From Model Accuracy toWorkflow Reliability: A Practical Framework for Enterprise AI Systems

Abstract

The predictive or generative capacity of a model isn’t enough for enterprise AI applications. Several factors can affect whether a user’s task succeeds: retrieval, enterprise data, orchestration, external tools, human approvals, and infrastructure. While a number of these dependencies are acknowledged in current standards and research, there is a practical approach to communicating what specific functionality an AI system needs to reliably do to be considered a product team. Objective: This article compiles and reviews existing literature on AI quality, generation with a machine learning production system, retrievalaugmented generation, agent failure, and service reliability from a workflow perspective to define and explore enterprise AI reliability. Approach: This is provided by a targeted narrative review of relevant standards, research publications and a first-party production incident. The contract details what job it is supposed to perform, what results the user expects, the conditions under which it can