A Distributed Execution Framework for Enterprise Decision Systems
Keywords:
Decision intelligence, enterprise governance, distributed AI, execution frameworks, orchestration, policy governance, scalability ,Scalable Decision Intelligence,Enterprise Governance,Distributed Artificial Intelligence,AI Execution Frameworks,Intelligent Decision Support Systems,Distributed Machine Learning,Enterprise AI Architecture,Governance Automation,Adaptive Decision Analytics,Explainable and Trustworthy AI.Abstract
Enterprise governance of Decision Intelligence enables organizations to make decisions at scale, responsibly, in a world full of both challenges and opportunities. The adoption of Distributed AI Execution Frameworks has made such scalable decision intelligence achievable in practice; however, governing these distributed frameworks properly remains an open problem. Governance mechanisms are essential for mitigating the associated risks and ensuring that accountability and transparency requirements are met. Policy-driven decision governance addresses the specification, enforcement, compliance, override, and audit of decision processes — enabling enterprises to realize the full potential of this approach.
Decision Intelligence (DI) reflects a growing effort among organizations to address the many factors that shape decision-making in a holistic way. As a metadiscipline, DI brings together Data-Driven Decision-Making and Machine Learning-Based Decision-Making, ensuring that neither happens in isolation but instead forms part of a coherent, well-considered strategy. Enterprise governance of DI thus enables decision-making that is scalable, transparent, auditable, and responsible — a capability sorely needed in a world that demands swift, competent decisions at every level of society.
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