Hello, fellow project managers!
Today, I want to share a story that might sound familiar to some of you. This isn't a guide filled with textbook advice — it's an honest reflection on how we, a team with experience in IT but no deep background in AI development, tried to integrate AI into our development workflow. Spoiler: it was challenging, eye-opening, but incredibly rewarding.
Where It All Began: "Let's Just Build an Internal Dashboard!"
In 2023, our company needed a simple internal dashboard for tracking project and financial metrics. The stack was pretty standard: Redmine for tasks (and finances, because why not), Metabase for visualization, and a developer who'd have to wear many hats — unless we could offload some of the work to an AI.
Our Redmine setup had been growing organically for years. Which is a nice way of saying: the documentation was... let's call it "aspirational." Some API parts weren't documented at all. So, instead of burning time manually digging through code, we dumped our plugins and custom extensions into Cursor and said, "You figure it out."
And it did. Cursor read our spaghetti and came back with surprisingly accurate documentation. For the first time in years, we knew what our system was actually doing. That alone felt like a win.
The Experiment: "What Happens When AI Joins the Team?"
The next step was to see what happens when a developer gets tasks — but solves them with an AI assistant. Here's how it went: first, our dev estimates the task the usual way, no AI. Then, does it again, this time using Cursor or similar tools as a co-pilot.
Spoiler alert: AI crushed the basics. Setting up boilerplate, generating simple CRUD stuff — it was like watching a robot sprint through what usually takes hours. But once we hit more complex logic, things slowed down. Not because the AI got lazy — but because you have to really know what to ask. Like, really know. Writing good prompts became a craft.
And that's where the real lesson hit us.
AI can't juggle too much context. Give it a small, focused piece of the puzzle? Works like a charm. Feed it the whole codebase and a vague request? It freaks out. Which is fair, honestly — most juniors would, too.
So we started treating the AI like a junior dev. Tasks got smaller. Contexts got tighter. We spoon-fed it just enough info to do the job, and it performed way better.
What We Learned: The Good, The Bad, and The Ugly
The Good: Speed and Consistency
The first thing that blew our minds was how fast AI could generate boilerplate code. What used to take us 2-3 hours of repetitive typing now took 30 minutes. Standard CRUD operations, API endpoints, basic UI components — AI churned them out like a machine. And the documentation! Remember that spaghetti code we had? AI read through it and created comprehensive docs that actually made sense. It even found patterns we didn't know existed in our own codebase.
Code review became less painful too. AI caught the obvious stuff we sometimes missed: missing error handling, inconsistent naming, potential security holes. It was like having a very thorough junior developer who never gets tired.
The Bad: Context Limitations
But here's where things got tricky. When we asked AI to handle complex business logic or understand our legacy code patterns, it started to struggle. It would produce solutions that looked perfect on paper but didn't fit our specific context. Like that time it created a beautiful authentication system that would have required us to rewrite half our existing infrastructure.
Integration was another headache. AI would build perfect components that just didn't work with our existing system. It was like trying to fit a square peg in a round hole — the piece was beautiful, but it just didn't fit.
And then there was the whole prompt engineering thing. We quickly realized that writing good prompts is an art form. You can't just say "build me a dashboard" and expect magic. You have to be specific, clear, and sometimes downright pedantic.
The Ugly: The Learning Curve
The human side was the hardest part. Some of our developers were terrified that AI would replace them. Others were so excited they expected it to solve world hunger by lunchtime. We had to manage expectations on both ends.
Quality assurance became a new challenge. AI-generated code looked correct but sometimes had subtle bugs that were hard to spot. We had to develop new review processes and testing protocols.
And don't get me started on dependency management. AI loved suggesting the latest and greatest libraries, but our production environment needed stability, not bleeding-edge features.
What We'd Tell Other Project Managers
If you're thinking about bringing AI into your development process, here's what we wish someone had told us before we started.
Start small. Don't try to revolutionize your entire workflow overnight. Pick one thing — maybe documentation generation, or unit test creation, or simple code review assistance. Get good at that before moving on to the next thing. We started with boilerplate generation and it was the perfect entry point.
