Cognitive Self-Service Analytics: Intelligent Architecture For AI-Augmented Decision Intelligence In Enterprise Environments

Abstract

Contemporary enterprises face a structural tension between rapidly expanding data assets and the cognitive bandwidth constraints of centralized analytical teams. This paper presents a systematic examination of cognitive self-service analytics platforms as intelligent sociotechnical systems that leverage machine learning, natural language processing (NLP), semantic reasoning, and explainable artificial intelligence (XAI) to democratize analytical decision-making across organizational levels. The platform architecture is analyzed across five interconnected layers — intelligent data integration, AI-augmented semantic modeling, adaptive visualization, governance and explainability, and collaborative knowledge systems — and evaluated against their impact on decision velocity, resource reallocation, and enterprise data literacy. Drawing on peer-reviewed empirical evidence from medium and large organizations, the paper identifies research gaps in human-in-the-loop (HITL) governance, AI-generated insight explainability, large language model (LLM)-assisted query interfaces, and conversational business intelligence frameworks. Findings confirm that governed cognitive analytics deployments achieve measurable improvements across financial, operational, and market performance dimensions. The paper positions cognitive self-service analytics within the descriptive-predictive-prescriptive analytics maturity continuum and outlines future research trajectories including autonomous insight generation, context-aware streaming intelligence, and federated governance architectures.