Customer Service
Sentiment monitoring across channels
Aggregate and score customer sentiment from reviews, social mentions, and survey data. Flags emerging issues before they escalate.
60–75%
reduction in insight lag time
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
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.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
Very easy to absorb. Runs quietly in the background or gives people a helpful new input. Makes everyday work easier with almost no friction.
Tooling required
Things to consider
Channel coverage matters: if you're only monitoring reviews but not social or support tickets, the sentiment picture is incomplete.
Sentiment scoring alone isn't enough — define what triggers an alert and who owns the response when a negative trend emerges.
Many specialist tools exist for this (Brandwatch, Sprinklr, etc.) and may be faster to value than building custom. Evaluate build vs. buy first.
Before building anything custom, check whether your existing review platform or CRM already has sentiment analytics built in. Most modern platforms do. The question may be 'are we using what we have?' rather than 'do we need AI?'
Experiment starter: For one month, manually aggregate weekly sentiment signals from your top two review channels and one social channel using an LLM summary. Share the digest with the trading team each week. If they ask for it to continue, the case for automating the pipeline is made.
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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The playbook gives you the full 150-opportunity directory, scoring tools, and 100+ templates for every stage of an AI transformation programme.