Case 02 / AI and PMO automation
GateTurning repeated PMO work into a reusable workflow
Parallel business lines made weekly reporting, sprint analysis, and risk review increasingly repetitive. I used Feishu Bitable, Meegle, MCP, and AI Skills to structure the inputs while PMs kept the final judgment. I also connected keyword research, content production, and performance review into one SEO workflow.
Project essentials
I built AI-assisted workflows for weekly reporting, sprint analysis, risk review, and SEO operations across parallel business lines.
I structured the data, defined risk fields and output templates, and connected keyword research, content production, and performance review.
Inputs were spread across teams and tools. Automation also had to preserve the PM’s final judgment on quality, priority, and risk.
Report and analysis drafts could be prepared in about ten minutes, SEO work gained a continuous loop, and repetitive consolidation dropped significantly.
How the AI-assisted workflow divides responsibility
Project data enters a shared structure first. Tool access, draft generation, and final judgment each have a clear owner.
Connect tools and data
Gives AI controlled access to project data and approved tools.
Run repeatable steps
Structures information, flags anomalies, and prepares report or analysis drafts.
Validate and decide
Checks quality, weighs priority and risk, then chooses the communication and delivery action.
Automation handles structure and drafts; PMs retain judgment, decisions, and communication
At a glance
Background
As more business lines ran in parallel, the cost of collecting, organizing, and reporting information rose quickly.
Challenge
Requests lived across tools and teams. Weekly reports, sprint analysis, and risk detection still depended on manual consolidation.
My role
I designed an AI-assisted workflow that organized project data, demand flow, and report drafts before PM review.
Result
Weekly reports, sprint analysis, and risk-review drafts could be prepared in about 10 minutes, leaving PMs to validate the output and make the final calls.
Reflection
AI works best here as operating support. It handles structure and first drafts, while judgment, trade-offs, communication, and follow-through stay with people.