It’s hard to find a developer today who doesn’t use AI assistants in their daily work. The interesting question is no longer “do you use AI?”, but “what do you use it for, and what shouldn’t you use it for?”.
Where AI adds real value
Generating repetitive code. Forms, standard components, test scaffolding, initial project setup: mechanical tasks where AI saves hours without introducing risk, because the result is easy to review.
Debugging and explaining errors. Pasting an error and asking for an explanation greatly speeds up understanding what’s going on, especially with less familiar libraries or frameworks.
Documentation and comments. Generating documentation from existing code, or explaining in plain language what a complex function does, is one of the most solid applications of AI in development.
Guided refactoring. Asking an AI to suggest how to simplify a function or split an oversized component helps spot improvements that sometimes go unnoticed after staring at the same code for too long.
Where AI doesn’t replace human judgement
System architecture. Deciding how to structure a database, how to split responsibilities between services or how to design a system to handle future growth requires understanding the business, not just the code. An AI can suggest patterns, but it doesn’t know your company’s real priorities.
Security. AI-generated code can introduce subtle vulnerabilities — incomplete validation, insecure credential handling, accidental data exposure — that only someone with security judgement will catch before they reach production.
Business decisions with technical impact. Choosing which technology to use, how much to invest in infrastructure, or when to migrate a legacy system are decisions where business context outweighs pure technical capability.
Final review. AI-generated code always needs human review before it reaches production. Accepting suggestions without understanding them is the fastest way to build up invisible technical debt.
The real risk: code that “works” but nobody understands
The most common problem we see isn’t AI generating incorrect code, but generating code that works yet nobody on the team fully understands. Over time, this turns into a fragile system: any small change can break something in an unexpected place, and nobody knows exactly why, because nobody consciously designed that part of the system.
How we apply it in practice
At IbizaWebSolutions we actively use AI to speed up mechanical tasks — boilerplate, tests, documentation — but every line that reaches production goes through human review, and architecture decisions are made by whoever understands the whole project, not by an assistant that only sees isolated fragments of code.
AI is an extraordinary tool for moving faster. It’s no substitute for knowing where you’re going.