Errors when using AI in eCommerce content

Publishing without reviewing, using a single prompt for everything, losing the brand voice, not checking the data, ignoring search intent, creating content without strategy…

AI can save you hours of work in your eCommerce, but be careful: it can also do you a lot of hurt if you use it without thinking. We are going to tell you what the most frequent errors are, because identifying them is the first step to being able to avoid them.

Artificial intelligence has entered the day-to-day life of many online businesses with a very tempting promise: more content, in less time and using fewer resources.

And this is objectively true. With the right tools you can generate product descriptions, category texts, blog posts and FAQs or informative sheets, at a speed that was previously unthinkable.

The problem is when that ease becomes a trap. Many online store managers start using AI, see that it "works", and move from using it as a tool to depending on it 100% and without any kind of editorial filter.

The result is usually empty content that does not sound like anything, that does not rank well in SEO or GEO either and that, in the worst case, can end up hurting the store's reputation.

Is it a problem of Artificial Intelligence? I would say it isn´t, it is more a problem of how it is used. So, we are going to look, one by one, at the 6 most common errors when using Artificial Intelligence in eCommerce content, so that you can avoid them from the beginning.

This is the most common and also one of the most dangerous. The workflow that you must not replicate is: open the AI tool, write a basic prompt, skim through the result, and click publish.

Think about it with the mindset of just a few years ago: would you publish content commissioned from a copywriter without reviewing it? Probably not, so it does not make sense to do exactly the same thing with ChatGPT, Claude, Gemini… or the model you use.

Anyone with a minimally trained eye (and not to mention search engine algorithms), recognises at a glance that tendency to generate generic phrases, predictable structures and that neutral tone that does not resemble any particular brand and all of them at the same time.

Phrases such as "this high-quality product is designed to satisfy your needs" do not say anything to absolutely anyone and, of course, do not convince a potential customer to buy.

Therefore, there is a problem from the user's point of view, but there is an inconvenience with SEO too (a rather big one, moreover).

Since Google's March 2024 update, the algorithm actively identifies patterns associated with low-effort content: predictable structure, generic information, absence of original perspective. Google does not penalise AI-generated content merely for being so, but it does punish content that does not provide value, regardless of who —or what— wrote it.

The solution is to treat the AI result as what it is: a first draft, not as a final text. You generate, read, correct, add what only you know about your product and your customer, and give it a brand voice that sounds human and specific to your business.

It is very easy to scale with IA. In one afternoon you can generate descriptions for hundreds of products without a problem, but this also requires a certain effort on your part to do it properly. If you use the same prompt for all of them, the result tends to be extremely uniform: same structure, same formulas, same sentence rhythm.

This exponentially enlarges a problem that already existed before AI in eCommerce: duplicate or almost duplicate content. According to an analysis by Semrush, product descriptions generated with the same template appear repeated in an average of 12 different stores. Google chooses one to rank, and if you are not the domain with the most authority, I am afraid it will not be yours.

To avoid it, vary your prompts depending on the type of product, the category and the customer profile it is aimed at. A description for a designer coffee maker aimed at a specialty coffee lover is not the same as one for a basic drip coffee maker for office use. AI can adapt, but it needs you to give it those parameters of role, context, format and to explain what you do not want.

Every online store has (or should have) its own way of speaking to its customers. Close or formal, technical or accessible, with humour or without it... always depending on the positioning it wants to occupy, and the audience it addresses.

Even if you do not believe it, that voice is one of the most valuable assets of an online business because it outlines a communication framework, in addition to generating recognition and trust.

AI, if it is not explicitly instructed, produces an intermediate and boringly neutral tone that does not fit any particular brand. If you have been building a relationship with your customers for some time and your store has its own personality, publishing generic content breaks that coherence in a minute.

The key is prompting again: dedicate time to building a good brand prompt. Describe how your store speaks, what words it uses, what it avoids, who it speaks to. The more specific you are, the better the result will be. It is also very useful to give it real references from your content, your website and social networks. Some platforms such as Gemini also allow you to upload a style guide for it to follow.

And when you have that system prompt worked out, use it as a basis in all content generations. You can turn it into a Gem or a GPT, which are the most efficient way to have context without the need to rewrite from scratch every time.

Language models do not consult databases in real time. They generate text based on statistical patterns, and that means that they can produce false claims with astonishing confidence. In the sector this is known as hallucination.

In an eCommerce, this has direct consequences. Imagine a product sheet in which AI writes that a fabric is "100% certified organic cotton" when it is not, or that a supplement "has been clinically tested" without that being true. Beyond the mess it is for SEO, you are facing a legal and trust problem with your customer.

The rule is simple: never let AI invent technical specifications, certifications, materials or properties of a product. You enter those data yourself, based on the real technical sheet. AI can help you write them in an attractive way once you give it the correct data.

One of AI's strongest points is its ability to generate texts optimised for SEO if you ask it to do so. The problem is that many interpret it as "put the keyword in as many times as you can", what has always been known as keyword stuffing, and that is exactly the opposite of what works.

Search intent is the real reason why someone writes something in a search engine. Someone who enters "waterproof men's trail shoes" in the bar does not want an article about the history of trail running. They want to compare options, see prices and make a purchasing decision. If your description does not respond to that specific intent, it will not rank well, even if it is perfectly written.

Before generating content with AI, ask yourself (and also ask the model), what your customer is really looking for when they arrive at that page. That answer should guide the entire generation of content.

The blog of an online store is a very powerful tool for attracting organic traffic, resolving customer doubts and reinforcing brand authority, there is no doubt about that.

Here AI can help you be more efficient, producing more content in less time, but do not lose focus on the user. Yes, you can generate many posts, but if they do not respond to any real user need, nor is there a coherent editorial plan... it is useless. Even if it is capable of capturing traffic, it will be traffic that does not convert.

Publishing twenty articles about topics vaguely related to your catalogue is not a content strategy. It is noise. And Google increasingly penalises noise, especially when it detects patterns of mass publication without depth or originality.

Use AI to develop ideas that you have previously defined. The editorial criterion (what to publish, for whom, when and with what objective) has to be yours. Artificial Intelligence is quite better at executing than planning.

To sump up, the main error when using AI in eCommerce content is to understand it as an autopilot and not as one more tool.

The best results with AI always come from the same combination: the speed and capability of the model, plus the knowledge that only you have about your product, customer, voice and strategy.

Use it to do more, not to think less.