Role-Based Programme
RB1299

AI for Service Delivery Operations

Advanced Delivery Intelligence, SLA Optimisation & Operational Excellence

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across service delivery operations, workload planning, SLA management, fulfilment, incident coordination, and operational improvement.
  • Use AI-assisted analysis to identify delivery bottlenecks, capacity constraints, service failures, recurring issues, performance risks, and resource gaps.
  • Apply AI to service scheduling, queue management, workforce planning, escalation coordination, vendor performance, operational reporting, and process optimisation.
  • Use AI-assisted analytics to improve service reliability, turnaround time, SLA achievement, productivity, customer outcomes, and operational visibility.
  • Build responsible AI-enabled Service Delivery Operations workflows with strong governance, controls, validation, escalation, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchService Operations AnalyticsSLA IntelligenceDemand & Capacity AnalysisWorkforce PlanningIncident AnalysisRoot Cause AnalysisWorkflow OptimisationVendor Performance AnalysisOperational Risk AnalysisManagement ReportingWorkflow AutomationAI AgentsDecision-Support Tools

Who should attend

  • Service Delivery Operations Managers
  • Operations Managers
  • Service Operations Managers
  • Service Delivery Leads
  • Operations Analysts
  • Service Operations Analysts
  • Service Coordinators
  • Service Fulfilment Managers
  • Workforce Planning Professionals
  • Service Performance Managers
  • Operational Excellence Professionals
  • Service Desk Operations Professionals
  • Vendor Operations Managers
  • Business Operations Professionals
  • Leaders Responsible for Service Delivery Operations

Prerequisites & Participant Readiness

  • Experience in service delivery, service operations, business operations, customer operations, or operational management
  • Familiarity with SLAs, service queues, fulfilment workflows, incidents, capacity, and performance reporting
  • Basic awareness of Generative AI and common business applications
  • Comfort working with operational, service, workload, and performance data
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, analytics, automation, and AI agents
  • Exploring AI applications across service planning, fulfilment, monitoring, coordination, and improvement
  • Distinguishing AI-assisted operations from autonomous service-delivery decisions
  • Understanding hallucinations, weak data, operational risk, and human accountability
Practical activities
  • Mapping AI opportunities across the Service Delivery Operations lifecycle
  • Identifying activities suitable for augmentation, automation, or continued human ownership
  • Creating an AI opportunity map for Service Delivery Operations teams

Scenarios

High Backlog & SLA Breaches to Service Recovery Model

Demand Volume → Queue Analysis → Capacity Gaps → Workflow Bottlenecks → SLA Risk → Resource Actions → Process Redesign → Recovery Metrics

Participants use AI to analyse a high-backlog service environment, identify workload, capacity, and process causes, and create a structured SLA recovery and operational improvement plan.

Multi-Team Service Delivery to AI-Enabled Operations Model

Service Intake → Team Handoffs → Vendor Dependencies → Incident Patterns → Performance Data → Automation Opportunities → Governance → Executive Dashboard

Participants use AI to diagnose a complex multi-team service-delivery operation, improve handoffs and accountability, introduce controlled automation, and build a scalable service-operations model.

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