AI for Customer Service and CRM
Processes, use cases and architecture
Ticket classification, response suggestions grounded in a knowledge base and interaction summaries. RAG retrieves approved content and a CRM connector creates a draft. Sending messages or changing commercial commitments requires appropriate approval.
Required data and applications
CRM, request history, catalogue, contracts, knowledge base and escalation rules. Information belonging to different customers remains segregated.
Human accountability and limitations
Response time, first-contact resolution, correction rate and satisfaction. Sensitive or out-of-scope requests go to an adviser. Tone and factual accuracy are evaluated separately.
Measuring benefits
The pilot compares a baseline with the target process on representative cases. Business owners define quality thresholds and the right to revert to manual processing before launch.
Bringing the right expertise together
FAQ
How should an AI programme be measured?
Compare outcomes with a baseline: quality, end-to-end time, total cost, adoption and incidents. Measure review effort and errors, not just the number of generated answers.