- The most rewarding uses are product copy, support drafts, data cleanup, translations and summaries: lots of routine, low cost of errors.
- The effort is small: a good tool, clear guidelines and one person who reviews the results.
- AI is not worth it for tiny volumes, and never where mistakes are expensive and nobody checks.
Affects you if you run a small business or an online shop and want to cut down on routine work.
01Product copy and descriptions
The classic. From bullet points about material, dimensions and special features, a description text appears in seconds, and you only polish it. Effort: build one good template, then minutes per product. Benefit: especially with many similar articles this saves hours per week, and the texts get more consistent, not worse.
02Draft replies in support
AI does not answer your customers, it writes drafts that you approve. Where is my parcel, how do returns work, does size M fit: such requests are 80 percent routine. Effort: phrase the most common cases properly once. Benefit: faster replies in consistent quality, and the tone stays friendly, even on a stressful Monday.
03Data cleanup and categorisation
Bringing unstructured data into shape: unifying product data, sorting requests by topic, cleaning address records. Unspectacular, but this is often the biggest lever, because this work otherwise simply stays undone. Effort: define clear rules and check samples. Benefit: clean data that your shop, search and reports can build on.
04Translations
Getting product texts and standard emails into a second language used to be its own budget. Today AI delivers usable raw versions that a person with language skills only reviews. Effort: low, as long as somebody can read the target language. Benefit: an additional market becomes realistic without agency prices. Legal texts remain the exception, they belong in professional hands.
05Reports and summaries
Long documents, reviews, support histories: AI summarises and pulls out recurring topics, say the three most common complaint reasons of the month. Effort: provide the data and ask the right questions. Benefit: you decide based on what is actually there instead of gut feeling. Numbers still get recalculated in a spreadsheet.
06Where it is not worth it
- Tiny volumes: with five product texts a month, maintaining templates costs more than writing yourself.
- High cost of errors without review: prices, legal texts and medical claims must not leave an AI unchecked.
- One-off tasks: automating something that never comes back is tinkering.
- As a substitute for missing processes: AI speeds up workflows, it does not repair them.
The rule behind it is simple: AI pays off where lots of routine meets a low cost of errors. The further you move away from that, the more review effort piles up, until the gain tips over.
Also keep the AI Act in mind: AI in customer contact must be recognisable, and everyone working with it needs documented training. There are separate posts on labelling and the training duty here in the wiki.
- For one week, note which writing and sorting tasks keep coming up.
- Start with one use that has high volume and low cost of errors, usually product copy or support drafts.
- Build a template with tone, mandatory details and examples instead of phrasing everything fresh each time.
- Set one rule: nothing reaches customers unchecked.
- After a month, take honest stock: time saved versus effort, then expand or stop.
Wondering what this means for your project?
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