A museum district IT team uses AI during sprint planning to review a feature list for a ticketing upgrade. What requirements benefit should they seek?

During sprint planning for a museum district ticketing upgrade, AI helps requirements completeness when the team uses it to spot likely missing non-functional requirements such as accessibility, performance, and audit logging. Feature lists skew toward visible buttons; NFRs quietly determine whether the upgrade is usable, fast enough for on-sale spikes, and auditable for financial or access events. The correct benefit is surfacing candidate NFR gaps for human confirmation and backlog inclusion—not letting the model silently rewrite scope.

Accepting only functional buttons while ignoring latency or accessibility fails because museum ticketing under load and under accessibility law will fail residents and visitors even if every button exists. Letting AI silently remove security requirements to shrink scope fails by concept: generative assist must not delete controls that protect payments, personal data, or admin functions. Banning discussion of non-functional needs because they slow sprints fails because unstated NFRs become production incidents that cost far more than planning time.

Exam caveat: AI NFR suggestions can be generic; staff must translate them into measurable acceptance criteria (e.g., WCAG target, p95 latency under peak, retention of audit events) tied to this museum’s stack and policies. Unused generic lists create false comfort.

Operational check: run the feature list through an AI NFR gap pass, have product and security owners accept or reject each suggested NFR, write measurable stories, and refuse sprint commitment on payment or account features that still lack accessibility, performance, and logging criteria.