Early in my management journey, I treated AI as a mere copy editor—a tool to polish my drafts and make my reports sound more professional. However, I quickly realized the limitation: AI is only as good as the context it’s fed. When my data was sparse, the output was superficial.
Recently, with the emergence of more capable AI models and better data infrastructure at work, I decided to shift my approach. Instead of using AI to just “write” for me, I started using it to “understand” my team’s work.
My Data Pipeline: From Noise to Context
I realized that as a manager, it is humanly impossible to monitor every detail. My goal was to build a system that aggregates scattered signals—Jira tickets, GitHub PRs, meeting notes, and chat logs—into a coherent narrative.
Here’s how I structured it:
- Data Extraction: Used MCP (Model Context Protocol) to pull data into my environment.
- Standardization: Converted everything into JSON format for easier AI parsing.
- Connection: This was the toughest part. Manually connecting the dots between Jira tickets and actual progress was difficult due to gaps in our internal workflows.
The Managerial “Aha!” Moment
The data collection was time-consuming and admittedly imperfect. But once the pipeline was in place, the turnaround time for generating insights dropped drastically.
More importantly, this process gave me something I couldn’t get from weekly reports alone: deep empathy for my team’s daily grind. I began to see the invisible effort behind every PR and every ticket, allowing me to understand the “why” and “how” behind their work.
Looking Ahead
This wasn’t just about automation; it was about building Managerial Intelligence. I know where the gaps in our tracking are, and I have a clear vision for the next iteration of this system.
In my next post, I plan to dive deeper into how I refine these data structures to make them even more actionable. If you’re also trying to bridge the gap between technical data and management, I’d love to hear your approach.