AI-Powered Budgeting and Cost Estimation: Predicting Project Costs and Preventing Overruns

Abstract

Artificial intelligence (AI) is reshaping project cost management and budgeting throughout the entire project lifecycle, from strategic planning and execution to monitoring, control, and closeout. Traditional methods reliant on periodic reporting and static forecasts have failed to keep pace with dynamic changes in labor, supply chains, and material costs, creating inherent cost and schedule risks across industries. Modern platforms now integrate predictive analytics, generative assistants, and natural language processing (NLP) to surface early risk signals and recommend resource allocation. Recent platform releases demonstrate this shift: Asana AI has introduced capacity-aware planning; ClickUp Brain leverages organizational knowledge graphs to quantify time savings; Notion AI automates multilingual meeting capture and synthesis; and SAP Analytics Cloud unifies real-time planning with predictive forecasting. Furthermore, British Airways' use of AI in its Mission Control and Pathfinder systems has demonstrated improved punctuality and decision quality at an operational scale. Vendor-reported results and case studies indicate significant reductions in manual effort, blind spots, variance detection time, and governance shortcomings compared to conventional approaches. This paper synthesizes emerging practices, compares AI-enhanced and conventional methodologies, and identifies pathways to avoid cost overruns and strengthen portfolio resilience.