Most teams use some combinations of Features, Stories, and Tasks to manage their work, but while some teams use this hierachy to laser focus the delivery of awesome software to their users, other teams struggle to deliver anything their customer want. In working with a large number of teams, I've identified some common patterns in how successful teams manage their work along with pitfalls that can trip up even the best intentions. In this sessions we'll explore these patterns and discuss ways that you can guide your team toward better results through more intentional story management practices.

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The goal of this session is to show how to use AI to build real software without losing your mental model of the system.

This is a live demo starting from a problem domain chosen on the spot.

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Traditional Agile often prioritizes work based on the constraints of development teams and resources. AI automation makes some work trivial, and this session shows one practical way to sort backlog items into AI-ready and non-AI-ready categories based on many factors, such as risk, reward, prior disasters of similar types of work, etc.

The workflow demonstrated shows a new way of thinking about backlog prioritization in an AI world, and what I've learned over the past six months of applying these principles.

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