Reframing Reliability and Observability in Legacy-to-Cloud Transitions: An Integrated SRE, Enterprise Observability, and AI-Driven Operations Perspective

Abstract

The accelerating digitization of enterprise systems has intensified the pressure on organizations to modernize legacy infrastructures while sustaining high levels of reliability, performance, and economic efficiency. Retail enterprises, in particular, face unique challenges due to their historical dependence on monolithic systems, tightly coupled point-of-sale architectures, and seasonal demand volatility. Within this context, Site Reliability Engineering (SRE), enterprise observability, and AI-driven operational paradigms such as MLOps and AIOps have emerged as interrelated yet insufficiently integrated bodies of practice. Existing scholarship often addresses these domains in isolation, thereby underestimating their combined potential to resolve the systemic fragilities inherent in legacy-to-cloud transformations. This research develops a comprehensive, theory-driven examination of how SRE principles can be operationalized in legacy retail infrastructures through advanced observability frameworks and augmented by artificial intelligence–enabled operational intelligence.

Grounded in an extensive synthesis of contemporary literature on observability, cloud-native monitoring, AI-based insights, and machine learning operations, this article advances a unifying conceptual framework that situates reliability as an emergent property of socio-technical systems rather than a purely technical outcome. Central to this analysis is the argument that observability constitutes the epistemic foundation upon which SRE practices can be meaningfully enacted, particularly in environments characterized by architectural debt and organizational inertia. The study draws substantively on recent applied research into SRE implementation in legacy retail contexts, demonstrating how error budgets, service level objectives, and cultural realignments can be adapted to non-cloud-native environments without undermining operational continuity (Dasari, 2025).

Methodologically, the research adopts a qualitative, interpretive approach that integrates comparative literature analysis, conceptual modeling, and critical synthesis. Rather than proposing novel algorithms or quantitative benchmarks, the study emphasizes explanatory depth, tracing the historical evolution of monitoring into observability, the convergence of SRE with DevOps and IT operations, and the increasing role of AI as both an enabler and a risk factor in operational decision-making. The results articulate a set of analytically derived insights concerning the conditions under which SRE and observability mutually reinforce one another, the organizational constraints that limit their effectiveness, and the economic implications of AI-driven observability at scale.

The discussion extends these findings by engaging with competing scholarly perspectives on automation, trustworthiness in machine learning, and sustainability in AI-enabled systems. It critically examines the tension between reliability and innovation, the risks of algorithmic opacity, and the long-term implications of embedding AI into reliability-critical workflows. The article concludes by outlining a future research agenda focused on cross-disciplinary integration, empirical validation in diverse retail settings, and the ethical governance of intelligent operational systems. In doing so, it contributes a theoretically rich and practically relevant foundation for advancing reliability engineering in complex, evolving enterprise environments.