Where does AI fit in with marketing strategy

Many people start with AI by asking it to write a caption, an email or a product description. It does that job well enough, but writing is one of the less strategic things it can do for marketing.

Strategy starts earlier, with evidence about what customers are saying, what competitors are claiming and which messages are working. AI can read and sort large amounts of that material far faster than a person can, which makes it a capable research and testing assistant. The decisions still need to be yours.

Start with evidence, not a random prompt

AI works with what you give it. Ask it to guess your market and you'll get an answer that sounds sensible and could apply to almost anyone. Give it real material and it can find patterns that would take you far longer to find by hand.

Good material includes customer reviews, survey responses, enquiry emails, notes from sales conversations, competitor websites, search terms and sales data. Ask it to pull out what comes up repeatedly: the needs people describe, the objections they raise, the frustrations they mention and the words they use. Keep the original material to hand so you can check its conclusions, and tell it to quote only phrases that appear in what you've supplied.

In my experience, AI works well when it's given guidance, and a lot of information about your business, before it produces anything good. I've seen poor-quality output built on wrong assumptions. That is why you can't take away the human input.

Where it helps in the strategy process

Once it has real evidence to work from, AI can help with several stages of building a strategy. Audience research is the obvious one, because it can summarise a large pile of comments or reviews into the needs, objections and language that keep recurring. Positioning is another. Give it competitor descriptions and it can show where claims are crowded and where few people are saying anything. A good prompt asks for several positioning options. For each one, ask for the ideal buyer, the core promise, what proves it and the risk of making the claim.

Segmentation works well too. AI can group people by behaviour, situation or how far along they are in deciding, so the message fits each group, and it can suggest several angles on the same offer. Choosing between those angles is your job, because it will produce an attractive one whether or not it's true of your business.

When it's time to produce assets, ask for variants and never a single final answer. Ten headlines, five email subject lines and several calls to action give you a full set of options to test, and you can refine the strongest in your own voice. It also handles repetitive work well, such as turning one piece of content into several formats, tagging content and drafting routine reports.

Test one thing at a time

More options only help if you test them properly. Change one variable at a time, for example two headlines with the same image, or two message angles with the same audience. If you change everything at once, you can't tell what made the difference.

AI helps again once the results come in. Give it the structured numbers, such as impressions, clicks, conversion rate, cost and sales over a set period, and ask it to separate what the data shows from what it's guessing. Then ask for the next tests worth running and what each one would teach you. Check anything it tells you against your own figures before acting on it.

Keep a simple log of each test: the hypothesis, the version, the channel, the dates, the spend, the result and what you decided. Without a log, AI suggestions stay as random output, and with one you build up learning you can reuse.

Where it needs checking

AI can produce recommendations that sound plausible and turn out to be weak, particularly when the material it's given is poor or incomplete. It can also invent competitor details, overstate a trend, write generic copy and reflect bias in the sources it analyses.

Human review matters most for claims about outcomes, pricing, testimonials and competitor comparisons. Verify keyword volumes, platform rules and competitor facts independently, and don't publish generated copy unchanged. It needs your voice, specific detail and a point of view, or it will read like everyone else's.

Privacy needs care too. Before you paste private customer information into a public AI tool, make sure you have an appropriate basis for using it and that the tool's terms allow it.

A sensible place to start

A simple sequence works well. Review the research, identify the language your buyers use, create three to five message angles, build assets for each channel, test them, analyse the results and improve. Then repeat.

The research step often turns up the biggest changes. A business may describe what it does using its own category name, while customers describe the problem in completely different words. Finding that gap is worth the effort, because the customer's own words are often the strongest message you have.

AI can turn scattered signals into something you can test. Deciding what to say, who to say it to and whether it's true of what you offer is still down to you.