From Tools to Teams: How AI-Enabled Project Monitoring Shapes Team Performance in Agile Software Development Projects

Abstract

Agile software development projects have become the dominant mode of IT project delivery globally, yet the relationship between AI-enabled project monitoring tools and agile team performance remains theoretically underdeveloped and empirically underexplored. Drawing on the Technology-Organisation-Environment (TOE) framework, this study examines how AI-enabled project monitoring shapes agile team performance, proposing team trust in AI monitoring as a mediating mechanism and agile maturity as a team-level moderating boundary condition. A quantitative multilevel study was conducted using a two-source, two-level research design, collecting data from 312 agile team members and their project managers, nested in 74 software development teams across five countries. Multilevel modelling via Mplus confirms that AI-enabled project monitoring positively impacts agile team performance, with this effect mediated by team trust in AI monitoring. Agile maturity strengthens the indirect effect, such that teams with higher agile maturity derive significantly greater performance benefits from AI monitoring than teams with lower maturity. The study advances project management theory by extending the TOE framework to an agile team performance context and by identifying team trust as the relational mechanism through which AI monitoring tools produce their team-level effects. Practical implications for project managers, Scrum masters, and IT organisations investing in AI-enabled project delivery tools are discussed.