Customer Service
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.
20–35%
reduction in contact centre volume
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
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
Klarna's AI chatbot achieved a 75% self-resolution rate with a 25% drop in repeat inquiries, available 24/7 across 23 markets and 35+ languages (OpenAI case study).
Knowledge base quality is everything — incomplete, outdated, or poorly structured content produces wrong or unhelpful answers at scale.
Escalation logic is as important as the bot itself: define clearly when the bot hands off to a human, and make the handoff seamless.
Customer experience risk: a bot that confidently gives wrong answers damages trust more than having no bot. Invest in evaluation before launch.
Run a shadow period before go-live: process real customer queries through the bot without responding to customers, and use the output to identify failure modes and refine the knowledge base.
Experiment starter: Identify your top 20 most-asked questions from support ticket data. Build a simple RAG system over your FAQ and policy documents and have the customer service team test it with those questions for one week. Only progress to a customer-facing pilot when internal accuracy exceeds 85%.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Customer Service
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Return reason categorisation
Automatically classify free-text return reasons into structured categories. Feeds merchandising and quality teams with actionable data.
AI Transformation Playbook
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