Scalable Governance Frameworks for AI-Driven Enterprise Automation and Decision-Making

Abstract

The use of the AI-based automation has surpassed the establishment of the conventional governance models at the enterprise level. The paper examines scalable governance designs to be used in AI-enabled enterprise systems with a shift in governance, as an external and discrete compliance process to an integrated architecture role in system workflows. The study evaluates the presence of different architectural patterns that facilitate policy enforcement, auditability and runtime controls in the AI systems, and thoroughly examines how these features can be integrated into automated decision-making pipelines. This piece of work serves as an essential contribution to a large gap in the approaches to the strategies of AI implementation by introducing the idea of governance as one of the central ideas of AI-driven automation processes, the authors ensure that businesses will be capable of governing the large-scale AI systems. It describes why AI architectures need to be designed in such a manner that they have their own governance systems that uphold transparency, accountability and ethical compliance. The present study is part of the corpus of AI governance studies as it carries useful information on how companies can broaden governance systems to guarantee that they keep up with the speed of the AI technology development. The research ends by demonstrating that effective and unified governance is essential to the process of making sure AI systems do not turn contrary to organizational values and rules that can inform business on the issues of AI-driven business decisions as more business continues to be automated.