INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO PROJECT MANAGEMENT: THE EXPERIENCE OF ALADDIN BY BLACKROCK
Abstract
The article examines the integration of artificial intelligence (AI) into project management as a transition from reactive control to predictive, data-centric management. A review of current works and practical reports on the impact of AI on planning, monitoring, risk management, and decision support is provided. Using the example of BlackRock's ALADDIN platform, unresolved managerial and methodological contradictions are outlined. The empirical part is based on a global survey by PMI Sweden. It has been determined that the gap between the technological capabilities of AI platforms and the management mechanisms for their responsible use manifests itself in three dimensions: methodological (lack of transparent designs for evaluating the effect of AI), organizational (insufficient maturity of management models), and transferable (limited transferability of ALADDIN approaches to non-financial contexts). The ALADDIN case study provides a brief history of the platform's development (an internal risk tool in 1988, further evolution into a unified platform) and its current architecture: a “common data language,” the Aladdin Data Cloud environment, a combination of modules for public markets and eFront for private assets, scenario modeling, stress testing, and generative tools based on NLP. The functional capabilities for project management are summarized: portfolio planning and “what-if” scenarios using a unified methodology for assessing risk and performance; predictive risk analytics with early detection of deviations; operational management (restrictions, policies, audit trail) and unified reporting; decision support based on generative insights; orchestration of processes for public and private assets in a single information environment. The final recommendations are based on two interrelated vectors: targeted AI implementation in “high-impact” processes (data/reporting, monitoring, planning/scheduling) and human capital development (data literacy, ethical and managerial competencies) as a condition for transforming AI potential into measurable performance improvements.
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