Governance systems across sectors vary widely in their ability to integrate artificial intelligence,
real-time monitoring, and adaptive oversight. While advanced organizations increasingly rely on
continuous sensing, data-driven decision-support, and event-validated learning, many institutions
remain anchored in reactive, compliance-centric governance models. This manuscript introduces
the Governance Maturity Model (GMM), a five-level capability framework that evaluates an
organization's readiness to implement adaptive, AI-enabled governance systems. The GMM
extends the Adaptive Governance Systems Framework (AGSF) and the AI-Enabled Governance
Oversight Model (AIGOM) by defining progressive stages of governance capability—from
reactive oversight to fully adaptive, intelligence-augmented governance ecosystems. The GMM
further establishes governance maturity as a dynamic institutional capability involving
governance observability, operational intelligence integration, adaptive recalibration, and crossdomain governance coordination within complex socio-technical environments. The model
provides a structured pathway for organizations seeking to modernize governance practices,
strengthen accountability, and align oversight mechanisms with the demands of complex,
dynamic risk environments.
Dr. Robb Shawe· International journal of adv...· 0 citations
Governance systems across sectors exhibit significant variability in their ability to integrate
artificial intelligence, real-time monitoring, and adaptive oversight. While some sectors
demonstrate advanced governance maturity—characterized by continuous sensing, predictive
analytics, and event-validated learning—others remain anchored in reactive, compliance-centric
oversight models. This manuscript presents a cross-domain comparative analysis of governance
capability across four major sectors: critical infrastructure, healthcare, finance, and public
administration. Using the Governance Maturity Model (GMM) as an evaluative framework, the
study identifies sector-specific patterns in governance readiness, oversight integration, and
adaptive capacity. Findings reveal that governance variability is shaped by environmental
complexity, regulatory intensity, technological integration, and organizational culture. The
analysis further demonstrates that governance variability reflects broader differences in
governance observability, operational intelligence integration, adaptive oversight capability,
institutional learning maturity, and resilience modernization across interconnected sociotechnical ecosystems. This manuscript extends the Adaptive Governance Systems Framework
(AGSF), the AI-Enabled Governance Oversight Model (AIGOM), and the Governance Maturity
Model (GMM) by providing a comparative foundation for cross-sector governance
transformation.
Dr. Robb Shawe· International journal of adv...· 0 citations
This study quantitatively evaluates the performance of a YOLO-based computer vision system
for real-time hazard detection across construction, manufacturing, and healthcare environments
in New York State. The analysis compares YOLO-based detection with traditional manual
inspection using key performance metrics, including mean average precision (mAP), recall,
precision, time-to-detection, and personal protective equipment (PPE) compliance rates. Results
indicate that YOLO-based systems significantly outperform manual inspection across all metrics,
demonstrating higher detection accuracy, faster response times, and improved compliance
monitoring. The findings provide empirical evidence supporting the effectiveness of artificial
intelligence–enabled safety systems in enhancing hazard detection performance and advancing
proactive safety management practices.
Dr. Robb Shawe· International journal of adv...· 0 citations
Artificial intelligence (AI) is rapidly transforming governance systems across sectors, yet most
institutions continue to rely on oversight models designed for pre-digital environments. As AI
becomes embedded in cyber-physical systems, organizational decision processes, and regulatory
infrastructures, governance must evolve from static compliance to adaptive,
intelligence-augmented oversight. This manuscript introduces the AI-Enabled Governance
Oversight Model (AIGOM). This layered decision-support intelligence architecture integrates
AI-driven sensing, operational observability, analytics, and adaptive decision-support into
governance systems while preserving human accountability, governance interpretation, and
ethical control. The model demonstrates how AI can serve as a governance augmentation layer,
generating decision-support intelligence, accelerating operational awareness, enhancing adaptive
oversight, and supporting real-time governance recalibration. AIGOM extends the Adaptive
Governance Systems Framework (AGSF) by specifying how AI capabilities interface with
governance processes across diverse sectors, including critical infrastructure, healthcare, finance,
and public administration. This manuscript establishes a theoretical and operational foundation
for AI-enabled governance across complex socio-technical environments.
Dr. Robb Shawe· International journal of adv...· 0 citations