The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in employment, worker management, and access to self-employment as high-risk under Annex III. Such systems attract stringent obligations for risk management, data governance, transparency, human oversight, recordkeeping, and conformity assessment. A fast-growing class of enterprise systems sits ambiguously against this category: external workforce-intelligence platforms that convert public labour-market signals, such as job postings, competitor movements, and skill-demand shifts, into aggregate strategic guidance. These platforms inform workforce planning at the level of roles, functions, and populations rather than making decisions about identifiable individuals. The boundary between high-risk employment AI and governed strategic decision support is, however, neither self-evident nor consistently applied in practice. This paper develops a classification framework that distinguishes workforce-intelligence architectures falling within the Act's high-risk employment scope from those that constitute defensible decision support. Following the design-science research paradigm, and drawing on the derogation criteria in Article 6(3), it analyses four system archetypes and identifies five architectural properties that materially affect classification and defensibility: an aggregation boundary, deterministic and reproducible processing, explain-only outputs, human-in-the-loop escalation, and end-to-end provenance logging. The properties are instantiated on a generalised reference architecture, translated into an operational compliance checklist, and extended into a practice layer that covers vendor and product assurance, effectiveness and bias testing across the lifecycle, post-deployment monitoring and re-classification triggers, and an operating model of roles with worker-representative consultation. The paper offers HR leaders, system designers, and governance functions a practical instrument for the Act's phased enforcement, and sets out a research agenda for responsible workforce intelligence.