sources	Medium
Product descriptions	Drafts from product data	Every specification and claim	Medium
Video scripts	Drafts hooks, scenes and captions	Claims, brand fit, feasibility	Medium
Images and graphics	Generates or edits visuals	Does not misrepresent the product or people; rights	High
Personalised messages at scale	Tailors messages to segments or individuals	Data use is lawful; no errors at volume	High

Why images and claims need the closest checks

The UK advertising regulator's position is that ads made with AI are held to the same rules as any other ad, and that a disclosure saying AI was used is very unlikely to fix a misleading impression. The ASA gives the example of an AI generated image showing a cosmetic effect the product does not produce. Any image that shows your product, results or customers should be checked for whether it represents reality.

Claims have the same issue in text form. A model asked for benefits will produce plausible ones. Keep an approved list of claims, with sources, and treat anything outside it as unverified until a person checks it.

What Google says about AI generated content

For content published on your site, Google's guidance is that it rewards original, helpful content that shows experience, expertise, authoritativeness and trustworthiness, however it is produced. It also says that using automation, including AI, to generate content primarily to manipulate search rankings breaches its spam policies. The practical consequence: AI can help you produce useful pages faster, and it can also help you produce unhelpful pages faster. The standard is usefulness to the reader.

Personal data and generative AI

Several use cases involve personal data: call notes, personalised messages, review analysis where reviewers are identifiable. The UK GDPR applies. The ICO's guidance on AI and data protection covers lawfulness, fairness, transparency and data minimisation in AI use. Practically: check what your AI provider does with inputs, do not paste customer personal data into consumer tools, and use aggregated or anonymised data where you can.

A four week adoption plan

1. • Week 1: pick two low risk uses (review summaries and content briefs, for example). Name a reviewer for each. Write a one page brief about your business for every prompt to include.
2. • Week 2: run them on real work. Record for each output whether it was used as is, used with edits or discarded.
3. • Week 3: add one medium risk, customer facing use (email drafts or ad variations) with review before anything goes out.
4. • Week 4: compare time spent and quality against before. Keep what earned its place; drop what did not.

Worked example: a content brief from search data

Illustrative example for a hypothetical accountancy firm.

• • Input: 40 Search Console queries the firm's "sole trader accounts" page appears for, the firm's business brief, and three competitor page headings.
• • Instruction: "Group these queries by what the searcher wants to know. Identify which groups the page does not answer. Draft a brief for an improved page: audience, questions to answer in order, proof to include from the business brief only, and a suggested title."
• • Output: five query groups, two of which (deadlines and what records to keep) the page does not cover; a brief with seven questions in order.
• • Check: the reviewer confirms the query grouping is sensible, adds a point about Making Tax Digital that the AI had not raised, and removes a suggested claim about "fastest turnaround" that is not in the approved proof.
• • Time: the brief takes 20 minutes including review, against perhaps an hour by hand. (Illustrative.)

The AI did the sorting; the person supplied the expertise and the honesty check.

What generative AI will not do for you

It will not decide your positioning, know your margins, or tell you which customers are worth acquiring. It will not notice that an offer has expired unless you tell it. And it will not take responsibility for what you publish. Those stay with people, whether in house or, in AfM's case, as part of a managed service where the founder reviews what ships.

For the tools behind these use cases, see the AI marketing tools comparison. For writing that sounds like you, see why AI content sounds generic.

FAQ

Do I have to tell customers I used generative AI?

There is no blanket UK rule requiring disclosure in ads. The ASA's test is whether leaving it out would mislead. For realistic images of people or product results, consider disclosure, but disclosure does not cure a misleading claim.

Which use case should a small business start with?

Summarising customer reviews or call notes into themes and language. It is internal, low risk, quick to check and often changes what you say in your marketing.

Can generative AI write all of our blog content?

It can draft it. Whether it should publish unedited is another matter: Google judges content on usefulness and expertise, and drafts without your knowledge and examples tend to be generic.

How do I stop AI copy sounding like everyone else's?

Give it your own material: real customer phrases, past writing you are proud of, specific proof and words you never use. Generic input produces generic output.