A home goods retailer we work with imported 400 new SKUs from a supplier catalog dump last spring – ceramic planters, in a dozen sizes and glazes, with nothing but a part number and a dimension sheet to go on. Their copywriter quoted three weeks to write unique descriptions for all of them. They tried an AI drafting tool instead, and the results were genuinely mixed: fantastic for the planters where size and glaze were the only variables, oddly generic for the handful of statement pieces that needed a real point of view.
That split is roughly what we see across most catalogs. The AI vs. human question isn't really "which one wins" – it's "which SKUs need which approach," and getting that split wrong is where most catalog projects lose money either way.
What AI actually does well
Given structured attributes – material, dimensions, weight, color, use case – a large language model produces a competent first draft in seconds. It's consistent: description 214 reads with the same tone and structure as description 3, which matters when a copywriter working through 400 items late on a Friday inevitably starts to drift. It's also cheap at scale; the marginal cost of the 401st description is close to zero once the prompt template is built.
What AI still gets wrong
AI drafts tend to hedge into vague superlatives when the input data is thin – "a versatile addition to any space" is what you get when the only input is "ceramic planter, 30cm." It also has no opinion. A skilled copywriter knows that the statement piece needs to lead with the emotional hook ("the centerpiece your dining table has been missing") while the utility SKU needs to lead with the spec ("holds up to 4L of soil, drainage hole included"). Left unprompted, AI treats both the same way. It can also invent details that sound plausible but aren't in your source data – a real risk for anything involving safety claims, certifications, or exact measurements.
A head-to-head comparison
| Dimension | AI-generated | Human-written |
|---|---|---|
| Speed for 400 SKUs | Hours | 2-3 weeks |
| Cost per description at scale | Very low (API cost) | Higher (hourly/per-word rate) |
| Tone consistency across large batches | High | Variable, depends on writer fatigue |
| Brand voice and emotional hook | Weak unless heavily prompted | Strong |
| Risk of factual invention | Present, needs review | Low if writer has correct source data |
| SEO keyword coverage | Good with the right prompt | Good, but slower to iterate |
| Handling of nuanced/luxury items | Often generic | Usually stronger |
Where a hybrid workflow beats both
The AI-draft-then-edit pipeline
The merchants getting the best result run every SKU through an AI draft first, then route descriptions into two buckets: a light-edit queue for straightforward, attribute-driven products, and a heavy-rewrite queue for hero products, gift items, and anything where brand voice carries real weight. On the planter catalog, that meant roughly 340 SKUs got a 90-second polish pass and 60 got a proper rewrite from the copywriter – a project that would have taken three weeks took four days.
- Feed the model structured attributes, not just a product name
- Give it two or three example descriptions in your actual brand voice as a style reference
- Flag any category with safety, medical, or certification claims for mandatory human verification
- Run a plagiarism/duplicate-content check across the batch before publishing
- Keep a human sign-off step before anything goes live
Does AI-generated copy hurt your SEO?
Not inherently, but sameness does. If ten stores selling the same supplier's ceramic planters all run the identical product feed through similar AI prompts, you can end up with near-duplicate descriptions across competing sites – which is a real risk for dropship and marketplace-sourced catalogs specifically. Search engines assess usefulness and originality, not authorship. Thin, unedited, templated output underperforms whether a human or a model wrote it. For more on how AI is reshaping the discovery side of this, see AI search is changing SEO.
The EU AI Act angle on AI-written content
Product description drafting itself isn't a high-risk AI use case under Regulation (EU) 2024/1689, and there's no legal requirement to label ordinary AI-assisted product copy as machine-generated. The transparency obligations that do exist target synthetic media (AI-generated images/video presented as real) and chatbot interactions – covered in our piece on how AI will transform PrestaShop stores. Ordinary text drafting sits outside that requirement, though GDPR still applies if your prompt pipeline processes any personal data, which for product copy it typically doesn't.
Building a workflow that uses both
Sort your catalog into two piles before you touch a drafting tool: attribute-driven SKUs (color/size variants, spec-led products) go into the AI-draft-then-light-edit pipeline, and hero or brand-voice-heavy items go straight to a human writer with AI drafts as optional reference only. Measure hours saved on the first pile before deciding how far to extend the approach.
Frequently asked questions
Will AI-written product descriptions get flagged as duplicate content?
They can, particularly with supplier-fed catalogs where competitors run the same source data through similar tools. Rewriting the structure, adding store-specific details, and varying sentence patterns per product reduces this risk more reliably than switching tools.
How much editing does AI-generated copy typically need?
For straightforward, attribute-driven products, usually a light pass of one to two minutes to fix tone and remove vague filler phrases. For hero products or anything emotionally positioned, expect a substantial rewrite rather than a quick edit.
Can AI write accurate technical specifications?
Only as accurately as the data you feed it. AI models will fill gaps with plausible-sounding but unverified details if your source data is incomplete, so always supply exact specs and flag anything the model shouldn't infer.
Is it cheaper to use AI for product descriptions long-term?
Usually yes for large or frequently refreshed catalogs, since the API cost per description is a fraction of a copywriter's per-item rate. For small, curated catalogs where every description matters for brand positioning, a human writer often remains the better investment.
Do I need to disclose that product descriptions were AI-assisted?
No current EU requirement covers ordinary AI-assisted product text specifically. Disclosure obligations under the AI Act target chatbots and synthetic media, not standard catalog copy.
Should every product on my store use the same approach?
No. Treat it as a spectrum: commodity and variant-heavy SKUs suit AI-first drafting, while flagship, seasonal, or story-driven products benefit more from a human writer working from scratch.
Related reading
- How AI Will Transform PrestaShop Stores
- AI Search Is Changing SEO: What Merchants Need to Know
- Core Web Vitals for PrestaShop
- How the EU AI Act Will Affect Online Stores
- AI Customer Support Trends for eCommerce
