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As organizations accelerate their use of AI, many find themselves with too many disconnected pilot projects and too little strategic coordination. In response, this article argues that AI investments should be managed as a portfolio rather than a set of experiments. It presents a step-by-step approach designed to help leaders determine where to start, how quickly to progress, how to balance short-term value with long-term capacity building, and when to stop or redirect initiatives. The portfolio progresses in four stages: 1) Opportunity (overview)where ideas are formulated and sorted; 2) Design and partnership (partner)where dependencies, governance and capacity needs are clarified; 3) Experimentation (Experiment)where initiatives are tested as learning journeys rather than validation exercises; And 4) Scale and operate (Navigate)where solutions are deployed and sustained. One part of the setting that particularly appealed to me was Step 1: opportunityand emphasis on speed “no” decisions. This phase is explicitly designed to quickly identify and eliminate weak, misaligned, or premature ideas, before time, credibility, and political capital are wasted. Based on this, I’m sharing a one page editable spreadsheet I created to help HR managers and their teams identify the AI-enabled and HR-related use cases most likely to drive business value starting with business questions before moving to solutions. While these are just a few sample questions and far from exhaustive, this type of structured questioning can help HR leaders and their teams guide the organization toward more disciplined AI portfolio decisions.