As engineering organizations scale, manual portfolio tracking becomes slow, fragmented, and error-prone. This article presents a three-phase framework for building an AI-powered technical operations engine that standardizes data intake, leverages AI agents and RAG to automate data aggregation and anomaly detection, and enables data-driven executive governance. By replacing reactive reporting with autonomous operational intelligence, organizations can improve forecasting accuracy, reduce operational overhead, optimize capital allocation, and scale technical portfolio management with greater accountability and efficiency.
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