AI Application Operations -- A Socio-Technical Framework for Data-driven Organizations

TL;DR

This paper presents a socio-technical framework for AI application operations that emphasizes continuous monitoring, feedback, and organizational roles to improve data-driven AI projects from idea to production.

Contribution

It introduces a comprehensive framework integrating technical processes and organizational roles, emphasizing monitoring and feedback for AI operations in data-driven organizations.

Findings

Framework supports continuous improvement and compliance.

Integrates runtime verification for AI safety and reliability.

Guides organizations from AI idea to production.

Abstract

We outline a comprehensive framework for artificial intelligence (AI) Application Operations (AIAppOps), based on real-world experiences from diverse organizations. Data-driven projects pose additional challenges to organizations due to their dependency on data across the development and operations cycles. To aid organizations in dealing with these challenges, we present a framework outlining the main steps and roles involved in going from idea to production for data-driven solutions. The data dependency of these projects entails additional requirements on continuous monitoring and feedback, as deviations can emerge in any process step. Therefore, the framework embeds monitoring not merely as a safeguard, but as a unifying feedback mechanism that drives continuous improvement, compliance, and sustained value realization-anchored in both statistical and formal assurance methods that extend runtime verification concepts from safety-critical AI to organizational operations. The proposed framework is structured across core technical processes and supporting services to guide both new initiatives and maturing AI programs.