Customer Service
Call quality and compliance monitoring
Analyse call transcripts for quality criteria: correct disclosures given, tone adherence, complaint procedure followed, resolution quality. Flags calls for review rather than sampling randomly.
60–75%
reduction in manual call monitoring effort
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
A U.S. hospital network using Amazon Connect Contact Lens moved from 5% manual call sampling to 100% AI-powered monitoring, with patient satisfaction up 11 points and QA productivity up 250% (D3 Clarity).
Transcription quality is the first dependency — automated transcription of contact centre audio varies in accuracy, especially with accents or noisy backgrounds. Validate before relying on it.
Define the quality framework before building — what specific behaviours are you looking for or against? The AI assesses against what you define.
Regulatory compliance requirements (FCA, for example) specify what must be said in certain call types. These mandatory disclosure checks are the highest-value use case and must be 100% reliable before use in a compliance context.
Use this for targeted sampling, not full replacement of QA. The AI should flag calls most likely to have issues; a trained QA assessor still reviews the flagged calls and makes the final assessment. Fully automated compliance decisions without human review create their own regulatory risk.
Experiment starter: Take 100 recent call transcripts already reviewed by your QA team. Run them through an LLM against your quality framework and compare the AI flags against QA findings. Measure recall (percentage of actual issues caught) and precision (percentage of AI flags that are real issues). An 80% recall rate with under 30% false positive rate is a reasonable threshold for use as a first-pass screening tool.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Customer Service
Complaint triage and first-draft response
Classify incoming complaints by type and urgency, then generate a first-draft response. Agent reviews, personalises, and sends. Faster turnaround, consistent tone.
Knowledge base chatbot (FAQ deflection)
Answer common customer questions using a RAG system over the knowledge base. Deflects repeat queries from contact centre. Escalates complex issues to humans.
Customer feedback synthesis
Aggregate and summarise customer reviews, survey responses, and support tickets into themes. Weekly digest for product and commercial teams.
AI Transformation Playbook
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