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AI makes software faster to build. Explore how to change ownership, decisions, funding and support so small teams can deliver what customers really need.
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In a previous article, I wrote about how to structure product teams for scale. Each team needs to be autonomous because otherwise the number of dependencies between teams will increase and slow down the delivery of everyone.
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Our team structures need to ensure that the teams are both effective and efficient. Effectiveness comes from a deep understanding of the end users and their needs. Efficiency is measured in the lead time that it takes to go from idea to satisfied customers.
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Since most organisations are structured in departments and functions, the impact of this siloed focus is that most agile implementations actually turn into Water-Scrum-Fall. To gather more data, I ran a poll on LinkedIn to gauge why people felt that this happened in their place o
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Processes are just a collection of best practices based on the context that we are working in. The thing is that the context that we are working in is always changing - so our processes need to change too.
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Research by Ron Kohavi shows that only 10% of features developed at Netflix were delivering the expected business value and only 8% at Airbnb. And these are industry leading tech companies. The results in other industries are likely to be even worse.
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Scrum came to prominence as an off-the-shelf replacement for the waterfall process. People are busy running their companies and building their products so they don't have time to think about software development practices from first principles. Scrum solved this problem by provid
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Team Topologies has quickly become the de facto guide for how to structure technology teams, and more importantly, the ways that the teams interact.
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It has been 23 years since the Agile Manifesto was published, and 20 years since the first book on Scrum was published. In that time, Agile replaced waterfall as the dominant methodology for software development. But despite its popularity, the results are still poor. On-time and
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