Role-Based Programme
RB1286

AI for Customer Service Operations & Quality

Service Analytics, Quality Intelligence & Operational Excellence

IMAGE REQUIRED
Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop practical AI capabilities for customer service operations, quality monitoring, performance analysis, and service improvement.
  • Apply AI to analyse interactions, quality scores, customer feedback, SLA performance, workload, repeat contacts, and operational trends.
  • Use AI to strengthen quality assurance, coaching insights, process reviews, service reporting, and knowledge improvement.
  • Improve customer service performance through structured root-cause analysis, workflow optimisation, capacity insights, and data-driven decision support.
  • Apply responsible AI practices related to customer privacy, quality scoring, employee data, bias, accuracy, and human oversight.

Tools covered

Generative AI AssistantsCustomer Service AnalyticsQuality Monitoring SupportConversation AnalysisCustomer Feedback AnalysisSLA AnalysisWorkforce & Queue AnalysisRoot-Cause AnalysisKnowledge ManagementProcess ImprovementDashboard InterpretationExecutive Summarisation

Who should attend

  • Customer Service Operations Managers
  • Customer Service Quality Managers
  • Quality Assurance Managers
  • Quality Analysts
  • Customer Support Operations Professionals
  • Contact Centre Operations Managers
  • Customer Service Managers
  • Customer Support Managers
  • Service Quality Professionals
  • Customer Experience Managers
  • Workforce Management Professionals
  • Customer Service Analysts
  • Process Improvement Professionals
  • Customer Operations Professionals
  • Customer Service & Success Leaders

Prerequisites & Participant Readiness

  • Experience in customer service operations, quality assurance, customer support, contact centre operations, or service management
  • Familiarity with customer-service KPIs, SLAs, QA scorecards, queues, and operational reporting
  • Basic familiarity with Generative AI tools is recommended
  • Ability to interpret customer-service and performance data
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, multimodal AI, and AI assistants
  • Exploring AI applications across service operations, quality monitoring, reporting, and improvement
  • Understanding how AI differs from CRM, contact-centre, QA, and analytics platforms
  • Recognising hallucinations, misclassification, biased scoring, and AI limitations
Practical activities
  • Mapping customer service operations to practical AI applications
  • Identifying high-value versus high-risk AI use cases
  • Comparing traditional and AI-assisted service-quality workflows

Scenarios

Declining Service Quality to Performance Recovery

Service KPIs → Interaction Analysis → QA Review → Customer Feedback → Root-Cause Analysis → Coaching Needs → Process Actions → Recovery Plan

Participants use AI to analyse a simulated decline in customer service quality, identify operational and behavioural causes, and develop a structured recovery plan covering coaching, process, knowledge, and performance management.

High Volume & Low Resolution to Operational Excellence Plan

Queue Data → Workload Analysis → Repeat Contacts → Knowledge Gaps → Process Compliance → Capacity Issues → Improvement Actions → Operations Roadmap

Participants use AI to analyse a simulated high-volume service environment with weak resolution performance, identify process and capacity gaps, and develop a structured operational excellence roadmap.

Related programmes

Continue with programmes from the same capability area.

Take the next step

Ready to make this programme work for your team?

Customise modules, duration and business scenarios for your team.

Instructor-ledVirtualHybrid

Designed around your roles, tools and real workflows.