Role-Based Programme
RB1287

AI for Customer Service Operations & Quality

Advanced Service Analytics, Quality Intelligence & 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 customer service operations, quality assurance, performance monitoring, workforce productivity, and continuous improvement.
  • Use AI-assisted analysis to identify service bottlenecks, quality gaps, recurring customer issues, SLA risks, workload patterns, and operational inefficiencies.
  • Apply AI to quality monitoring, interaction reviews, agent coaching, service analytics, process improvement, knowledge management, and operational reporting.
  • Use AI-assisted insights to improve service consistency, productivity, first-contact resolution, customer satisfaction, compliance, and management visibility.
  • Build responsible AI-enabled Customer Service Operations and Quality workflows with strong governance, fairness, privacy, validation, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchService Operations AnalyticsQuality MonitoringConversation AnalysisSLA IntelligenceWorkforce InsightsRoot Cause AnalysisProcess Mining ConceptsCustomer Feedback AnalysisKnowledge ManagementPerformance DashboardsWorkflow AutomationAI AgentsDecision-Support Tools

Who should attend

  • Customer Service Operations Managers
  • Customer Service Quality Managers
  • Customer Support Operations Managers
  • Quality Assurance Managers
  • Customer Service Team Leaders
  • Contact Centre Operations Managers
  • Customer Service Analysts
  • Quality Analysts
  • Service Excellence Professionals
  • Customer Experience Operations Professionals
  • Workforce & Performance Management Professionals
  • Customer Support Leaders
  • Process Improvement Professionals
  • Service Operations Analysts
  • Leaders Responsible for Customer Service Performance & Quality

Prerequisites & Participant Readiness

  • Experience in customer service operations, quality assurance, contact centre management, customer support, or service excellence
  • Familiarity with SLAs, quality scorecards, customer interactions, service metrics, workforce performance, and operational reviews
  • Basic awareness of Generative AI and common business applications
  • Comfort working with customer, quality, operational, 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 operations, quality monitoring, coaching, reporting, and improvement
  • Distinguishing AI-assisted quality management from fully automated workforce decisions
  • Understanding hallucinations, data-quality risk, employee fairness, privacy, and human accountability
Practical activities
  • Mapping AI opportunities across Customer Service Operations and Quality
  • Identifying activities suitable for augmentation, automation, or continued human ownership
  • Creating an AI opportunity map for service operations teams

Scenarios

Inconsistent Service Quality to AI-Enabled Quality Improvement Programme

Customer Interactions → Quality Review → Performance Patterns → Root Causes → Coaching Gaps → Process Issues → Improvement Actions → Quality Metrics

Participants use AI to analyse inconsistent service quality across teams and channels, identify common quality and capability gaps, create targeted coaching and process improvements, and establish a measurable QA programme.

High Service Demand to Operational Excellence Model

Demand Volume → Queue Analysis → SLA Performance → Workforce Capacity → Customer Sentiment → Process Bottlenecks → Automation Opportunities → Executive Dashboard

Participants use AI to analyse a high-volume customer-service environment, identify operational bottlenecks and quality risks, optimise workflows and capacity, introduce controlled automation, and build a scalable service-operations model.

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