When chaos becomes method: AI and GitHub to manage a project nobody wanted to touch

A client calls me. Tense voice, measured words.

“Ivan, we have a problem. Actually, we have the problem.”

I know that tone. It’s the tone of someone who has already tried to fix things internally, failed, and is now looking for someone to tell them the truth without beating around the bush.

The problem is a management software — not a website, not an app — a critical system running important business processes. It’s a few years old. It grew fast, as always happens when business runs faster than architecture. And now everything has piled up: open bugs that nobody closes, change requests that nobody plans, developers working on different versions of the code without knowing what the others are doing.

The classic scenario that, on paper, “works.” But inside, it’s a minefield.

🧠 The first meeting: understanding what’s really broken #

When I enter a project like this, I don’t look at the code first.
I look at the people. I look at how they communicate. I look at where information gets lost.

The team was made up of four solid developers. Serious. Competent.
But they worked like this:

• the code lived on a shared network folder
• changes were communicated via email or on an Excel spreadsheet
• bugs were reported verbally, in chat, via tickets — with no consistent method
• nobody knew for certain which was the “good” version of the software

And you know what happens in these situations?
Everyone is right from their own point of view. But the project, as a whole, is out of control.

The problem isn’t technical. It’s organizational.
And that changes everything.

📌 The proposal: GitHub as the project’s backbone #

The first thing I put on the table was clear, direct, no frills:

We adopt GitHub. All code goes through there. No exceptions.

It’s not about trends. It’s not because “everyone does it.”
It’s because GitHub solves, with concrete tools, problems that no Excel spreadsheet can ever manage:

• Real versioning : every change is tracked, commented, reversible
• Branches and Pull Requests : every developer works on their own copy, then proposes changes to the team — they don’t overwrite each other’s work
• Integrated issue tracker : bugs and feature requests live in the same place as the code
• Complete history: who did what, when, why

I saw the senior developer’s face. A mix of curiosity and skepticism.
“But we’ve always done it this way.”

I answered calmly: “I know. And the result is the reason I’m here.”

I didn’t say it to provoke. I said it because it’s the truth.
And the truth, when said the right way, doesn’t offend. It liberates.

🔬 The second step: AI as an accelerator, not a replacement #

Once the workflow on GitHub was defined — branches, reviews, controlled merges — I made my second proposal.

“Let’s integrate artificial intelligence into the bug resolution process.”

Silence.

I understand the reaction. When you say “AI” in a room full of developers, half think of ChatGPT generating random code, the other half think you’re telling them their job is no longer needed.

Neither of those things.

What I proposed is very different:

• When a developer picks up a bug, before writing a single line of code, they use AI to analyze the context
• The AI reads the code involved, the logs, the problem description
• It proposes hypotheses. Not definitive solutions — reasoned hypotheses
• The developer evaluates, verifies, and then implements

AI doesn’t replace the programmer.
AI saves them the first two hours of analysis — those hours spent reading code written by someone else, trying to figure out what on earth is going on.

And those two hours, multiplied by every bug, every developer, every week, become a number that changes the project’s economics.

📊 The numbers I put on the table #

I didn’t sell dreams. I presented conservative estimates.

The team handled an average of 15-20 bugs per week.
Average resolution time was about 6 hours per bug (between analysis, fix, testing, deploy).

With the introduction of GitHub + AI into the workflow, my estimate was:

A reduction of over 90% in total resolution time.
A 12x increase in the team’s capacity to close tickets.
Without hiring anyone. Without changing the people. By changing the method.

🛠️ How it works in practice #

The workflow I designed is simple. Intentionally simple.

1. The bug arrives as an Issue on GitHub
Clear title, description, priority label. No more emails, no more chat.

2. The developer creates a dedicated branch
fix/issue-234-vat-calculation-error — the name says it all.

3. Before touching the code, they query the AI
They pass it the relevant code, the error, the context. The AI returns a structured analysis: where the problem might be, which files are involved, which tests to verify.

4. The developer implements the fix
With a huge advantage: they already know where to look.

5. Pull Request with review
A colleague reviews the code. Not for formality — for quality.

6. Merge into the main branch
Only after approval. The “good” code stays good.

7. The Issue closes automatically
Complete traceability. From problem to solution, everything documented.

📈 What changed after three weeks #

The first days were the hardest. Always are.
New tools, new habits, the temptation to go back to “how we used to do things.”

But after three weeks, something happened.

The senior developer — the one who had looked at me with skepticism — wrote to me:

“Ivan, yesterday I fixed a bug that last year had blocked me for two days. With AI, it took me forty minutes. Not because the AI wrote the code. But because it showed me right away where the problem was.”

There it is. That’s the point.
AI doesn’t write better code than an experienced developer.
AI accelerates the path between the problem and understanding the problem.

And understanding is always the most expensive step.

🎯 The lesson I take home #

Every time I enter a struggling project, I find the same pattern:

1. Competent people
2. Inadequate tools
3. Absent or informal processes
4. Growing frustration

The solution is never “work harder.”
The solution is work differently.

GitHub isn’t a tool for developers. It’s a tool for teams.
AI isn’t a toy. It’s a competence multiplier.

But neither works if there isn’t someone looking at the project from above, understanding where the hours are being lost, and having the courage to say: “Let’s change.”

💬 To those who recognize themselves in this story #

If you’re managing a software project and you see yourself in what I’ve described — code scattered everywhere, bugs that keep coming back, a team that works hard but closes little — know that it’s not the people’s fault.

It’s the fault of the system they work in.

And the system can be changed.
It must be changed.

You don’t need revolutions. You need precise choices, implemented with method.

A shared repository. A clear workflow. An intelligent assistant that accelerates analysis.

Three things. Three decisions.
That transform chaos into control.

Glossary #

Pull Request — Formal request to incorporate changes from a branch into the main branch, with mandatory code review. The mechanism that ensures “good” code stays good.

Version Control — System that tracks every code change, maintaining complete history. Git is the standard; GitHub adds collaboration on top of Git.

Issue Tracker — Integrated tracking system for bugs and feature requests. On GitHub, issues live in the same place as the code, with traceability from problem to solution.

Code Review — Code review by a colleague before merge. Catches bugs, improves quality and spreads codebase knowledge across the team.

Branch — Independent development line that allows working on isolated changes without affecting the main code until approved merge.