AI Archaeology: Extracting Strategic Intelligence from Decommissioned Machine Learning Models

Abstract

AI Archaeology represents a transformative discipline that systematically extracts strategic intelligence from decommissioned machine learning models to inform future development initiatives. This comprehensive framework addresses the critical gap between substantial model development investments and the limited effort allocated to post-deployment intelligence gathering. The archaeological perspective transforms retired models from simple cleanup operations into valuable learning opportunities that reveal performance patterns, failure mechanisms, and environmental interactions previously unexplored in production settings. Through systematic failure pattern identification, organizations can categorize recurring degradation modes that remain invisible during individual model assessments. Feature impact intelligence provides unprecedented insights into temporal stability dynamics, revealing how individual features contribute to system resilience or degradation over extended operational periods. Drift pattern documentation establishes evidence-based monitoring protocols that optimize resource allocation while maintaining system reliability across diverse operational contexts. Anti-pattern recognition enables systematic documentation of recurring failure modes, converting expensive mistakes into institutional knowledge that prevents repetition across development teams and project cycles. Knowledge management systems facilitate the transformation of archaeological insights into actionable organizational intelligence, creating living knowledge bases that grow more valuable through continued documentation efforts. The archaeological framework enables organizations to develop more resilient machine learning systems through evidence-based decision-making rather than theoretical assumptions, ultimately improving system longevity and reducing maintenance overhead through systematic intelligence extraction from historical deployments.