Institutional Readiness and Transformational Barriers: Artificial Intelligence Adoption Frameworks for Organizational Delivery Capability

The paper has demonstrated substantially slower adoption of artificial intelligence technologies compared to peer sectors such as healthcare, retail, and manufacturing operations, raising critical questions about organizational readiness, technological barriers, and structural impediments to AI-driven project delivery transformation. This paper investigates the gap between AI’s demonstrated capabilities and the project management community’s apparent reluctance to systematically integrate intelligent automation into project planning, execution, monitoring, and control processes. Through analysis of current AI vendor strategies (foundational models vs. autonomous bespoke systems), the research identifies critical decision factors that determine organizational eligibility for AI adoption within project contexts: capital investment requirements, data architecture maturity, dataset quality standards, scalability potential across diverse project typologies, and risk tolerance thresholds. The paper categorizes inherent implementation challenges into technical, financial, and organizational domains, including concerns about model adequacy across heterogeneous project types, cost-prohibitiveness for small-to-medium enterprises, ethical governance of autonomous decision-making in project contexts, and the potential marginalization of resource-constrained organizations lacking sufficient data infrastructure. Additionally, the work examines transitional pathways that position domain-specific AI foundation models as intermediate stepping stones enabling progressive organizational adaptation and capability development, with implications for the future role and skillset evolution of project management professionals operating in algorithmic decision environments. The analysis concludes that successful AI integration in project management requires concurrent attention to technological investment, organizational maturity assessment, ethical frameworks, and workforce adaptation strategies, rather than treating AI adoption as a purely technical implementation challenge