TDAMR: A Novel Three-Day Agile Methodology With Decision Gates for AI Project Management

This paper presents TDAMR (Tri-Day Agile with Milestone Reviews), a novel project management methodology specifically engineered for artificial intelligence (AI) development initiatives. While traditional agile frameworks struggle with AI-specific challenges such as model iteration uncertainty and data quality management, TDAMR introduces a hybrid approach combining structured tri-weekly work sessions with quantitative decision gates. The proposed methodology implements three core components: (1) focused development days (Monday, Thursday, Friday) to optimize resource allocation and reduce context switching, (2) biweekly decision gates with defined performance metrics (PM ≥ 90) and risk assessment thresholds (RAM ≤ 50), and (3) adaptive planning mechanisms for handling model iteration uncertainties. The framework introduces new quantitative metrics including Performance Metric (PM), Risk Assessment Metric (RAM), and Client Satisfaction Index (CSI) to enable data-driven decision making throughout the project lifecycle. Theoretical analysis and simulation-based modeling suggest TDAMR could potentially address common AI project challenges including resource utilization, risk mitigation, and stakeholder alignment. The methodology is particularly designed for medium to large-scale AI initiatives requiring iterative development and cross-functional team coordination. This paper presents the framework's theoretical foundations and simulation results, with future work focusing on empirical validation across diverse AI projects to measure the methodology's effectiveness in real-world scenarios.