Autonomous BI: Designing Self-Healing and Self-Explaining Enterprise Reporting Systems With AI

Abstract

Enterprise business intelligence (BI) systems are undergoing a structural shift: rather than serving as passive reporting layers requiring constant human interpretation, they are evolving into autonomous platforms capable of self-monitoring, adaptive recovery, and machine-generated insight. Yet most published work either addresses isolated pipeline reliability or individual analytical capabilities, leaving a gap in holistic architectural design. This paper proposes a multi-layered autonomous BI architecture that systematically integrates self-healing data pipelines with self-explaining AI components across ingestion, transformation, serving, and orchestration layers. We formalize anomaly detection algorithm selection for enterprise KPI characteristics—distinguishing univariate seasonal metrics from correlated multivariate sets—and present a tiered detection stack combining Seasonal-Trend decomposition (STL), Isolation Forest, and LSTM autoencoders with SHAP-based attribution. We further propose a multi-stage NLP pipeline with explicit confidence-scoring contracts for intent recognition, entity extraction, semantic parsing, and narrative generation. A multi-signal priority scoring function integrates statistical deviation, business impact, temporal urgency, and user relevance to personalize dashboard content. A benchmark analysis using the Numenta Anomaly Benchmark (NAB), Yahoo Webscope S5, and Spider NL-to-SQL datasets is presented, drawing on published algorithm performance data to establish a comparative evaluation framework for the proposed stack. Reported deployment observations from an anonymized e-commerce platform further demonstrate operational feasibility, including a 94% reduction in false-positive alerts and an 82% reduction in mean time to recovery relative to a rule-based baseline.