Is AI always necessary?

Like many people today, I use AI actively in my work. It is genuinely useful: it helps me move faster, reduces routine work, and generally improves productivity. But it is important to treat AI as exactly that — a tool, not a replacement for a person. It does not fully understand the context, is not responsible for the outcome, and still needs critical review from an engineer. Its usefulness depends directly on how and where it is applied.

There is another important trait of modern AI systems to keep in mind: they are optimized to be helpful and to "please" the user. In practice, that means a model will try to suggest a solution even when no valid solution exists. I once ran into a good example of this. AI was trying to solve a problem that, under the given conditions, simply had no correct solution. It proposed different approaches and generated large chunks of code, but it never stopped and said that the task was unsolvable as stated. That is an important limitation: AI is not always able to recognize where its answer no longer applies or say "no" at the right moment. Critical evaluation of the result remains the engineer's job.

To be fair, the opposite also happens. Sometimes AI really does help you break out of your usual way of thinking. I had a case where I had already exhausted almost all the obvious options and, more as a long shot, asked AI what else I could try. It suggested an approach I had not even considered — and that turned out to be the one that worked. It was also much simpler and faster than everything I had tried before. Moments like that show one of AI's real strengths: it can suggest non-obvious ideas and act as a kind of amplifier for engineering thinking.

But back to the main point. Right now we are in a kind of AI boom, where AI is being added to almost every product, regardless of whether it is actually needed. Sometimes it gets absurd. I came across a joke video with a project manager, a sales rep, and a client, where the client explicitly asks for a solution without AI. In the end, they still "add AI for free," simply because that is what everyone does now and because it is easier to sell. Even though it was a joke, it captures the current market pretty well: AI is increasingly becoming a marketing label rather than a deliberate engineering choice.

Do I use AI in my own products? Yes, I do. For example, in Alemity, it helps rewrite and adapt text for different languages. At an early stage, this reduces the cost of professional translation and makes it faster to launch content in several localizations. The important part is that AI is solving a specific practical problem with clear value, and its output remains under control and can be edited manually when needed.

But there is a category of products where AI may not just fail to help — it can introduce additional risk. This is especially true for infrastructure products, such as Adal. In systems like this, the key requirements are predictability, reliability, and a guaranteed result. A user expects a request to be processed in a strictly defined way, without "guessing" or variable behavior. Adding AI to such critical parts of the system introduces uncertainty: the result may depend on context, interpretation, or even random factors. In the end, that does not strengthen the product. It reduces trust in it.

In this context, a simple practical principle emerges: AI is appropriate where variation in the result is acceptable, and inappropriate where strict determinism is required. If a system must produce a predictable, reproducible, and verifiable result every time, adding AI only increases the chance of deviations and errors. Conversely, in tasks where multiple valid answers are acceptable, and where speed, ideas, or flexibility matter, AI can significantly enhance a product. This approach lets you use AI deliberately, as a tool, rather than treating it as a universal answer to every problem.

So the real question is not whether to use AI or not. The question is where and why to use it. Adding AI does not make a product better by itself. Like any technology, it creates value only when it solves a specific problem. Otherwise, it is just added complexity, more uncertainty, and more potential points of failure.

Good engineering is not about using the trendiest tools. It is about making well-founded decisions. Sometimes that means adding AI. And sometimes it means deliberately choosing not to.