Cloud-Native Modernization As AI Readiness Infrastructure: An Architectural Framework From Multi-Industry Case Studies

Abstract

Enterprise cloud modernization programs are typically measured by two of their most common objectives: lowering the total cost of operations and eliminating technical debt․ This framing systematically undervalues the most strategically valuable and complex result of these programs: the creation and operation of data platform infrastructure for ML, real-time analytics and generative AI at enterprise scale. The paper reports a multiple-case study of three enterprise cloud-native modernizations of proven business value in the telecoms, retail, and supply chain verticals. A cross-case analysis led to the formulation of an ARCMF․ The AI-Ready Cloud Migration Framework (ARCMF) comprises four enabling patterns․ They are the data contract, the event-driven integration backbone, the tiered data Lakehouse architecture, and the versioned inference API. Adopting Yin's multiple-case study method, the analysis revealed that organizations embedding these patterns right from the program's beginning can deploy production ML capabilities months ahead of those that treat AI enablement as a post-migration task, while achieving orders of magnitude lower marginal costs. Achievements include $12M+ per year in software licensing savings for telecommunications transformation, re-platforming a retail system comprising 85% of the organization's revenue, and eliminating $1.5M per month in supply chain operational losses with machine learning-based routing intelligence. This paper builds a multi-industry synthesis, a practitioner's four-pattern architectural building blocks for AI readiness, and seven foundational design principles for AI-ready cloud modernization. Finally, the paper discusses implications for enterprise architects, cloud platform engineers, and technology leaders; limitations in the study; and future research.