During Business Understanding for the maintenance-manual knowledge assistant, the PM wants to speed up drafting candidate use-case questions and synthesizing stakeholder-interview notes. How can generative AI reasonably accelerate this phase of the project?

Leveraging generative AI to accelerate project timelines is an explicit Business Understanding enabler, and its correct application here is as a drafting and synthesis accelerator: generative AI can quickly produce candidate use-case questions, summarize stakeholder-interview notes, and draft scoping documentation, which the PM then reviews, edits, and validates with the actual stakeholders. This speeds up administrative and drafting overhead without removing human judgment from the process. The choice that automatically trains a fully functional fraud-detection model in parallel confuses accelerating a planning phase with actually building a separate, unrelated production model — generative AI drafting scoping notes does not train a fraud-detection system. The choice that replaces the need for stakeholder interviews entirely misapplies the accelerator; stakeholder interviews themselves gather context and buy-in that generative AI cannot substitute for, since it can't independently know Meridian's specific operational realities without being told. The choice that generates the final production training dataset without human review is inappropriate and risky — production training data must be validated and reviewed, not generated and used unreviewed, especially this early in the lifecycle. The correct scope of GenAI acceleration in Business Understanding is administrative and drafting support, not decision-making or data generation.