This research presents the current state of development and implementation of AI predictive analytics for project management support through the lens of Indonesian financial projects, highlighting a practical implementation gap and its impacts toward the fulfillment of enhancements in the core project management aspects of risk prediction, budget and resource estimation, deliverables quality monitoring, and automation of project financial processes. A qualitative methodology is applied to explore the various field experiences and sentiments of five financial project professionals in individual financial firms through direct semi-structured interviews. Analysis of the findings affirms the implementation of machine learning predictive analytics in the four key project areas, with tasks in risk strategy implementation, budget estimation, and resource allocation management revealed as the prominent applications utilized by respondent financial companies. Benefits in streamlining tasks and improvements toward project efficiency are noted by the respondents, along with enhancing team awareness against unseen risks and resource issues, setting new standards for quality management, and reducing human errors in rapid financial processing. The findings further uncovered key barriers in predictive analytics implementation success stemming from system maturity and accuracy issues, and moreover the influence of intangible human factors toward the financial sector business environment and practices. Improving the synergy of the technical capabilities predictive AI systems with human subjectivity and intuition is paramount to ensure successful implementations of predictive project support tools in the Indonesian finance sector.