Cognitive Project Management for Artificial Intelligence Deployment and Data Quality Governance in Cloud Ecosystems: A Saudi Vision 2030 Perspective - Kayani, MOHSIN (Technium: Romanian Journal of Applied Sciences and Technology, 2025)
Artificial intelligence (AI) and cloud computing form the technological nucleus of Saudi Arabia’s Vision 2030 agenda, representing the Kingdom’s determination to shift from an oil-dependent economy to a knowledge-based digital ecosystem. While AI algorithms and cloud platforms are rapidly proliferating across public and private enterprises, many projects fail to achieve sustainable impact because they lack structured project-management methodologies and robust data-quality governance. This research introduces a comprehensive framework that integrates the Cognitive Project Management for Artificial Intelligence (CPMAI®) methodology with data-quality governance principles anchored in SDAIA’s National Strategy for Data and AI and the Digital Government Authority (DGA) standards. The objective is to develop a repeatable model for AI deployment in cloud ecosystems that ensures both technical excellence and policy alignment. The study applies a mixed conceptual and empirical approach. It combines bibliometric mapping of peer-reviewed literature (2020–2025) with policy-content analysis of Saudi digital-governance documents to identify patterns in AI project execution, governance maturity, and data-quality practice. Synthetic benchmark datasets were constructed to illustrate regional and sectoral variance in AI adoption and data-governance scores. The framework is further evaluated through case-aligned indicators: KPI reliability, AI governance score, and data-quality index. Findings indicate that AI deployment projects conducted under a CPMAI-informed structure achieve greater predictability, fewer failures in model integration, and stronger traceability of data lineage compared with traditional project management techniques. Across sampled Saudi regions, Riyadh and the Eastern Province exhibit the highest levels of AI project maturity, while emerging regions such as Qassim and Jazan are in the initiation or piloting stages. Sectoral analysis reveals that telecommunications and finance have the most advanced data-quality governance indices (>85/100), whereas healthcare and logistics require greater standardization. The paper proposes that linking data-quality governance with the CPMAI lifecycle (understanding → preparation → modeling → evaluation → deployment → monitoring) creates a self-reinforcing loop of learning and accountability. By embedding Vision 2030 and SDAIA benchmarks into each phase, the framework bridges technical execution and policy compliance. Graphical analyses demonstrate a positive correlation (r = 0.76) between KPI reliability and AI governance scores, suggesting that organizations with stronger data management and ethical oversight also achieve higher operational efficiency. Ultimately, the research concludes that Saudi enterprises can realize Vision 2030’s digital-economy goals only when AI initiatives are managed through cognitive project methodologies that elevate data governance to a strategic function rather than a compliance burden. The proposed CPMAI × Data-Governance model offers a practical blueprint for building trustworthy, auditable, and value-driven AI systems within Saudi cloud ecosystems. Corresponding Author: mohsin.ashraf17111@gmail.com