Probably everyone who has used ChatGPT or another language model to write marketing copy in recent years has experienced this exact moment at least once. You type in a topic, ask for an article, wait a few dozen seconds, and receive several thousand characters written in perfectly acceptable language.

There are subheadings. There is an introduction. There are benefits. There is a summary. Sometimes even a call to action.
Except, after reading the whole thing, it’s hard to answer what we actually learned.

The text is correct, but it could easily sit on the website of a software provider, a training company, a bank, or a local shop. You’d just need to swap out a few nouns.

And that is precisely what I call marketing fluff.

The issue isn’t that AI writes poorly. Quite the opposite. Modern models can write very fluently. The problem arises when we ask them to create content without providing the material needed to build something truly ours.

“Write me an article about…” is probably not enough

You can, of course, prompt the model: “Write an expert article about the most common mistakes in B2B marketing.” You w

Will get a text. You will likely read about a lack of strategy, not knowing the target audience, inconsistent communication, improper measurement of results, and the need for regular optimization. Is any of that untrue? Not really. Is that precisely why similar sentences can be found on thousands of websites? Yes. A language model is exceptionally good at generating responses that fit patterns present in its training data and the context it receives. So, if it gets a very generic task—lacking information about the company, the audience, the author’s role, and real-world experience—the most natural outcome will be an equally generic text.

Paweł Tkaczyk points out a similar issue: AI handles tasks with a clear pattern, first drafts, variations, or editing very well, but performs much worse where an author’s authentic point of view or an original perspective is needed. That is why the problem with average content rarely starts with the model. More often, it starts with the brief.

Before you ask AI for a text, answer what you actually have to say

This is a step that is very easy to skip because AI offers the tempting possibility of starting work from the end.

Instead of gathering information, establishing a thesis, and finding examples, you can simply ask for a finished article. Except that is when the model starts inventing the content strategy as well. Imagine an article by a Google Ads agency about why campaigns aren’t generating sales.

We could send just the topic to the AI. Or, we could first gather insights from a specialist about five accounts where campaigns struggled, see what they had in common, ask the sales team about lead quality, and only then create the material based on that. In the first version, the model will likely describe typical errors in Google Ads.

In the second, you might get a text containing a line like: “In four out of five accounts we analyzed, the problem wasn’t campaign configuration. The ads were driving traffic, but the offer was more expensive than the competition’s and gave the customer zero reason to buy right here.” That is the difference between content generated on a topic and content that actually adds value. AI can be a great help in drafting the second version. However, it cannot invent a company’s real experience out of thin air.

The best prompt won’t replace good input

An entire cottage industry has grown around prompt engineering, and it sometimes feels like finding a complex enough prompt will magically turn ChatGPT into a great strategist, copywriter, and industry expert all at once. A good prompt certainly helps. It should specify the target audience, communication goal, brand context, format, tone, the knowledge the model can draw from, and constraints.

It’s best to treat it like a brief for a copywriter rather than a magic spell. Tkaczyk also notes that a command like “write a post about my product” doesn’t tell the model who it’s actually writing to, what that person’s hesitations are, or what stage of the buying process they are in. However, what you attach to that brief is even more crucial.

If you feed the AI: a transcript of an interview with an expert, survey results, sales team feedback, customer reviews, an excerpt from a strategy workshop, campaign data, examples of strong brand copy, and credible sources, then the model has real material to work with. If you only provide the phrase “write an article about solar energy for businesses,” even the best prompt cannot manufacture knowledge that wasn’t provided in the first place.

Smartbuzz rightly suggests a similar workflow: first supply data and business context, then use AI to analyze it, structure it, and build a draft.

AI is a pretty good researcher, but it shouldn’t be your only source

One area where AI truly speeds up work is research. It can help organize a long report, compare multiple sources, find contradictions between materials, build an initial list of interview questions for an expert, or point out missing areas in a drafted outline.

