As artificial intelligence becomes integral to business operations, knowledge workers across industries face pressure to develop basic AI coding competency. A new framework is emerging to help non-engineers navigate this transition by identifying software-shaped problems already embedded in their work—tasks that could benefit from automation, enhancement, or novel solutions built with AI tools. The approach centers on practical decision-making: once a problem is identified, workers must choose between three strategies. Automating streamlines existing workflows using AI; upgrading improves current tools and processes; inventing creates entirely new capabilities. Rather than requiring deep technical expertise, the methodology emphasizes recognizing opportunities and selecting the right strategy for each situation. The key to building competence lies in selecting a meaningful first project—one that solves a real problem in the worker's domain while remaining achievable with current AI tools. This hands-on approach to learning AI coding reflects a broader shift in workforce development, where these skills are no longer confined to software engineers but essential for anyone managing data-intensive or process-heavy work.