The current state and trends of artificial intelligence in project management: a bibliometric analysis

• Autor(a) principal: Kuster, Luis
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: https://hdl.handle.net/10438/31274
• topic: Project management; Artificial intelligence; Bibliometric analysis; Scientific mapping; Emerging trends; Gestão de projetos; Inteligência artificial; Análise bibliométrica; Mapeamento científico; Tendências emergentes; Administração de empresas; Administração de projetos; Inteligência artificial; Bibliometria

Artificial Intelligence is considered among the most disruptive technologies across industries. Financial institutes use AI to detect fraudulent behavior in credit card use. Healthcare applies AI to diagnose and treat illnesses. Marketers apply AI to target advertising and run chatbots. Currently, researchers predict AI will soon disrupt Project Management by promising more accurate predictive project analytics and enhanced efficiency through intelligent resource management software and chatbots. Increased data collection capabilities are a main driver of these advancements. With the development of the AI in Project Management, the synthesis of knowledge and recent discoveries has become essential for development of the research field. In response, this thesis applies a bibliometric analysis on the existing literature on ‘Artificial Intelligence in Project Management’. The study presents an extensive literature review on AI technical building blocks, challenges, and industry adoption. Based on 467 documents retrieved from Web of Science and Scopus, the bibliometric analyses methods of Co-Citation, Bibliographic-coupling and Co-Word are applied. From the Co-Citation analysis, the intellectual structure of AI in Project Management is identified, from which the knowledge fields of Software Effort Estimation, Project Risk Modeling, and Construction Cost Estimation emerge. Furthermore, the bibliographic-coupling analysis identifies four emerging trends in the discipline, namely (i) increased automation, explainability, and data robustness in cost estimation models, (ii) intelligent Project Control systems based on Earned Value Management, (iii) risk mitigation scenario modeling, and (iv) optimization of input factors for effort estimation models. The findings from the bibliometric analysis are discussed and compared to the literature review. Furthermore, practical applications of AI to different project management processes are discussed on their benefits, trade-offs, and hurdles to implementation. Lastly, this thesis provides ten recommendations for future studies.