AI marketing, honestly.

AI marketing is the use of AI systems to do the work of a marketing team: deciding what to say, writing it, designing it, scheduling it and reading the results. It is broader than an AI writing tool. A writing tool hands you a paragraph, whereas an AI marketing system owns the loop from strategy to published post to what you learned from it.

An agent runs a cycle rather than answering a prompt. It observes your brand and your channels, decides what is worth saying this week, writes and art-directs the content, schedules it, publishes it and reads the performance back into the next cycle. The distinction that matters is autonomy: a tool waits to be asked, an agent works while you are not looking.

No, and the difference is context. A chatbot knows what you typed into it; an AI marketing system knows your site, your product catalogue, your past posts, your brand rules and what performed. That context is the entire reason the output sounds like you rather than like a language model.

Keep a human approving the work until the voice is proven. Feed the system real evidence (your site, your posts, your products) rather than adjectives in a prompt. Never let an image model re-imagine a product you actually sell. Composite the real photograph instead. Maintain a do-not-say list, because the default register of a language model is now recognisable to readers. Rotate content formats, since sameness is what gives automation away. Ground every factual claim in something you really have. Post at a cadence a human could sustain. And close the loop by measuring what shipped against what actually worked.

Eventually, but not on day one. The failure mode of autonomous marketing is not a bad post. It is a bad post you never saw. The safe pattern is to start every brand in manual review, read the drafts until they stop surprising you, and then opt into autonomy deliberately and reversibly, rather than having it on by default.

Give the system your actual brand inputs rather than a description of them, keep an explicit do-not-say list, and hold it in a review queue until the drafts stop needing edits. Brand consistency is not a prompt you write once; it is a profile the system maintains and you correct.

Constrain it to evidence you have supplied and reject anything it cannot source. Models asked to be persuasive will confidently produce a statistic, a testimonial or an award that does not exist. The discipline is that a claim without a source does not ship. The post goes out without the claim.

At a cadence a competent human could sustain: typically a handful of posts a week per channel, not dozens a day. The temptation of AI is volume, and volume is precisely what the platforms already suppress. The output should look like a well-run brand, not a content farm.

Because a language model with no context falls back on its own default register: the words that appear in the most marketing copy it has ever read. That is where 'elevate', 'unlock', 'seamlessly' and 'game-changer' come from. The cure is not a better prompt but better inputs: real brand evidence, plus a banned-phrase list that forces the model off its defaults.

It can generate a picture of something that resembles your product, which is usually worse than useless. Image models invent plausible objects, so a generated version of your bottle is subtly not your bottle. The correct technique is compositing: generate the scene, then place your real product photograph into it, so the item on screen is the item you actually ship.

Not for being AI-generated. Google's stated position is that it rewards helpful, people-first content regardless of how it was produced, and that what it acts against is scaled, low-value content made to game rankings. In practice the risk is not the tool but the temptation. Mass-producing thin pages is penalised whether a human or a model wrote them.

Publishing something false, publishing something off-brand, and publishing too much. The first is a hallucinated claim nobody checked; the second is the model's default voice leaking into yours; the third is treating cheap generation as a reason to flood your channels. All three are governance problems, not model problems, and all three are solved by review, real inputs and restraint.

Ask what it reads before it writes. A tool with no access to your site, products and past posts cannot sound like you. Ask whether it can publish natively or just hands you text to paste. Ask whether review is the default or an afterthought. Ask what it does with a product photograph. And ask what happens after a post goes out, because a system that cannot read its own results is a generator, not a marketer.

Against the same things you would measure a human marketer against: reach, engagement, and whatever conversion actually pays you. The one metric to distrust is volume. The useful question is not how much the system produced but which of its angles earned attention, and whether that finding changed what it produced next.