Is it worth adding AI to your app?
It is worth it when AI solves a task your users already do today, but slowly or tediously, and when an occasional mistake does no harm. If both conditions are present, the odds of getting a return are good.
It is not worth it when AI is added just to make the app look modern. A chat assistant sitting on top of a sign-up form that could be handled with three buttons is almost always worse than the three buttons.
My rule is simple: remove the AI from the feature description and read it again. If it still makes sense as a problem ("customers take too long to find the right product"), move forward. If the sentence stops making sense, you don't have a problem, you have a wish.
Where AI inside the app tends to work well
The best uses are those where AI helps a person get faster to a result that they can verify themselves. The person stays in charge, and the AI saves time.
Here are a few examples that tend to make sense:
- Summarizing long texts, such as support history or visit notes, for people who need to read them quickly.
- Suggesting a reply for the support agent to review and send, instead of sending it automatically.
- Extracting data from documents, such as invoices, payment slips, or receipts, and filling in the form for the user to check.
- Natural-language search inside a catalog or the company's own knowledge base.
- Classifying and prioritizing requests that arrive in volume, such as support tickets or quotes.
Where it becomes a cost with no return
The first case is the generic assistant: an open chat that answers anything. It costs money per use, invites users to test its limits, and doesn't solve anything specific to your business.
The second is AI making decisions on its own in sensitive areas: approving credit, giving health advice, calculating taxes, handling legal matters. Here the risk of a wrong answer delivered with an air of certainty is high, and the responsibility is yours, not the model's.
The third is replacing a clear rule with AI. If the discount is 10% for customers who have bought more than three times, just write the rule. It's cheaper, faster, and always gives the same result.
What happens under the hood when an app "has AI"
In most cases, the app doesn't have a model inside it. It sends the text or file to an external service through an API and receives a response. This changes three things you need to ask about.
Cost: you usually pay based on usage volume, not a flat fee. A feature that users trigger ten times a day costs very differently from one they trigger once a week. Ask for an estimate based on expected usage, and ask what happens if usage doubles.
Data: whatever you send leaves your system. If it includes personal, financial, or confidential data, you need to know where it goes, whether it is stored, whether it is used to train models, and whether this complies with Brazil's LGPD and with what you promised in your privacy policy.
Dependency and slowness: if the external service goes down or changes its rules, the feature stops working. So the app needs to stay useful without the AI, with a clear message instead of a frozen screen.
How to test the idea before integrating it
Before paying for integration, validate on a small scale. This is the path I recommend:
- Write the task in one sentence: "the user does X and the AI returns Y."
- Take twenty to thirty real examples, without sensitive data, and run them manually in a standard AI tool.
- Mark how many results you would accept without editing, how many with adjustments, and how many you would throw away.
- Decide what the app does when the response is bad: a redo button, manual editing, an alternative path.
- Only then design the screen and simulate the flow before programming.
See the flow working before you decide
The most overlooked point is the experience of using the AI: how long the user waits, what they see while waiting, how they correct a mistake. You get a much better sense of this by clicking through than by reading a specification.
A clickable prototype is made for exactly this. At SKAD, the R$ 99 AI-generated prototype lets you see your app's screens and flow, including where a feature like this would fit, along with a rough estimate, before any development decision.
Frequently asked questions
Does every app need artificial intelligence?
No. Most apps solve the problem well with clear screens and simple rules. AI only pays off when it automates a specific task that the user already does and can verify.
Does integrating AI into the app make development much more expensive?
It depends on the scope. There is the cost of developing the integration and the recurring cost of using the service, which varies with volume. Always ask for an estimate of each one separately.
Is it safe to send my customers' data to an AI?
Only if you know where the data goes, how it is handled, and whether this is covered by the LGPD and your privacy policy. When in doubt, send only the minimum necessary or anonymize it first.
What should I do when the AI gives a wrong answer inside the app?
Plan for this from the start: let the user review and edit, offer a redo option, and keep a manual path available. In sensitive areas, the final decision should always be human.
Conclusion
Before you sign up for any AI feature, pick a single task your user performs, test it with twenty real examples, and write down what would go wrong. If the success rate doesn't convince you, it won't convince your customer. If it does, simulate the flow in a prototype, confirm the cost per use, and only then request development.