AI-Enabled Real-Time Processing in Cloud-Native Enterprise Data Platforms

Abstract

Enterprise data platforms increasingly embed artificial-intelligence inference directly inside their real-time stream-processing pipelines, scoring, classifying, or enriching records within the same low-latency path that ingests and transforms them, rather than treating AI inference as a separate, offline consumer of already-processed data. This integration is attractive precisely because it collapses the latency between data arrival and AI-enriched insight, but it also pulls together three concerns that enterprise data engineering has historically addressed separately: schema and data-quality validation at ingestion, elastic compute scheduling for AI inference workloads under cloud-native infrastructure, and the stateful correctness guarantees a stream-processing engine provides. Each concern has a mature literature of its own, but a platform that enriches a stream with AI-generated output without a shared contract linking these three concerns risks scoring malformed records, starving inference of resources during load spikes, or silently invalidating its own correctness guarantees once a model begins to drift. This paper proposes AIRSTREAM (AI-Integrated Real-time Stream processing for enterprise data platforms), an architecture in which a schema-validated ingestion layer, an elastic AI inference layer, and a consistency-preserving enrichment layer are coordinated by a shared stream contract coordinator that keeps data-quality contracts, inference resource allocation, and enrichment correctness guarantees mutually consistent, including when model drift degrades a previously validated contract. Following a design-science research approach, AIRSTREAM is illustrated, not empirically validated, through a comparative capability table, a short case scenario, and two illustrative figures, with every quantitative value explicitly labeled as an illustrative estimate rather than measured production data. The paper situates AIRSTREAM against the literature on stream-processing correctness models, data-quality and lifecycle management for production machine learning, elastic cloud-native infrastructure for AI inference, and model drift.