Agentic AI-Driven Forecasting for IT Projects

Traditional IT project forecasting methods rely on siloed, retrospective data (e.g., Jira ticket histories), leaving teams unprepared for evolving risks such as shifting customer demands, accumulating technical debt, or new regulatory mandates. Studies show that 60% of IT projects exceed budgets due to unplanned scope changes, exposing the limitations of reactive approaches. We introduce Agentic AI for Proactive IT Forecasting (AAPIF), a novel framework that integrates intelligence-grade premise valuation with multi-source data fusion to proactively forecast project outcomes across technical, business, and market contexts. Unlike static models, AAPIF dynamically weights input data—such as customer requirements, organizational context, and compliance signals—based on reliability (freshness, credibility) and relevance (contribution weights C_i). It continuously refines predictions using reinforcement learning. Key Contributions: A mathematical model computing confidence-weighted success probabilities, achieving 89% accuracy—a 32% improvement over Random Forest baselines. Actionable intelligence protocols that reduce data collection errors by 45%, utilizing premise valuation (e.g., stakeholder alignment scoring) and automated risk alerts. In a fintech case study, AAPIF reduced unplanned scope changes by 37% through risk prediction (e.g., "72% likelihood of API scalability issues in Q3") and strategic recommendations (e.g., "Reassign three developers to refactor modules"). By transforming raw data into strategic foresight, AAPIF empowers project managers to become proactive architects of success, rather than reactive trouble-shooters. Keywords: Agentic AI, IT project forecasting, premise valuation, Agile project management, predictive analytics, risk mitigation