10 Developer Tasks You Can Already Delegate to AI

Not that long ago, AI in software development felt like a fancy autocomplete. Nice to have, sometimes impressive, but not really essential.

That’s no longer the case.

If you’re writing code every day and not using AI at all, you’re basically choosing to work slower. Not because you’re a bad developer — but because some parts of the job simply don’t require a human brain anymore.

AI doesn’t design architectures, doesn’t make final decisions, and doesn’t take responsibility for production issues. What it does handle extremely well is the stuff that:

• repeats over and over;

• drains your energy;

• gives you zero satisfaction.

Those are the tasks we’re talking about.

DTOs, entities, controllers, services, mappers — if you’re a backend developer, you already know the drill. There’s barely any logic, but it still eats up time.

Today, you can just tell AI:

"Create a REST controller for a User entity with basic CRUD operations."

A second later you’ve got a solid starting point.

Is it perfect? No.
Do you still need to adjust it to your project? Of course.
But 90% of the boring work is already done.

What used to be “writing code” is now more like review, tweak, and move on.

Unit tests matter. We all know that. Still, very few developers actually enjoy writing them, especially when it’s not complex business logic but simple service checks.

This is where AI shines. You give it a service class, and it generates reasonable tests, covers the obvious cases, and often points out edge cases you didn’t think about.

You shouldn’t blindly commit what it gives you — but as a starting point, it’s incredibly effective. Hours of work shrink down to minutes.

You open a method. It works. Tests are green. But reading it feels like walking through mud.

Rewriting it from scratch feels risky. Leaving it as-is feels wrong.

AI is surprisingly good here. It can take messy but functional code and clean it up — shorter methods, better naming, clearer structure — without changing the behavior.

You still make the final call, but getting a fresh outside perspective often helps more than you’d expect.

You’ve got the stack trace. You’ve got the code. You’ve even got coffee. Still no idea what’s wrong.

AI works like that one patient coworker you can dump logs on and say, "Explain this." It breaks down the error, points out likely causes, and often gets you dangerously close to the real issue.

Even when it’s not 100% right, it usually breaks the mental deadlock — and that alone is huge.

New project. Legacy system. Code written three years ago by someone who no longer works there. Classic situation.

AI is great at translating “what on earth is happening here” into plain English. You drop in a class or method and ask what it does and why it exists.

It won’t replace real understanding, but it dramatically lowers the entry barrier, especially during onboarding.

README files, JavaDoc, comments — everyone agrees they’re important. Almost no one enjoys writing them.

AI handles this beautifully. It can generate clean, readable documentation that explains what a class or method does without drowning in fluff. It’s especially useful before a release or when handing a project over to another team.

You can use AI as a practice interviewer. It asks questions, challenges your answers, and forces you to explain why you made certain choices.

The same goes for code review. Ask AI to critique your solution and see what it points out. Not every comment will be valid, but some will be genuinely useful.

It’s like having a sparring partner that never gets tired.

Complex joins, nested queries, weird performance issues — even if you know SQL, this stuff can get messy fast.

AI is good at reading queries, explaining what they actually do, and suggesting simpler or more efficient versions. Especially when you’re trying to answer “why is this slow?” rather than “how do I write SQL?”

If you write articles, create courses, or mentor other developers, AI can save a ton of effort. It’s great at generating consistent, progressive examples instead of random snippets that don’t connect.

That saves time — and more importantly, mental energy.

Most technical documentation is in English, and not always friendly English. AI doesn’t just translate — it adapts. It can simplify complex language, adjust tone, and make content feel more natural for a specific audience.

For blogs, courses, and internal docs, this is incredibly useful.

AI isn’t taking developers’ jobs. It’s taking the boring parts of the job.

And that’s a great deal.

The less time you spend on repetitive, draining tasks, the more time you have for:

• architecture;
• problem-solving;
• real engineering decisions.

In the end, the developers who win aren’t the ones who fear AI — they’re the ones who know how to use it well.