Model Online / On-Site
Duration
Modules 4 modules

Course Objective & Scope

Contact centers and customer experience (CX) teams generate thousands of call transcripts, chat logs, survey comments, and support tickets every day—yet manual quality assurance typically reviews less than 2% of interactions. This 0.5-1 day applied workshop shows CX and contact center leaders how to harness generative AI across the entire customer support lifecycle.

Working hands-on with realistic call transcripts and feedback datasets, participants design four high-impact workflows: Voice of the Customer (VoC) root-cause mining, intelligent intent classification and routing, real-time and post-call Agent-Assist Copilot templates, and 100%-coverage automated QA scoring rubrics.

Who Should Attend?

  • Contact center directors, customer service heads, and Customer Experience (CX) leaders
  • Quality assurance (QA), workforce training, and complaint management specialists
  • Product owners and business analysts managing CRM, CCaaS, ticketing, and AI support tools

Key Takeaways

  • Extract structured root causes, emerging product defects, and churn signals from unstructured transcripts and feedback
  • Design intent routing and triage flows that separate self-service queries from high-risk escalations
  • Build Agent-Assist Copilot workflows that surface policy answers in real time and automate After-Call Work (ACW) summaries
  • Translate manual QA scorecards into calibrated LLM-based Auto-QA rubrics covering compliance, accuracy, and empathy
  • Prioritize contact center AI initiatives against core KPIs including AHT, FCR, CSAT, and QA coverage

Curriculum

The Contact Center AI Value Map: Beyond Basic Chatbots
  • Why augmenting human agents and automating QA often delivers faster ROI than full customer-facing deflection
  • Mapping AI interventions across Pre-Interaction (triage), In-Call (Agent Assist), and Post-Call (ACW & VoC)
  • Prerequisites for success: speech-to-text (STT) accuracy, speaker diarization, and PII redaction
Voice of the Customer (VoC) and Transcript Analytics Lab
  • Designing structured extraction prompts to turn raw transcripts into hierarchical root-cause taxonomies
  • Detecting repeat-contact drivers, broken processes, and high-risk churn signals beyond simple sentiment scores
  • Building automated executive VoC digests for product, operations, and commercial stakeholders
Intelligent Intent Routing and Agent-Assist Copilot Design
  • Structuring multi-label triage for intent, urgency, regulatory sensitivity, and customer vulnerability
  • Designing real-time Agent-Assist cards that retrieve exact policy clauses and next-best actions
  • Automating After-Call Work (ACW): generating standardized CRM notes, disposition codes, and follow-up tasks
Automated Quality Assurance (Auto-QA) and Coaching Workflows
  • Refactoring subjective QA scorecards into evidence-backed, binary and tiered LLM evaluation rubrics
  • Auditing 100% of interactions for mandatory compliance disclosures, resolution quality, and agent empathy
  • Calibrating Auto-QA scores against human QA auditors and generating targeted coaching briefs

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