Beyond the Product Lifecycle: A Policy-Driven Systems Intelligence Framework for Governing AI across Organizational Decision Time

Abstract

Artificial intelligence governance has largely been framed around product lifecycles, model deployment stages, and post hoc compliance audits. While these approaches offer necessary safeguards, they are insufficient for governing AI systems that continuously shape organizational decisions across time, scale, and uncertainty. This paper proposes a Policy-Driven Systems Intelligence Framework that reconceptualizes AI governance beyond static lifecycle checkpoints toward dynamic decision-time regulation. The framework integrates systems thinking, institutional policy design, and organizational intelligence to govern how AI influences strategic, operational, and tactical decisions throughout their temporal evolution. The proposed framework introduces decision time as a primary governance dimension, emphasizing anticipation, intervention, and accountability before, during, and after AI-assisted decisions occur. Rather than treating AI as a bounded technical artifact, the model positions AI as an embedded socio-technical actor whose outputs interact with human judgment, organizational incentives, and regulatory norms. Policy instruments such as adaptive guardrails, decision provenance tracking, role-based escalation thresholds, and continuous risk recalibration are embedded directly into decision workflows. At the organizational level, the framework enables alignment between AI behavior and institutional objectives, ethical commitments, and public interest obligations. It supports cross-functional governance by linking executive oversight, operational controls, and frontline decision rights within a unified intelligence architecture. At the policy level, the framework offers regulators and standard-setting bodies a scalable approach for supervising AI systems without stifling innovation, shifting emphasis from model-level compliance to outcome-sensitive decision governance. By foregrounding decision time, the framework addresses emerging risks such as automation bias, policy drift, silent capability expansion, and cumulative harm that often escape lifecycle-based controls. The contribution of this paper is twofold: it advances AI governance theory by introducing decision-centric systems intelligence, and it provides a practical blueprint for organizations and policymakers seeking resilient, transparent, and adaptive governance mechanisms for AI-enabled decision environments. The framework is intended to support trustworthy AI deployment in complex organizational and societal systems where decisions, not products, are the primary locus of impact. Ultimately, it positions governance as an ongoing cognitive and institutional capability that evolves with organizational learning, policy feedback, and societal expectations in rapidly transforming AI-mediated decision ecosystems across sectors and jurisdictions globally.

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