About this assessment
Service level management is the promise IT makes to the business and the discipline of keeping it. Many organizations have service level agreements (SLAs) that were never checked against what the delivery chain can support, are measured with data nobody reconciles, and are reviewed in meetings that produce minutes rather than decisions. Artificial intelligence (AI) adds a new layer: AI services can be fully available and still wrong, model providers can retire the model a service depends on, and agents both deliver and consume services around the clock.
The model follows the practice in the order it is built. It starts with the mandate, owner, scope, tiers, people and regulatory obligations, then the agreement standard and metric definitions. It tests how requirements become costed, validated targets with stated workload limits, whether internal teams, suppliers, cloud and AI model providers, and automations and agents underpin each agreement, and whether the chain can deliver it. It then looks at measurement, including honest measurement of bot-handled work, error budgets and breach forecasting; at breaches, waivers, credits and disputes; at reports that lead with exceptions; at whether services are green on paper but frustrating in practice; and at reviews that drive tracked action and a practice that improves itself.
Each question is scored on three dimensions: Documented, Implemented and Effective. This exposes practices that exist on paper but not in daily work, work that is done but never written down, and practices that run but are never measured.
Adjacent single-practice depth sits in other library models: Technology Service Catalog for offerings, Business Relationship Management for surveys and complaints, Service Integration and Management for multi-supplier integration, Incident Management for priority and resolution clocks, Continual Service Improvement for the improvement plan and AI Governance for agent autonomy decisions.