Dynamic AI Portfolio Governance Model Integrating Total Cost of Ownership and Technology Architecture Trade-offs

Authors

  • Xiaochao Pu

Keywords:

Artificial Intelligence Governance, Portfolio Management, Total Cost of Ownership, Technology Architecture, Strategic Decision-Making, Risk Mitigation, Operational Efficiency, Hybrid Models, Investment Analysis, Enterprise Optimization, Regulatory Compliance

Abstract

The dynamic landscape of artificial intelligence in large-scale enterprises has necessitated the development of portfolio governance models that optimize both strategic objectives and operational realities. This research paper introduces a comprehensive framework for Dynamic AI Portfolio Governance, integrating robust Total Cost of Ownership (TCO) analytics with multifactor technology architecture trade-off assessments. Drawing on empirical data and comparative metrics from 2020 to 2024, the model reveals how global AI investments skyrocketed from $58 billion to $1.5 trillion, with operational efficiency ROI improving from 7.5% to 13.4%. A granular analysis illustrates that hybrid technology architectures deliver a 12-18% reduction in aggregate operating expenditures over three years relative to traditional on-premises or cloud-only models. Surveyed enterprises with mature unified governance frameworks achieve a 30% higher realized ROI and significantly reduced risk exposure, specifically lower incident and compliance breach rates. The framework’s adaptive cycle enables continuous portfolio optimization, supporting strategy definition, asset tiering, predictive financial modeling, iterative trade-off feedback, and ongoing monitoring. Governance budget analysis identifies platform investment as the dominant cost category at 60%, followed by architecture and compliance. Enhanced scalability, control, and predictability emerge as cornerstones in long-term value realization, while regulatory alignment and risk mitigation remain essential for sustaining competitive advantage and trust. The findings presented offer a replicable methodology for technology leaders and portfolio managers seeking to harmonize investment excellence and operational resilience in the age of intelligent automation.

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Published

30.11.2024

How to Cite

Xiaochao Pu. (2024). Dynamic AI Portfolio Governance Model Integrating Total Cost of Ownership and Technology Architecture Trade-offs. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 3975–3984. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7985

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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