Why we decided to integrate AI into every stage of development

Six months ago, our engineers used AI the way most teams do – as a fancy autocomplete. Someone would ask ChatGPT to explain an error or generate a boilerplate function. It helped, but it felt random. No system, no consistency across projects.

Then we picked one project as a pilot and asked: what if AI wasn’t a side tool, but part of the process itself?

Today, on that flagship project, AI is embedded in every stage of the SDLC:

📋 Requirements — AI explores the codebase and helps generate structured Change Requests with accptance criteria and UI mockups

💻 Development — each Change Request includes a ready-to-use prompt for the AI coding assistant, so implementation starts from a structured spec, not a blank screen

🔍 Code Review — every merge request gets an automated AI review before a human even looks at it

📝 Documentation — AI updates functional requirements, creates test cases, and maintains help pages in two languages

🧪 Testing — AI generates test scenarios from requirements and helps write Playwright tests

It started with a simple question to the team: “Where do you lose the most time on things that feel mechanical?”

The answers shaped our roadmap. Code review was the first target. Documentation — the biggest pain point nobody wanted to own — was the second.

We’re not claiming AI replaced anyone. What it replaced was the tedious, repetitive work that drained our best engineers. The kind of work that made Fridays feel long.

The results from the pilot were strong enough that we’ve already started scaling these practices to other projects. Not everything transfers one-to-one — but the core patterns do.

This is the first post in a series where we’ll share – honestly – what worked, what didn’t, and what we learned along the way.

What’s the one process on your team that feels like it’s begging for AI