AI Features in Apps: 10 Practical Uses That Make Business Sense

"We want AI in there." We hear this sentence at consultations more and more often. And our first question is always the same: why? Because an AI feature in an app isn't a goal — it's a tool. When it solves a real user problem, it can save hours of work every day. When it's there just so the homepage can wear an "AI-powered" badge, it's expensive decoration.

In this article we go through 10 practical uses of AI that actually work for our clients. For each one you'll find a concrete example from small and mid-sized business practice, an estimate of implementation difficulty and — because we're honest — the situations where you shouldn't do it.

Most of these features run on the APIs of large language models today. That means you don't have to train your own model or hire data scientists: the app sends data to an API and gets a result back. We deliberately don't list specific price sheets or model names here — they change every few months. As an order of magnitude, though, the operating costs of most features below run in the hundreds to low thousands of crowns per month for an SMB app.

1. Natural-language search

Classic search fails when the user doesn't know what a thing is exactly called. AI search understands intent: in a spare-parts e-shop it finds "head gasket for a 2015 Octavia" even without the exact catalogue number; in an internal knowledge base it answers "how do we handle complaints over 10 thousand".

Real-world example: a wholesaler with 8,000 items deployed semantic search and the number of "I can't find it" calls to the customer line dropped by a third.

Difficulty: medium. You need embeddings and a vector database, but it's a well-trodden path.

When not to do it: if you have 50 products, well-named categories are enough.

2. Summarising long texts

An app that works with long texts — inquiries, reports, meeting minutes, reviews — can show the user a three-sentence summary before they open the whole document.

Real-world example: a real-estate CRM summarises the communication history with a client into five bullet points. The agent gets oriented in 20 seconds before a meeting instead of ten minutes of scrolling.

Difficulty: low. One API call with a well-written prompt.

When not to do it: with texts where every word matters (contracts, legal documents) — a summary can tempt you to miss the detail that decides everything.

3. Automatic categorisation and tagging

Manual sorting is exactly the kind of work people hate and AI handles reliably: routing an inquiry to the right department, tagging products, sorting feedback into bug/feature/praise.

Real-world example: a company with 200 e-mails a day lets AI pre-sort inquiries by type and urgency. The salesperson opens a sorted list in the morning instead of inbox chaos.

Difficulty: low to medium — depends on the number of categories and the required accuracy. Expect the system to need an escape hatch: an unsure category for cases where the model isn't confident.

When not to do it: when a wrong category means a serious problem (e.g. triaging medical requests) and you have no human check in the process.

4. Customer support chatbot

Careful — this is the most commonly botched AI feature of all. A chatbot that answers with generic phrases will reliably annoy your customers. But a chatbot connected to your actual data (orders, availability, knowledge base) can resolve 60–80 % of repetitive questions.

Real-world example: an equipment rental deployed a chatbot that can answer "do you have a trailer free this weekend?" by looking at real availability. Phone calls dropped by half.

Difficulty: medium to high. The chat itself is easy; connecting it to your systems and handling the edge cases is the real work.

When not to do it: until your data is in order. A chatbot on top of an outdated knowledge base is worse than none.

5. Extracting data from documents and invoices

Re-typing data from PDF invoices, orders or delivery notes into a system is work nobody has to do anymore. AI pulls structured data out of the document — supplier, amounts, line items, due dates — and pre-fills the form.

Real-world example: an accounting office processes hundreds of received invoices a month. Extraction with a human check cut the processing of one invoice from manual entry to a few seconds of review.

Difficulty: medium. The key is to have a person confirm the result — AI fills in the draft, responsibility stays with the user.

When not to do it: with documents that have critical consequences and no human check. AI occasionally invents a digit, and with invoices that's an expensive mistake.

6. Content and product recommendations

"Customers who bought X also buy Y" is a classic, but AI recommendations can do more: they account for the season, the specific customer's history and what they're browsing right now.

Real-world example: a sports equipment e-shop recommends wax and a service package with skis — not another pair of skis. Average order value grew by single-digit percent, which at their margin translates to tens of thousands of crowns a month.

Difficulty: medium. You can build a simple version on top of order history; sophistication can be added gradually.

When not to do it: with a small catalogue or few orders — the model has nothing to work from and the recommendations feel random.

7. Voice-to-text transcription

Field workers, tradespeople, salespeople in the car — none of them wants to type reports on a phone. Dictating 30 seconds and letting AI create a structured record is an order of magnitude more pleasant.

Real-world example: service technicians dictate their findings after a call-out. The AI transcript is immediately split into "work performed", "materials used" and "recommendations" — and the record is in the system before the technician starts the engine.

Difficulty: low. Transcription quality is excellent today, even with industry slang.

When not to do it: in noisy environments with no chance to correct, or when the record is legally binding.

8. Automatic content translation

Running an app for both local and international users? AI translation keeps both language versions in sync — product descriptions, notifications, articles — at a fraction of an agency's price.

Real-world example: a marketplace with thousands of listings translates offers automatically and labels them as machine-translated. Foreign buyers see the content immediately, not in two days.

Difficulty: low for content, medium for a whole app (the workflow of when and what gets translated).

When not to do it: with marketing copy where tone decides — there, use AI translation as a first draft, not the final version.

9. Generating product descriptions

A hundred new products, each needing a description. AI generates the first version from the parameters (name, category, attributes) and a person polishes it. Three hours of work instead of three days.

Real-world example: a household goods e-shop generates descriptions from supplier data and manually reviews only the top products. New goods are online the same day, not in a week.

Difficulty: low.

When not to do it: never publish generated copy without review for products where a parameter error means returns (dimensions, compatibility, ingredients).

10. Predictive alerts

An app that notices something is happening before the user does: stock will run out in 5 days at the current sales pace, a client has stopped opening the app and is at risk of churning, a machine is reporting anomalies and a failure is approaching.

Real-world example: a fitness studio gets alerts about members whose attendance dropped below half their usual level. The front desk sends them a personal message — and some of them come back instead of cancelling their membership.

Difficulty: medium to high — you need historical data and a well-defined notion of what "a problem" means.

When not to do it: when there's nobody to act on the alerts. A notification without a follow-up process is just more noise.

How to tell which AI feature makes sense for you

Try a simple test. A feature passes if you answer yes to all three questions:

Question	Why it matters
Does it save someone at least an hour a week?	Otherwise the investment won't pay back
Will it survive an occasional AI mistake?	AI gets things wrong — the process must expect it
Do you have the data for the AI to work on?	No data, no intelligence

The warning signs, on the other hand: "the competition has it", "it looks modern", "investors want to hear it". Those are reasons for a badge, not for a feature.

What it costs and how to start

The good news: most features on this list can be added to an existing app one at a time, without rebuilding the whole system. That's exactly what we recommend — start with the one feature with the biggest impact, measure the result, then continue. We wrote about how AI is changing app development itself in our article on the AI revolution in mobile app development.

Technically, the key is a well-designed backend — AI features are at their core API integrations, and everything from our article on the API-first approach applies to them. If you're still planning your app, go through our guide to creating a mobile app — AI features fit into its MVP design step.

Want to know what AI could do specifically for your business? Get an indicative price in our configurator, or book a consultation — we'll walk through your processes and tell you straight where AI helps and where it's just a fashionable word.