Closed-Loop AI Frameworks for Real-Time Decision Intelligence in Enterprise Environments

Abstract

The traditional analytics systems tend to be retroductive, where they look at the past information with a view to deriving insights that may not be really applicable in the rapidly changing enterprise world. This limitation is what this paper is going to look into on how closed-loop AI systems can truly achieve their potential by making it possible to have real-time decision intelligence in the enterprises. It puts an emphasis on the integration of streaming data pipelines, feedback loops that are continuously running and adaptive inference systems in which AI systems dynamically learn based on the outcome of their actions and respond with new actions accordingly. In the article, it is also described how latency control, traceability of decision, and stability of the system are the key architectural recommendations that are significant to maintain the reliability and efficiency of the real-time AI systems. In addition, the paper also talks about the technical issues, such as processing large volumes of data, and about governance issues, such as accountability, transparency and auditability of the decision-making processes. The research is a roadmap of how companies can utilise the potential of AI to make operational decisions and manage to have scalability, reliability, and effective supervision of the system simultaneously. The proposed closed-loop model would turn the decision-making process more agile in such a way that an AI-based system could always be developing and adapting to different circumstances in business.