Integrating human capital analytics into strategic decision-making has moved from an optional HR capability to a firm-level competence that shapes productivity, innovation, risk management, and long-term value creation. Yet evidence remains uneven across industries, with many organizations still treating analytics as a reporting function rather than a decision system embedded in strategy cycles. This paper develops and tests a cross-industry empirical model explaining how analytics maturity converts workforce data into strategic choices and measurable outcomes. Drawing on survey and archival indicators from organizations in manufacturing, services, finance, healthcare, and information technology, the study operationalizes human capital analytics maturity across four layers: data governance and integration, analytical capability (descriptive to predictive), decision integration (use in planning, budgeting, and operating reviews), and ethical assurance (privacy, transparency, and bias controls). Using structural equation modeling with multi-group invariance tests, we estimate direct and mediated effects from analytics maturity to decision quality, strategic alignment of workforce investments, and organizational performance, while examining contextual moderators such as industry dynamism and firm size. Results are expected to show that decision integration is the pivotal mechanism: analytics improves outcomes primarily when insights are translated into portfolio choices on hiring, reskilling, mobility, and retention, and when line leaders co-own metrics and accountability. Cross-industry comparisons further clarify that data governance is a stronger prerequisite in highly regulated sectors, whereas advanced modeling differentiates performance in fast-changing sectors. The study contributes a parsimonious measurement instrument, evidence on boundary conditions, and an actionable pathway for executives to institutionalize people analytics as part of strategic management—linking human capital investments to business priorities with traceable, ethically responsible evidence. Practically, the paper proposes a repeatable governance-and-review cadence, including KPI trees that connect workforce drivers to financial and customer outcomes, and guidance for building cross-functional analytics teams that sustain adoption beyond pilot projects in diverse organizational settings.