From
Sarah Chen
Subject
The preview review queue
Eight small changes and the export-permissions change are marked ready for review. It's good work, but checking all of it myself is about six hours.
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LEADERSHIP SCENARIO
You are an experienced engineering manager at DataFlow. Your team is producing implementation drafts faster with AI, but the review queue keeps growing. A customer preview is approaching, and the senior engineer you have long relied on is spending evenings trying to keep up.
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Eight small changes and the export-permissions change are marked ready for review. It's good work, but checking all of it myself is about six hours.
THE SITUATION
You have an established history with this team. The review habits that supported its earlier delivery now sit alongside a growing flow of AI-assisted work. Sarah has raised the strain as Product prepares for the preview.
This scenario gives experienced managers a focused setting to reconsider familiar working practices. You take part through conversations with the team and its stakeholders, with the consequences unfolding in your inbox.
WHAT YOU'LL PRACTICE
Practice managing delivery pressure when the pace of implementation changes.
Practice direct conversations about work and expectations with people you know well.
Explore how changes in one part of the delivery process affect the rest of the team.
HOW IT WORKS
The scenario begins with a message about the review queue before the customer preview.
Reply from your email inbox in your own words. The dashboard keeps your conversations together. No coding environment is needed.
Continue the email exchanges as colleagues respond to your questions and decisions. Your session shows the story’s timing and delivery settings.
A personalized coaching debrief helps you reflect on your conversations and decisions once the scenario ends.
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The Review Queue is part of Leading AI-Assisted Engineering Teams. Practice the conversations around AI-assisted work in a fictional team.
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