Enterprise Architecture Modernization Blueprint for Large‑Scale IT
Abstract
Large-scale IT modernization has become a strategic priority for enterprises facing rising technical debt, fragmented legacy systems, regulatory pressure, and the rapid adoption of cloud and AI technologies. Despite significant investment, modernization programs frequently fail due to siloed decision-making, inconsistent architectural practices, and the absence of a unified modernization blueprint. This whitepaper introduces the Enterprise Architecture Modernization Blueprint (EAMB) — a structured, five-layer architectural framework designed to guide modernization across infrastructure, applications, data, integration, and business capabilities. The paper formalizes modernization as an architecture-led discipline and contributes three original, citable models: the Modernization Taxonomy, the Modernization Decision Matrix, and the Legacy Complexity Index (LCI). Together, these frameworks provide a quantitative and repeatable method for assessing legacy complexity, selecting modernization strategies, and sequencing transformation initiatives. The paper also presents modernization architecture patterns, anti-patterns, AI‑enablement opportunities, and a phased modernization roadmap. A real-world case study from a large North American financial institution demonstrates how EAMB reduces modernization risk, accelerates delivery, and enables cloud-native, data-driven, and AI-ready capabilities. By integrating academic rigor with practical enterprise architecture methods, this work offers a scalable, industry-agnostic blueprint for modernizing legacy environments. It provides researchers and practitioners with a unified, evidence-based model for planning, governing, and executing modernization at scale.
Fields of Study & Subjects
Information Technology Governance and Strategy
Software System Performance and Reliability
Advanced Software Engineering Methodologies
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 18 January 2026
- Language
- EN
- Document type
- Preprint