AI as a trend, not always as a solution

Lately, I've been observing how many companies and project teams are literally chasing after adding artificial intelligence to their systems, processes, and products. This is understandable, of course — AI is currently one of the hottest trends in the tech industry, and clients often ask about the possibilities of using AI in their projects.

But do we really need complex machine learning algorithms everywhere? Does every business solution require AI models that process data, learn, and make autonomous decisions?

The problem with overengineering

The answer is: no. Not all problems require advanced AI solutions. We often deal with a classic case of overengineering — a situation where we use overly complex tools to solve a problem that can be solved in a simpler, more efficient, and cheaper way.

Examples where AI might be unnecessary:

• Simple automations — if you want to automatically sort files by date, you don't need an AI model. A simple script will do.
• Basic data filtering — classic business rules and SQL queries are often sufficient.
• Simple classifications — if you have a limited number of categories and clear criteria, a decision tree might be enough.
• Processes with predictable patterns — not all processes require learning.

When does AI make sense?

AI is a powerful tool, but it's worth using it where it actually brings value:

• Complex problems where it's hard to write rules — image recognition, sentiment analysis, language translation.
• Data that changes dynamically — when patterns in data evolve and you need a model that adapts.
• Large-scale problems — where classical approaches are not efficient enough.
• Personalization at a level that can't be achieved with rules — recommendations, user experience optimization.

Let's not go down that path

It's not about avoiding AI at all costs. It's about choosing the right tool for the right problem. Sometimes a simpler solution will be:

• Faster to implement
• Cheaper to maintain
• Easier to understand and debug
• Sufficient for business needs

It's not worth adding AI just to tell a client "we have AI." This approach often leads to unnecessary costs, system complexity, and frustration when the solution doesn't meet expectations.

Let's also not go in the direction where we talk about AI, but behind it there's actually "if-ology" — just conditional statements. If a solution is based only on simple conditional rules, that's not artificial intelligence, it's classic business logic. There's no point in wrapping it in a marketing package labeled "AI-powered" when we're actually dealing with simple conditions.

Need help evaluating?

If you're wondering whether AI has any application in your case and if there's even such a need, I invite you to a consultation. Let's try to evaluate together:

• Does your problem really require AI, or might the solution be simpler?
• What are the alternatives and costs of each approach?
• Where will AI actually bring value, and where might it be unnecessary complexity?

Sometimes the best solution is the one that solves the problem efficiently, not the one that sounds the most "cutting-edge."

If you decide to use AI in your project, it's also worth reading: The Value of Prompts: Why Quality Matters So Much — about how important it is to create effective prompts.