A Better Way to Make Sense of It All

Productivity

Every Wednesday, my team updates their own OneNote notebook where I can check for project updates to learn where there has been progress or where we may be going off-track. It is a simple system, but it grew cumbersome as the team began to grow, forcing  me to make sense of the progress from week to week. I found myself spending more time trying to piece together what all those updates meant than actually thinking about the work itself.

At first, I assumed the solution might be a project management tool or some new reporting process, but after exploring a few different platforms, most were either too complex for our needs or not optimized for collaboration and reporting. My staff were already doing the hard part; they were documenting accomplishments, challenges, risks, and next steps every week. Asking everyone to change how they worked would create a new burden without necessarily improving visibility.

That raised a different question: what if AI could help organize the information we already had?

We started experimenting with a workflow that would read weekly updates and highlight what actually changed from one week to the next. One of the first things we discovered was that AI struggled for the same reason people did: everyone organized information a little differently. The projects were there, but inconsistent headings and formats made it difficult to reliably connect related updates over time. Rather than introducing a new tool, I asked the team to make a small adjustment to how they documented their work by using a more consistent structure. The improvement was immediate. Once the information followed a common format, AI could organize it much more effectively.

To make the workflow more reliable, we split the repetitive work to be handled by two specialized agents: one focused on pulling the information together and another created the weekly summary I received every Wednesday. The goal was to spend less time assembling information and more time understanding it.

What surprised me most was where the value appeared. I expected to save time, and I did. Reviewing the weekly leadership update now requires about 45 fewer minutes each week. The bigger benefit was the perspective it created. When information was scattered across individual updates, it was difficult to recognize patterns and problems. Once updates were organized consistently, it became so much easier to see which projects were moving forward and where my attention might be needed.

Many groups assume they need more data before they can gain better insights, but in our case, the information already existed. What was missing was a practical way to connect it and view it as part of a larger picture.

"I thought I was building a better status report. What I was really building was a better way to see the work we were already doing."

The lesson I'd share with others is simple: before creating a new process, take a closer look at the information you already have. Many of us aren't struggling with a lack of information; it's the visibility. Sometimes AI is most valuable when it helps people understand what's already in front of them. 

_{The example, details, and outcomes described in this article were provided by the contributor. AI tools were used to help organize and draft the article, which was reviewed and validated by human staff to ensure accuracy, quality, and appropriate oversight.}