Customer Service
Escalation priority scoring
Score incoming tickets by urgency and escalation risk based on language signals, customer history, and issue type. Ensures high-risk contacts reach senior agents faster.
30–50%
reduction in time-to-resolution for escalation-risk contacts
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.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
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
Define what constitutes an escalation risk before building the model: legal threats, social media mentions, safety concerns, high LTV customers, repeat complaints on the same issue.
Scoring should route to the right queue, not just flag. Build the routing logic before deploying the scoring — otherwise you create an alert no-one acts on.
False negatives (missing a genuine escalation risk) are more damaging than false positives. Tune towards high recall in the early stages and let agents clear false positives.
The scoring logic should be revisited quarterly — language patterns change, and what signals risk today may not signal it in six months. Build a review process in from the start rather than treating the model as set-and-forget.
Experiment starter: Take three months of closed tickets and retrospectively identify those that escalated (to complaints, legal, social media). Prompt an LLM to score those same tickets on escalation risk using your defined criteria. Measure how many of the actual escalations the AI would have flagged. A recall rate above 80% justifies a live pilot.
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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