Use case — Customer Service
Give service teams faster access to context, answers, and next actions.
Service quality varies with tenure and queue pressure. Retrieval-grounded drafting narrows that variance while keeping the customer-facing response an agent decision.
Agent review is preserved for consequential communication unless an approved use case defines a narrower, safer bounded automation — for example a status lookup with a fixed response format.
The outcome
Agents open a case with the history, the grounded answer, and the likely next action already assembled — and still decide what the customer receives.
The friction today
What teams describe
- Answers depend on agent memory and tenure
- Policy changes faster than the macro library
- New agents escalate cases experienced agents close quickly
- Contact summaries are skipped when queues are long
Current state, and the workflow afterwards
Current-state workflow
- ArriveContacts land across channels with inconsistent categorisation and unclear urgency.
- SearchThe agent hunts across the knowledge base, past tickets, and colleagues for an answer.
- RespondWording depends on tenure, so quality varies between agents and shifts.
- CloseAfter-call work is rushed, so summaries and CRM updates are thin and handoffs lose context.
Future-state workflow
- ClassifyThe request is classified by intent and priority and routed to the right queue.
- RetrieveApproved policy and product content is retrieved and filtered by the customer's entitlement.
- SuggestA draft response is offered with citations to the source passage.
- DecideThe agent edits and sends; the system does not reply to a customer unattended in this design.
- SummariseA structured summary and next action are written back to the service record.
Example workflows
- Intent and urgency classification
- Incoming contacts are categorised and prioritised, with low-confidence cases sent to a human triage queue.
- Knowledge-grounded answer suggestion with citations
- A suggested answer drawn only from approved knowledge, with the source article shown next to it.
- Case summary and handoff
- A structured summary so the next agent or team starts with the full context.
- Escalation recommendation
- A recommendation with the reason attached; the agent or supervisor makes the call.
- After-call work and CRM/ticket update
- Drafted wrap-up notes and field updates the agent confirms before saving.
- Quality sampling
- A sampled review set with consistent criteria, so quality is measured rather than assumed.
What AI assists, and what people decide
What AI assists
- Classifying intent, urgency, and language
- Retrieving grounded answers with citations
- Drafting responses, summaries, and wrap-up notes
- Recommending escalation and routing
What people decide
- What is actually said to the customer
- Whether to escalate, refund, or make a commitment
- Whether the retrieved answer fits this customer's situation
- When a case needs a supervisor rather than a faster reply
Approved data and systems required
Systems involved
- Service desk or ticketing
- Knowledge base
- CRM
- Entitlement or account data
Approved data inputs
- Approved policy, product, and procedure content
- Ticket history for the contact in question
- Entitlement data needed to filter what may be offered
Controls and failure modes
Human-control pattern
- Agents own every customer-facing response
- Complaints, vulnerability signals, and regulated topics route straight to a person
- Sources are cited and carry a last-reviewed date
- Low-confidence retrieval suppresses the draft rather than guessing
Failure modes
- Wrong answers from outdated content — mitigated with review dates and content ownership
- Over-trust under queue pressure, addressed with sampling and coaching
- Entitlement leakage if filtering is not enforced at retrieval time
What this does not promise
- No unattended replies to customers in this design
- No deflection target treated as a success measure on its own
- No handling of complaints or vulnerability cases without a person
Implementation path
- 1. Clean the knowledgeAgree which articles are approved, current, and citable. Retrieval quality is knowledge quality.
- 2. Start suggest-onlyAnswers are suggested to agents before anything is considered for automation.
- 3. Add summarisationCase summary and wrap-up once suggestion accuracy is understood.
- 4. Define escalationSet thresholds, exception paths, and the shutdown control.
- 5. Sample continuouslyQuality sampling and feedback loops become part of normal operations.
Measures
These are evaluation targets you agree and track together, not promised results. Baselines are measured before launch so any change is attributable, and figures stay specific to your data, volumes, and process.
What you can measure
- Time to first meaningful response
- Suggestion acceptance and edit rate
- Handling time on comparable case types
- Deflection only where the customer confirms resolution
- Sampled quality and citation accuracy
What each stakeholder needs to know
- Executive sponsor
What improves?
Consistency and response time, with the customer-facing risk contained because the reply is always an agent decision.
- Functional leader
What changes for the team?
New agents reach competent answers faster, and content gaps become visible through rejected suggestions.
- Operations leader
Where do exceptions go?
Regulated topics, complaints, and low-confidence retrievals bypass drafting and route to a named queue.
- Technology & security reviewer
What does this touch, and how is it controlled?
Retrieval is filtered by entitlement at query time, not after generation; no customer PII is added to the index; every suggestion is logged with the passages used.
- End user
What is my role?
You check the citation, edit the draft, and send. Rejecting a suggestion is useful signal, not a failure.
The right AI solution is not selected by trend. It is designed around the workflow, approved data, systems, people, risk, and measurable outcome.
Find the AI opportunities worth implementing in your business.
Start with a structured assessment of your workflows, systems, and data, and leave with a prioritized view of where AI can create real value.