GenAI for Marketing: A Practical Starting Guide

Most people with a GenAI licence use it the same way: open the tab, type a rough instruction, get something generic back, rewrite half of it anyway, and quietly conclude the tool is overhyped.

The tool is not the problem. The way most people are using it is.

This is a starting guide for a marketer or founder who has ChatGPT, Gemini, or Claude sitting open in a browser tab and genuinely does not know how to use it well day to day. Not a strategy piece about rebuilding your marketing operating model around AI. A practical one, about the specific tasks worth delegating this week, the ones you should keep for yourself, and the difference between a prompt that produces something usable and one that produces filler.

What is actually safe to delegate right now

Four categories of marketing work are genuinely well suited to a language model today, and most beginners underuse all four.

First-draft copy variants. If you already know what you want to say, generating five headline options, three email subject lines, or a handful of ad copy variants from one clear brief is fast, cheap, and a legitimate use of the tool. You are not asking it to decide the message. You are asking it to produce options around a message you have already set.

Summarising customer interviews or reviews for patterns. Feed a language model fifty product reviews, twenty support tickets, or the transcripts of ten customer calls, and ask it to surface recurring language, complaints, and phrases customers actually use. This is a task humans do badly at scale, not because they lack the skill, but because reading fifty reviews and holding the patterns in your head is genuinely hard. A model can do this in minutes and hand you back the actual words your customers use, which is often more useful than any survey.

Competitor content audits. Pointing a model at a competitor's website, blog, or social presence and asking it to map their messaging themes, content gaps, and positioning claims is a solid use of the tool. It will not tell you what to do about it. It will tell you, accurately and quickly, what is already out there.

Repurposing one piece of content across formats. Turning a long blog post into a LinkedIn post, three tweets, and an email is close to ideal AI work: the thinking has already happened, and the task is genuinely mechanical reformatting with tone adjustment. This is where most beginners get the most immediate time back.

What still needs a human doing the thinking

Positioning is not a delegation task. Deciding what your business's actual differentiation is, who you are for, and what you are not for requires judgement about your specific market and your specific customers that a general-purpose model was never trained to hold. Ask it to help you think through the question, by all means. Do not ask it to answer it.

What to actually say, in the specific situation you are in, also stays with a human. A model can give you five versions of a message. It cannot tell you which one is true for your business, or which one your specific customer, in your specific market, will actually believe. That judgement comes from knowing your customers, not from a prompt.

Anything requiring real customer judgement falls in the same category. If a customer complaint pattern surfaces in your review summary, deciding whether that pattern is a product problem, a communication problem, or a segment mismatch is a call only someone who understands the business can make. The model found the pattern. It did not diagnose the cause.

The difference between a prompt that works and one that does not

The single biggest reason beginners get generic output is that they write prompts the way they would type a search query, a few keywords and a hope, rather than the way they would brief a new team member.

A vague prompt: "Write a LinkedIn post about our new feature."

A prompt that produces a usable draft: "Write a LinkedIn post announcing our new invoicing feature, for small business owners in India who currently use spreadsheets or a basic accounting tool. They are price-sensitive and sceptical of anything that sounds like it requires IT setup. The tone is direct and slightly informal, similar to our last three posts, which I am pasting below. The post should end with a specific next step: try the feature free for 14 days, not a generic call to action. Keep it under 150 words."

The difference is not length for its own sake. It is that the second prompt gives the model an audience, what that audience already believes, a tone reference drawn from real examples, and a specific constraint on what happens next. A model without that context has nothing to work from except the statistical average of everything it has seen about invoicing features, which is exactly what generic output looks like. Give it real context, and the draft comes back close enough to use with editing rather than close enough to discard.

The same principle applies to every task in the list above. A review-summary prompt that just says "summarise these reviews" will give you a bland paragraph. One that says "these are reviews of a project management tool, I want to know specifically what frustrates our free-tier users versus our paid users, and I want direct quotes, not paraphrases" will give you something you can actually act on.

The mistakes worth avoiding from day one

Publishing AI output unedited is the most common and most damaging beginner mistake. A first draft from a language model is exactly that: a first draft. It needs the same editing pass you would give a junior copywriter's work, checking tone, accuracy, and whether it actually says what you meant.

Trusting AI-generated statistics or facts without verification is the second. Language models generate plausible-sounding text, not verified fact, and a confidently stated statistic or a specific claim about your market can be entirely fabricated. Anything with a number in it, or a claim about a competitor, a regulation, or a market size, needs an independent source before it goes anywhere near a client or a customer.

Using it to replace strategy instead of accelerate execution is the third, and the most consequential. A model can generate content faster than any team. It cannot tell you whether the content strategy is right in the first place. Teams that let the tool make strategic decisions by default, because it is faster to accept a suggestion than to think one through, end up producing more content with nothing new to say. The tool should compress the distance between a decision you have already made and a usable draft, not make the decision for you.

Where this connects to AI search

There is one more thing worth knowing, briefly, without turning this into a different post. The content you produce, with or without AI assistance, is increasingly being read by AI answer engines like ChatGPT and Perplexity before it is read by a human. Structuring what you publish so it answers a specific question clearly, rather than talking about your business in general terms, matters more now than it did two years ago. That is a subject in its own right, covered elsewhere on this site. For now, the practical takeaway is simple: write to answer a real question, whether a human or an AI tool is doing the reading.

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The Hexagram Diagnostic assesses your Intelligence pillar, including how AI tools are or are not embedded in your day-to-day marketing execution. It takes 8 minutes. Run it at adg-advisory.com.