Technology Choices at Scale

This chapter analyzes the challenges and strategies for scaling AI technologies across various organizational contexts, focusing on infrastructure obstacles encountered by MegaRetail, RegionalTelco, and CommunityBank. It identifies three key infrastructure walls: traditional ML's feature engineering challenges, GenAI's token economics, and specialized AI's edge constraints. The chapter provides frameworks to anticipate and mitigate these walls, ensuring scalable and cost-effective AI deployment. It examines the deployment strategies for AI systems across different organizational contexts, focusing on the trade-offs between on-premise, cloud, and hybrid architectures. The analysis is framed by the PRESS profile, which evaluates sustainability, safety, explainability, responsible, and people-first scores to guide platform selection. The chapter highlights the importance of a comprehensive Total Cost of Ownership (TCO) analysis, which includes hidden costs such as platform licensing, integration, training, and maintenance. It concludes with a framework for platform selection aligned with PRESS principles, ensuring that infrastructure decisions are strategically aligned with business objectives. The chapter also presents a comprehensive metrics framework for guiding AI infrastructure decisions, focusing on key performance indicators (KPIs) such as cost per transaction, user satisfaction, latency, regulatory adherence, and innovation speed.