You can upload customer survey results and ask it to cluster recurring pain points. You can feed it dozens of SEO keywords and ask it to group them by intent. Finally, you can give it several sources and ask it to highlight the most relevant points for a specific target reader. This is a far better use of AI than asking: “What are the latest statistics

Models can state false information with convincing confidence. They can mix up dates, study authors, figures, or simply invent a source out of nowhere. Both Widoczni and Smartbuzz point out hallucinations and reliability issues as primary risks of using AI for content creation. That is why our process should follow a simple rule: AI can help read the sources, but it cannot replace them. If a text contains a specific number, legal change, statistic, or platform update, verify it at the source.

Thesis first, text second

This is one of the simplest ways to eliminate marketing fluff. Before the article is written, try answering this question in one or two sentences: What are we actually trying to say with this piece? Not: “The article will be about using AI in marketing.”

That is a topic. A thesis would be: “AI doesn’t make companies produce better content. It makes them produce the content they were already capable of making, faster. If the process was weak before, you can now simply produce weak content at a larger scale.” Immediately, you know what kind of article will come out of that. AI can later help find counterarguments, examples, structures, or weak points in the reasoning.

But the stance itself should come from the author or the brand. This highlights one of the biggest differences between using AI as an author versus an assistant. An assistant helps you better articulate what you have to say. An author would first have to decide what is worth saying in the first place.

Don’t ask AI to write everything all at once

Another mistake stems from convenience. If the model can generate ten thousand characters in a few minutes, why not ask for the whole article right away? You can. But then you get a text where every section feels like it carries equal weight. Sections end up similar in length, the arguments remain safe, and the entire structure becomes predictable. A better workflow looks different. First, gather research and raw materials.

Then, define the thesis. Next, build an outline. Check if anything is missing and if the flow makes sense. Only after that do you work on specific sections. Smartbuzz recommends a similar approach: breaking the task down into stages rather than generating the entire piece with a single prompt—starting with an outline and only then developing individual sections.

This allows you to stop the process at any point and say: “No, this section adds nothing. Let’s cut it,” or “We have our own case study here, and that needs to be the anchor of this section.” AI stops dictating the structure of the text. It simply helps execute it.

Feed AI samples of your real brand voice

Giving an instruction like “write naturally and professionally” can mean almost anything. It is far more effective to show the model five pieces of text where someone on your team said: “This is exactly how we want to sound.” Then, it can analyze sentence length, argument structure, humor, vocabulary, heading styles, directness, or how often the author uses examples.

Widoczni highlights the necessity of defining communication tone and providing sample brand content to the AI. At the same time, even after this calibration, they recommend human editing before publication. However, one distinction is critical here. Brand voice isn’t just a list of five adjectives. “Professional, expert, friendly, dynamic, and authentic” describes half the internet today. A much more useful instruction sounds like this: “Write to a business owner like a practical expert who knows the topic inside out. Don’t explain the obvious.

Feel free to challenge popular assumptions. Use concrete examples. Avoid phrases like ‘in today’s fast-paced world,’ ‘crucial importance,’ and ‘it’s worth remembering that.'” Suddenly, the AI knows how to adapt.

The first AI draft should be material for editing, not publication

This might be the single most important rule of the entire process. Just because the model prepared a text in three minutes doesn’t mean the article is ready after three minutes. The first draft lets you skip the blank page syndrome.

You can work on it, move sections around, cut weak parts, insert data, swap examples, and strengthen arguments. Widoczni lists drafting as one of the strong use cases for AI, but immediately stresses the need for independent research and adding your own expertise. A great editing exercise is testing every major section with a simple question: Could our competitor publish this exact same paragraph? If the answer is “yes,” something unique to you is missing. It could be an example, a specific figure, a customer story, a takeaway from a campaign, an expert opinion, a counterintuitive argument, or a real situation you observed.

Not every paragraph has to be revolutionary, but if the entire article could belong to any company in your industry just by swapping the logo, you can’t expect it to build your brand.

“Humanizing” text isn’t about adding a few casual words

With the rising popularity of AI came a whole category of prompts like “humanize this text.” Sometimes they work. Usually, only superficially.

