AI-Driven Operational Context Modeling for Managed Service Providers

Managed Service Providers operate complex, multi-tenant infrastructure environments composed of heterogeneous tools for monitoring, ticketing, asset management, logging, and remediation. While these tools generate large volumes of operational signals, the absence of persistent and shared context across systems results in alert fatigue, prolonged incident resolution, and engineer burnout. This paper introduces a formal framework for Operational Context Modeling tailored to MSP environments. We define operational context as a structured, evolving representation of relationships between infrastructure assets, events, incidents, actions, and outcomes over time. We argue that AI systems for MSP operations must treat context as a first-class object rather than an ephemeral byproduct of individual alerts. The paper outlines a reference model for context accumulation, discusses inference challenges under partial observability, and proposes evaluation metrics for measuring operational intelligence without exposing proprietary algorithms or customer data.