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MoSCoW Method

A prioritization technique that categorizes requirements into Must-have, Should-have, Could-have, and Won't-have. It creates clear alignment on what is essential for a release versus what can be deferred or dropped entirely.

MoSCoW works by forcing stakeholders to make explicit trade-offs rather than treating every requirement as high priority. Must-haves are non-negotiable for launch. Should-haves are important but the product can ship without them. Could-haves are desirable extras. Won't-haves are explicitly out of scope for the current cycle. This clarity prevents scope creep and keeps teams focused on delivering core value.

When building AI features, MoSCoW helps teams resist the temptation to pursue perfect model performance before shipping. A must-have might be that the model handles the top three use cases with 90% accuracy, while handling edge cases gracefully is a should-have. Growth teams use MoSCoW to ensure launch milestones include essential instrumentation and analytics alongside the feature itself. By explicitly categorizing what won't be included, teams avoid the common AI product trap of delayed launches due to endlessly pursuing higher accuracy on diminishing-return scenarios.

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