Requirements engineering commonly distinguishes between functional and non-functional requirements. In AI-based systems, an orthogonal perspective might also be worth considering: whether requirements concern AI components or not.

This paper reports on a 2.5-year industrial research and development project in which we designed and built MONEKTO, a holistic data-driven recruitment system for a Polish temporary employment agency. The system integrates three data streams: recruitment process events, candidate and job order profiles, and employment histories - to support both candidate-order matching and employee retention prediction.

Drawing on project logs covering June 2023 to January 2026, we identified three categories of requirements in AI-based systems: Non-AI-based requirements, AI-based requirements, and AI-enabling requirements.

We show how these categories differ in their lifecycle: while traditional requirements stabilize after implementation, AI-enabling requirements emerge incrementally and require continuous investment throughout the system lifetime. We report five observations on the emergent nature of AI-enabling requirements and derive seven practitioner lessons for RE in AI-based system projects.