Set clear boundaries. We learned the hard way that you need rules about when and how to use AI. We decided that any task taking less than 4 hours manually was fair game for AI assistance. Everything AI produces gets reviewed by a human — no exceptions. And we created templates for common prompts so we're not reinventing the wheel every time.
Train your team. This is probably the most important part. AI tools are only as good as the people using them. We ran workshops on prompt engineering, made sure everyone understood what AI can and can't do, and created a shared knowledge base of what works and what doesn't.
Measure everything. You need to know if this is actually helping or just creating more work. We track time savings, bug rates, deployment success, and team satisfaction. Regular surveys help us understand how AI is affecting developer experience. And yes, we calculate ROI — because at the end of the day, this needs to make business sense.
Talk to your team. Be honest about what AI is and isn't. It's a tool, not a replacement. Show your developers how AI can free them up to work on more interesting problems. Position it as a way to learn new technologies faster, not as a threat to their jobs.
The Numbers Don't Lie
After three months of this experiment, we had some pretty impressive results. We cut the time spent on boilerplate code generation by 40% — that's almost half the time we used to spend on repetitive tasks. Our documentation coverage improved by 60%, which meant new developers could actually understand our codebase without needing a PhD in archaeology.
Code reviews for basic issues became 25% faster, and our developer satisfaction scores went up by 15%. Most importantly, we got simple features to market 30% faster. When you're competing in a fast-moving industry, that kind of speed improvement is gold.
Hindsight is 20/20
Looking back, there are definitely things we'd do differently. We should have created a more structured onboarding process for AI tools. Instead of just throwing everyone into the deep end, we could have run hands-on workshops with real project examples, set up a mentorship program pairing AI-experienced developers with newcomers, and done a gradual rollout starting with the most enthusiastic team members.
We also learned the hard way that prompt management is crucial. We should have created a centralized library of proven prompt templates from day one, put our prompts under version control to track improvements, and done some A/B testing to see which prompt approaches work best for different types of tasks.
Quality assurance was another area where we could have been more proactive. We should have established AI-specific testing protocols, set up automated validation of AI outputs against our project standards, and scheduled regular audits of AI-generated code quality.
What's Next?
Based on what we've learned, we're planning to expand AI usage to more complex tasks as the tools get better. We want to integrate AI into our CI/CD pipeline for automated code analysis, maybe even develop custom AI models trained on our specific codebase and patterns. And who knows, maybe we'll create AI-powered project management tools for task estimation and resource allocation.
The Bottom Line
AI is a tool, not a magic solution. It can significantly improve your development efficiency, but only if you manage it properly, train your team, and integrate it thoughtfully into your existing workflows.
Start with low-risk applications like documentation, testing, and simple code generation. Don't try to solve world hunger on day one. Invest in your team's AI skills — the success of this whole experiment depends on how well your people can work with these tools.
Measure everything. Track the impact on productivity, quality, and team satisfaction. You need to justify the investment, and numbers speak louder than words. And stay flexible — these tools are evolving so fast that what works today might be obsolete tomorrow.
Conclusion
Our AI experiment taught us that artificial intelligence can be a powerful ally in software development, but it's not a magic bullet. Success requires careful planning, proper training, and ongoing management.
The key is to treat AI like any other team member — with clear expectations, proper onboarding, and regular feedback. When done right, AI can help your team focus on what they do best: solving complex problems and creating innovative solutions.
Would we do it again? Absolutely. In fact, we already are — but now with better rules, tighter scopes, and way fewer "wait, what is this AI even doing?" moments.
So yeah, AI is here. But if you want it to help you build — you'll need to learn to be a better manager. One who speaks fluent "prompt."
P.S.
If you're considering AI integration in your development process — start small, measure everything, and remember that the goal is to make your team more effective, not to replace them.
As we learned:
"AI is like a very smart intern who never sleeps, but still needs clear instructions and regular supervision."
Andrei Gorlov