The model swaps “essential” for “important,” adds a few rhetorical questions, shortens sentences, and throws in phrases like “the truth is…” The text starts to look less formal, but there still isn’t a human in it. True humanization comes from things the model cannot know on its own: details, observations, flaws, real experiences, and strong opinions. If a copywriter has run a hundred Google Ads campaigns, they can tell you which mistake they actually see every week versus the ones that mainly exist in online guides. If a sales representative talks to clients daily, they know how customers actually describe their problems—often completely differently from how the company labels them on its website.

If a business owner made a costly mistake when launching a new product, that story is far more compelling than five more “best practices.” AI can help articulate those stories well. It cannot live them for you.

Use AI where humans genuinely waste time

It would be a mistake to conclude that AI should sit quietly in the corner correcting commas. On the contrary. It excels at many stages of the content process. It can generate ten headline variations once your thesis is locked down. It can summarize a long interview and cluster quotes by topic. It can catch repetitive phrasing in an article, suggest questions you forgot to ask an expert, or draft three different CTA options.

It also handles repurposing high-value content into other formats extremely well. A single strong article can be turned into a series of LinkedIn posts, a newsletter, a short video script, or a set of topics for future pieces. Smartbuzz highlights content repurposing and personalizing distribution as key areas where AI dramatically cuts execution time. Here, scale is a massive advantage.

You have one great piece of source material, and AI helps leverage it ten times over. That is a completely different scenario from generating ten articles out of thin air.

Does Google punish AI-written text?

This question still comes up surprisingly often. The simple presence of AI in the content creation process is not an issue for Google.

In its guidelines, Google explicitly states that generative AI can be helpful during research and organizing original content. The problem starts when automation is used for mass production of pages that offer no value to the user. Google’s policy on scaled content abuse covers creating large volumes of unoriginal pages primarily to manipulate search rankings. Crucially, Google doesn’t care whether that content was produced by a model, a human, or a mix of both. What matters is its quality and intent. In its recent guidance on generative search results, Google boils the topic down to a very simple question: Will a visitor coming to the page be satisfied with what they found? And that is a great benchmark for more than just SEO.

AI belongs in the process, not in the author’s seat

The best workflow today can be described as human-in-the-loop. The human makes decisions, provides knowledge, and evaluates output, while AI helps execute individual steps faster.

It can look like this: Expert identifies a problem → AI helps structure the research → Human selects the thesis → AI proposes an outline → Author refines it and adds proprietary material → AI drafts selected sections → Human edits → AI helps trim text, spot repetition, and generate headline variations. Smartbuzz describes a similar collaboration loop between a specialist, AI, and a copywriter, emphasizing that the model relies on data and instructions provided by the human.

In practice, AI can be present at almost every stage. That doesn’t mean it should make every decision.

AI’s greatest advantage shouldn’t just be publishing more

This is perhaps the ultimate paradox. For years, the bottleneck in content marketing was the cost of content creation. A good article required research, interviewing experts, copywriters, editors, and time. AI dramatically lowered the cost of generating text that looks like an article. As a result, the internet isn’t facing a content shortage. It is facing an even greater surplus.

If every company can generate thirty articles a day answering the most obvious client questions, simply owning those thirty articles stops being a competitive advantage. The real advantage will be owning what cannot be generated from a generic prompt: your own data, real experience, domain experts, a recognizable tone of voice, a unique perspective, and the courage to write something different from the top five Google results. That is why AI shouldn’t primarily be used to produce more content. It should allow us to eliminate mechanical work faster so we have more time for the parts that truly require a human touch.

If implementing AI leads you to publish twice as much, but everything sounds identical to your competitors, you’ve increased production—not marketing impact. If, on the other hand, research takes you two hours instead of six, analyzing an expert interview takes minutes, and you invest the saved time into refining your arguments and examples, then AI genuinely improves your process. And that is likely the best way to think about it. Not as a copywriter to be left alone with a task, but as a lightning-fast collaborator capable of handling heavy lifting—provided someone on the other end knows why the work is being done in the first place.

If you want to leverage AI in content marketing without turning your company’s communication into another stream of identical, generic text, we can help you structure the entire process. At Panda Marketing, we combine strategy, client expertise, and AI capabilities so that technology speeds up your content workflow rather than replacing its underlying meaning. If you are looking for support in content creation and brand communication, let’s work together.