Beyond the Product Lifecycle: A Policy-Driven Systems Intelligence Framework for Governing AI across Organizational Decision Time
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".