Completion Date
9-21-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Program or Discipline Name
Project Management
Abstract
Artificial intelligence (AI) is increasingly used in project management, yet limited research has examined its relationship with workflow efficiency and process standardization in real estate private equity (REPE) acquisitions. This quantitative correlational study examined these relationships and identified which AI-supported functionalities respondents perceived as contributing most to project management performance. The study drew on project management principles and the Technology Acceptance Model. Data were collected through a cross-sectional QuestionPro survey of professionals involved in REPE and related acquisition workflows, resulting in 146 completed surveys. The study used descriptive statistics, Pearson correlations, and regression analysis to evaluate the data. AI implementation was strongly associated with workflow efficiency (r = .841, 95% CI [.784, .884], p < .001) and process standardization (r = .771, 95% CI [.693, .831], p < .001). Automation received the highest rating among the AI-supported functionalities examined. The findings indicate that greater AI implementation is associated with more efficient and standardized perceived acquisition workflows, although the design does not establish causation. REPE firms may benefit from prioritizing practical AI applications that support automation, workflow visibility, and repeatable processes while maintaining professional oversight.
Recommended Citation
van den Bosch, A. D. (2026). AI-Driven Project Management in Real Estate Private Equity: Impacts on Workflow Efficiency and Standardization. Retrieved from https://digitalcommons.harrisburgu.edu/dandt/104