AI Service Management I

The vocabulary and mental model the rest of the certification rests on: what a service is, how value is co-created, the parties in a service relationship, utility, warranty and experience, outcomes, and service level agreements.

The system that turns opportunity and demand into value: the components of a service value system, the seven guiding principles and how to apply them, the continual improvement model, and governance as direction and control.

The operating layer: the product and service lifecycle activities, value streams, and the core management practices covering incident, problem, change, request, service level and monitoring, together with supplier management and dependency on AI models.

How service operations change when AI is central to them: AIOps and event correlation, predictive and proactive operations, virtual agents on the service desk, intelligent and agentic automation, and human oversight of AI-driven service actions as a primary control.

Governance of AI inside service management: accountability when an agent acts on a live service, data quality and provenance in AIOps pipelines, transparency to users, the risk of over-automation, and how AI service governance connects to an organization wide AI governance function.

The human and sustainable dimensions of service value: managing and measuring experience beyond technical service levels, trust and adoption as prerequisites for getting value from AI services, and the compute and energy footprint of running them.