Incorporating generative AI models, particularly large language models (LLMs), into software engineering and software acquisition processes are being investigated with a head spinning speed. Opportunities are plentiful; however, naturally, LLMs must be applied judiciously and responsibly, with attention to potential biases, error margins in novel situations, the clarity of user query interpretation, and the ethical implications of their deployment. Capabilities of generative AI will continue to progress; however, it will not supplant human involvement but rather complement it. Based on our work with many organizations, the Carnegie Mellon University Software Engineering Institute has adopted a broader perspective and formulated several dozen use cases for using generative AI in common software engineering and acquisition activities. Deciding which use cases would be most feasible, beneficial, or affordable is a non-trivial decision for those organizations just getting started with generative AI technologies. In this talk, I will summarize our experience of developing these use cases and an approach for assessing suitability of applications to enable responsible and intentional use.