Strategic Integration of Data Analytics and Artificial Intelligence in Project Management for Enhancement

Research Idea and Importance

Project management is central to attaining business objectives, such as sustainable competitiveness in business. Although project managers rely on extensive data and information for decision-making and project execution success, research and practice show data analytics and artificial intelligence (AI) are under-utilized or applied incompletely for project management purposes. Based on this premise, the research was conceived to explore data analytics and AI strategic integration into project management to direct project execution and stakeholders’ decisions. This research idea is essential because data analytics and AI augment data for more effective decisions, which is central to project execution and portfolio management in organizations (Veiga et al., 2022; Pouriayevali and Student, 2022). Also, the research idea is important because project managers need help utilizing data analytics and AI tools to monitor such project aspects as cost, scope, performance, and progress, resulting in project failure and business resources’ waste. These considerations motivated the research idea because integrating data analytics and AI into project management processes and practices is an unexplored inquiry area.

Literature Review

Research establishes that it is not clear the extent to which data analytics and AI tools have been adopted in project management. Previous investigations also established a research gap concerning how organizations exploit data analytics and AI to synthesize information from different project aspects and set criteria that can augment positive project results. David et al. (2022) established that data analytics and AI constitute the newest technology that significantly enhances enterprise project management success. Regardless, scholarly discourse concerning potent implementation modalities and the contribution of data analytics and AI tools to project success continues to be at its immature stage. Also, considering the increasing complexity of project management needs due to dynamic social and economic contexts, further inquiry on the optimal modalities of conceptualizing and applying data analytics and AI for project management is relevant (David et al., 2022). Considering that project management practices have mostly remained unchanged for the last four decades, this study helps augment project outcomes failure scenarios attributed to improper methodologies application by providing new perspectives on conceptualizing project management processes. This is because organizations can fail to attain anticipated results due to poor decision-making based on valuable data and information insufficiently utilized (David et al., 2022; Pouriayevali and Student, 2022).

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Data analytics demonstrates a more significant contribution in optimizing project and resource assignments processes than other records management novelties. Data analytics for project management is becoming a crucial enterprise management tool because it facilitates core capabilities, including rational service delivery, resource optimization, risk mitigation, and cost curtailment (Selamat et al., 2021). Despite these significant capabilities, Veiga et al. (2022) assert that project management is widely unutilized concerning data analytics and AI knowledge studies. One reason for this situation is the established contention on tool selection due to the increasingly changing innovation landscape. Inherent to this, Veiga et al. (2022) are encouraged to discover how businesses exploit such tools to enhance project results, business risk mitigation, and resultant performance. Zhao et al. (2023) investigated AI-supported and data-driven engineering project supply chain management, including decision-making models and competitive supply chain simulators. Although these factors demonstrated that AI configuration allows customized situation scenarios support, this study failed to offer a comprehensive understanding of data and information synthesis via AI for project management. However, it laid a significant framework for this project because it established a scale for assessing management problems ascribed to dynamic requirements, including project environment, organization, supply chain, and market.

Studies established that project managers hardly have competent data management, let alone data analytics, capabilities, despite their valuable growth opportunities for business deliverables outcomes augmentation. Reis et al. (2027) established project managers’ poor qualification concerning using advanced project management tools like business analytics. This is further confirmed by research, establishing how organizations suffer project results deterioration attributed to improper or i