Practical AI for busy marketing teams

If you work in marketing, the difficult bit can be finding time to think between everything you need to produce.

The campaign needs launching. Sales want a case study. The newsletter is due, and someone has asked whether you could “just do a quick video”.

That's where I'd start with AI. Look for a recurring task that eats into your week, then work out how AI could help you do it well. My Marketing AI Cheat Sheet (available on request) covers ten practical uses, from audience research to reporting. Here’s how those ideas can fit together in everyday work.

Start with what customers actually say

Before creating another campaign, look at the information you already have. Approved survey comments, customer questions and sales notes can reveal useful patterns.

AI can help group that material into themes: common frustrations, objections, buying questions and gaps in your content. Ask it to show which source supports each theme and highlight views that disagree.

Suppose several customers mention uncertainty about installation. That could suggest an explainer, a clearer product page or a conversation with sales. It doesn't automatically prove that installation is your biggest barrier to purchase. Check the interpretation with people who speak to customers.

You can also use an AI tool with access to current sources to compare competitors’ public offers and messaging. Open the original pages and check dates. An apparent gap in their marketing is an idea to investigate.

Turn the objective into a workable brief

A campaign plan becomes more useful when you give AI the real constraints.

Explain what you want people to do, who you're trying to reach and what you can deliver. Include the offer, available budget, deadline and team capacity. Ask it to flag anything missing before drafting.

For example, a small team promoting an event might need a plan built around an existing mailing list and two useful posts each week. A daily video schedule could look impressive and be completely unrealistic.

Agree what success means too. If you want qualified enquiries, define “qualified”. Otherwise, you risk celebrating activity that hasn't helped the business.

Give your content a recognisable voice

“Make it engaging” leaves rather a lot open to interpretation.

Give AI your brand guidance and a few approved writing examples. Explain what you like about them: perhaps direct openings, practical examples and straightforward language. Include phrases you would never use.

Then ask for a small number of different angles. You might compare an opening based on a customer problem with one that challenges a common assumption.

Add your expertise during the edit. Check every claim, offer and quotation. A convincing sentence still needs evidence.

Repurposing belongs here too. An approved webinar or case study can provide material for a newsletter, LinkedIn posts and a carousel. Give each piece a purpose and adapt it to the audience. Preserve the original meaning and ask AI to add no new facts.

Improve the customer journey

AI can also help you review what happens after someone clicks.

Give it the actual website copy and explain the visitor's likely question. Ask where the wording is unclear, which objections remain unanswered and what information a customer needs before enquiring.

Use those findings to draft headings, useful FAQs and search descriptions. Check the suggestions against your real offer. Adding keywords won't rescue a page that leaves the reader confused.

For email, try a focused experiment. Ask for two subject lines addressing different audience concerns, with the same body copy beneath them. Set out what you expect to learn and choose a measure linked to your goal, such as completed registrations.

Check audience permissions and preferences before sending. Personalisation should use information you are authorised to use, and should never invent a previous relationship.

Explore visuals before commissioning them

AI can help develop campaign concepts, image briefs, storyboards and shot lists.

For an illustrative product campaign, you could explore a demonstration, a customer situation and a comparison of features. Discuss which approach makes the offer easiest to understand before spending time on production.

Check image rights, representation and accessibility. Make sure any product shown matches what customers can actually buy. An attractive image of a feature you don't offer creates another problem to fix.

Ask better questions of your results

A useful report helps you decide what to test next.

Give AI an approved analytics export, explain the metrics and specify the periods being compared. Ask it to show calculations, identify missing data and separate observations from possible explanations.

More clicks alongside fewer enquiries might raise questions about the audience, landing page or tracking. Those are possible causes to investigate. The numbers alone don't establish why something happened.

Check the calculations and lead quality before changing spend. Keep someone responsible for the final interpretation.

Brief it properly and improve the draft

Use my Prompting Pillars to make the task clear. Here's an example for repurposing a webinar, with the details to supply:

Goal: Encourage relevant enquiries about our service.

Context: Use the approved webinar transcript. Our audience is [describe audience]. The offer is [confirmed details].

Persona: Act as a marketing colleague experienced in [sector].

Tone: Use our brand voice, British English and plain language.

Specifics: Draft three LinkedIn posts with different angles. Use only the approved transcript and offer details. Flag missing information before drafting.

Examples: Follow the attached approved posts and our notes explaining what works.

Then use DIP: Draft, Improve and Polish. Review the first version against the goal, ask for specific changes and check the final wording yourself.

Make one useful approach repeatable

Choose one recurring task this week. Record how long it usually takes, then test AI and include checking time in your comparison.

Use approved tools and minimise personal data. Save the instructions, source requirements and review steps that worked. Name the person who approves the output, and measure whether quality or the business result improved.

Want the full Marketing AI Cheat Sheet, or help putting these ideas into practice? Email me at jonathan@pollinger.ai. We can discuss practical AI training around the work your marketing team actually does.