1 year of using AI tools in enterprise apps — here’s what I learned

I build enterprise POS Android apps — lots of architecture, lots of business rules, and plenty of “this has to be correct.” 🧱

My AI journey started in a pretty normal place: using ChatGPT to reason through tricky architecture decisions and scaffold the boring-but-necessary code.

Then it escalated fast. 🚀

Once tools like Cursor made “chat-to-code” feel native inside the editor, I noticed a shift. I stopped creating classes manually and started directing how they should be created. Cursor’s agent-style workflows are built to take broader tasks, plan ahead, edit across files, and even run commands — a move from autocomplete to execution.

What I do differently now 👇

• 📚I don’t anymore write files and classes, I write TDD for AI model.
• 🧠 I think in systems, not snippets When code generation gets cheap, architecture decisions get expensive. I spend more time on patterns, boundaries, and long-term maintainability than on low-level syntax.
• 👀 I review everything AI moves fast — and it can be confidently wrong. Reading “foreign code” is now a core skill, not a nice-to-have.
• 📚 Docs are part of the toolchain Strong README files and a /docs folder keep the model grounded in the application’s business logic.
• 🧪 Tests protect me from becoming a human linter I lean on TDD and automated tests to shorten feedback loops and avoid spending my day QA’ing AI output.
• 🧹 I treat AI output like real work in Git Frequent commits, then rebase/squash. Clean history still matters.

Last year for me was basically this: try things, break things, learn the edges. 🔍

The AI space is moving so fast that it feels like every month there’s a new tool raising the bar for what’s possible.