Authors: Sebastian Moreau, Yuki Matsumoto, Adrian Kovalenko, Matteo Ricci, Ananya Kulkarni
Abstract: Digital transformation in human capital management has created complex, distributed ecosystems in which employee data originates from connected devices, cloud platforms, transactional systems, and external intelligence services. Fragmented architectures limit the ability to sense patterns, contextualize signals, and coordinate timely action across SAP SuccessFactors and heterogeneous cloud HCM landscapes. This study introduces a digital nervous system architecture that integrates Internet of Things telemetry, scalable big data infrastructures, and artificial intelligence driven cognition into a unified sensing and response framework. The proposed model organizes system design into sensing layers for real time signal acquisition, transmission layers for streaming and synchronization, cognitive layers for predictive and prescriptive analytics, and response layers for coordinated orchestration across talent, payroll, performance, and compliance domains. A formal Enterprise Signal Latency Index is developed to quantify responsiveness across distributed platforms, alongside a Neural Stability Metric that measures adaptive coherence within the integrated HCM ecosystem. Through architectural modeling and scenario based evaluation, the research demonstrates reductions in signal propagation delay, improved anomaly detection accuracy, enhanced decision synchronization across platforms, and strengthened systemic resilience. The findings establish a scalable blueprint for constructing intelligent, continuously learning digital infrastructures that unify IoT, big data, and artificial intelligence within multi cloud human capital environments.
DOI: https://doi.org/10.5281/zenodo.19104930
