Legacy information systems continue to underpin critical operations across industries such as banking, government, manufacturing, and large-scale enterprises. While these systems provide stability and institutional memory, they also introduce significant challenges related to scalability, security, interoperability, maintainability, and innovation velocity. Recent advances in artificial intelligence, including machine learning, predictive analytics, and generative AI, have emerged as transformative forces capable of re-engineering legacy systems rather than merely replacing them. This research article presents an in-depth, theory-driven and practice-oriented examination of AI-enabled legacy system modernization, with particular emphasis on augmented intelligence, quality assurance transformation, and human–AI collaboration. Drawing strictly on the provided academic, industry, and thought-leadership references, the study synthesizes multidisciplinary perspectives from software engineering, digital transformation, cybersecurity, organizational strategy, and human–computer interaction. The article develops a conceptual framework explaining how AI techniques support incremental migration, automated code abstraction, predictive quality assurance, and continuous system evolution, while simultaneously addressing governance, data privacy, cybersecurity, and ethical considerations. A qualitative methodological approach is adopted, integrating comparative analysis of existing frameworks, thematic synthesis of case-based insights, and interpretive evaluation of reported outcomes. The findings reveal that AI-driven modernization is not a purely technical endeavor but a socio-technical transformation requiring augmented intelligence models that balance automation with human judgment. The discussion critically evaluates limitations, including algorithmic bias, skills gaps, and integration complexity, and outlines future research directions related to resilient architectures, explainable AI, and adaptive governance. The article concludes that AI-enabled legacy modernization represents a foundational pathway for sustainable digital transformation when aligned with human-centric design and strategic oversight.

Error budgets, which come from Service Level Objectives (SLOs), are a way to measure and control the trade-off between the speed of software supply and the risk of reliability. Error budgets are common in modern SRE practice, but they are harder to use in banks because of operational resilience standards, tight auditability, third-party concentration risk, and the fact that disruptions affect customers and markets in different ways. This paper presents the Finance Error Budgeting Framework (FEBF): a governance-conscious, dependency-based, and regulation-aligned error budgeting approach intended for financial SRE teams. FEBF brings in (i) risk-tiered SLO design that is in line with important business services, (ii) dual-ledger burn attribution across service and dependency layers, (iii) burn-rate-driven release governance and change control integration, and (iv) evidence-ready artifacts that meet the operational resilience standards of DORA, FFIEC, and PRA. We offer clear definitions, a plan for how to put them into action, flowcharts, tables and chart specifications for empirical evaluation, and a policy playbook that is ready for use in a business. The result is a model that works, can be scaled up, and makes incidents more likely to end well while making it easier to defend against regulatory action.