Generative AI for e-commerce: use cases, risks and rollout

A generative model writes or edits content based on an instruction and the input you give it. In e-commerce that input is mostly product data: a supplier file, a spec sheet, a photo, an existing description. The output is only as good as that input. A model that receives a product name and a price will fill the rest with plausible text, and plausible is not the same as correct.

That is why the most useful AI work in a store is close to the product data. Writing a description from twenty verified attributes is a safe, repeatable task. Writing one from a product name alone is guesswork. The same goes for translation, categorization and image text: the more structured facts the model reads, the less it has to invent.

The table lists the use cases that matter most for online stores. The last column is the part you should not hand over: it is where errors cost money, trust or compliance.

Most AI problems in e-commerce are predictable, which means you can design around them before the first batch runs.

The EU AI Act, Regulation (EU) 2024/1689 on EUR-Lex, entered into force on 1 August 2024 and applies in stages. According to the European Commission, its transparency rules apply from August 2026. They sit in Article 50.

In paraphrase, Article 50 says three things. Providers must design AI systems that interact directly with people so that those people are informed they are dealing with an AI system, unless that is obvious. Providers of systems that generate synthetic audio, images, video or text must mark the output in a machine-readable format as artificially generated or manipulated. Deployers must disclose deepfakes, and AI-generated text published to inform the public on matters of public interest, unless the text has undergone human review or editorial control and a person holds editorial responsibility. This paraphrase is not legal advice: read the article itself and ask your legal adviser how it applies to your store.

Start small and on real products. A pilot on one category teaches you more than a vendor demo on a sample catalog.

Measure your own catalog before and after, instead of relying on someone else's average. Compare the pilot category with a similar category you did not change over the same period, so a seasonal swing does not look like an AI effect.

Expect content changes to show in search data over weeks, not days. Look at trends per category rather than single products, and write down what changed and when, so you can link a shift in the numbers to a specific run.