Low-Hanging Fruit Strategy

This approach deliberately prioritizes solving the immediate, achievable 90% of problems rather than pursuing ambitious but uncertain moonshot projects. The strategy recognizes that most AI initiatives focus on high-risk, high-reward goals while neglecting practical, solvable problems that affect daily productivity and organizational effectiveness. By isolating specific problems, exploring existing patterns, and determining precise data requirements, the method creates a pragmatic development framework that delivers measurable value. Examples include synchronizing information after absences, extracting tacit knowledge for onboarding, and processing shared research materials. This philosophy suggests that sustainable AI advancement comes from accumulating many successful, focused solutions rather than betting everything on transformative breakthroughs.