Ravindrakumar Prajapati
Ravindrakumar Prajapati
Sr Anaplan Manager, Anaplan Solutions Architect, Relanto Inc
Computational Intelligence in the Era of Enterprise AI: Architectures for Self-Learning Decision Platforms
Enterprises operate in dynamic, data-intensive environments where static analytics, periodic forecasting, and manual workflows struggle to keep pace. While computational intelligence has advanced predictive capability, most enterprise systems remain architecturally fragmented, limiting adaptive, machine-driven decision-making. This paper introduces a unified framework for Self-Learning Decision Platforms (SLDP) that integrates reinforcement learning agents, deep neural networks, and adaptive inference engines within scalable data ecosystems. Its layered cognitive structure perception, reasoning, and action continuously interprets operational data, generates predictive insights, simulates scenarios, and optimizes decisions in real time across supply chain, financial risk, and process automation domains. The paper also examines key deployment challenges, including interpretability, data governance, latency, and organizational trust, and shows how hybrid neuro-symbolic approaches improve transparency, adaptability, and reliability. The result is a deployable reference architecture and a strategic pathway for organizations moving from rule-based automation toward adaptive, self-learning decision platforms, illustrated through domain-oriented case scenarios.