The pitch is simple: point a language model at your catalogue and watch the product descriptions write themselves. And to be fair, they do. What used to take weeks of copywriting and translation can now happen in an afternoon. But we keep seeing the same thing happen after that afternoon. The descriptions are fluent, persuasive and confidently wrong, because the data underneath them was wrong first.
The model is only as good as the spreadsheet
A language model doesn't know your products. It knows how products like yours are usually described. Give it a lift height of 4,500 mm and it will write a lovely paragraph about a 4,500 mm lift height. Give it 45,000 mm because someone added a zero, and it will write an equally lovely paragraph about that.
The old problem with bad product data was that it sat quietly in a spreadsheet until a customer spotted it. The new problem is that AI takes the bad data and multiplies it: into web copy, into twelve translations, into the comparison table, into the sales deck. An error that used to live in one cell now lives in fifty places, written in polished prose that nobody thinks to question.
When a value is missing altogether, it can get worse. Ask a model to describe the battery capacity of a product where that field is blank, and unless it's been told otherwise, it may well fill the gap with something plausible. Plausible is the most dangerous kind of wrong.
Where AI genuinely earns its keep
None of this means you should keep AI away from your catalogue. Used properly, it's one of the most useful things to happen to product content in years. The trick is to use it for language, not for facts.
Turning structured specifications into readable copy is a language task, and models are excellent at it. So is adapting tone for different channels, drafting first-pass translations for a native speaker to review, and writing variations for different markets. All of these work brilliantly when the facts come from a structured source the model can't change.
AI is also surprisingly good at the dull side of data work: noticing that one product says "SS" and another says "stainless steel", flagging values that look out of range compared with similar products, or suggesting which category a new item probably belongs in. It shouldn't make those changes on its own, but it can put them in front of a person in seconds.
Where AI fits in product content
Let AI handle
- Turning structured specs into readable descriptions
- First-draft translations for human review
- Channel and market variations of approved copy
- Flagging inconsistent or suspicious values
Keep a source of truth for
- Specifications, dimensions and capacities
- Certifications, compliance and safety claims
- Pricing, availability and compatibility
- Anything a customer could hold you to
Ground the model in data it can't argue with
The setups that work all share one idea: the model writes the words, and the product information system owns the facts. Specifications go in as structured fields, not as a blob of text the model has to interpret. The instructions tell it to use only those fields and to say when something is missing rather than invent it.
Then you check. Not by reading every description, which defeats the point, but by automatically checking that every number in the output matches a number in the source. If the copy mentions a capacity that isn't in the data, it gets flagged before it goes anywhere near a website.
This is exactly why the unglamorous work matters more now, not less. A properly structured catalogue used to be nice to have. Now it's what decides whether AI saves you weeks or quietly publishes mistakes in twelve languages.
A sensible way to start
If you're thinking about using AI for product content, resist the urge to run it across the whole catalogue on day one. Pick a range you know well, where your team can spot a mistake at a glance, and work through it like this:
- Fix the fields first. Make sure the specifications for that range are complete and consistent before any copy is generated.
- Write the rules down. Tone of voice, banned claims, required phrases and units. If it isn't written down, the model can't follow it.
- Check the numbers automatically. Every figure in the output should trace back to a field in the source data.
- Keep people on translations. Machine translation is a strong first draft. A native speaker who knows the products should still sign it off.
- Measure the edits. Track how much reviewers change. When the edits drop, widen the rollout.
The boring bit is the competitive advantage
Everyone has access to the same models now. The difference between a business that gets real value from AI and one that just produces more content, faster, is almost entirely in the data underneath. Clean, structured, well-governed product information is what turns a clever demo into something you'd trust on your website.
So yes, let AI write your product descriptions. Just make sure it's describing the right products.