This two-day programme is built for the people who run customer service, support and contact-centre teams and who now have to decide where AI belongs in the service desk. Customer service runs on written work: tickets, chats, emails, complaints, escalation notes, knowledge articles and quality reviews. Generative AI classifies, drafts and summarises that work, and an AI agent goes one step further: given a goal, it reads the ticket, applies the routing rules, drafts the first reply and hands the result to a person for approval. The decisions that follow sit with the service leader: which work is handed over, under which rules, and on what evidence the result can be trusted in front of a customer.
Day one establishes how generative AI works, takes every participant through structured prompting on their own customer enquiries, and applies AI across the service workflow: ticket triage and routing, response drafting and complaint handling, knowledge assistants grounded in policy, and sentiment and theme analysis across ticket history. Day two moves from assistants to agents. Participants design and build a ticket triage and first-reply agent on a support inbox carrying realistic ambiguity and planted edge cases, instrument it and read its traces, metrics and costs, then sit as the review board on a deployed support agent's pilot output and issue a go or no-go memo. Governance for customer-facing AI and the ninety-day plan close the programme.
Every lab runs in a pre-configured cloud environment opened from a browser, with no software to install. Participants choose a visual build track or a coding-agent track, and each lab is verified against fixed acceptance criteria. Participants leave with a working triage agent, a service prompt library, an evidence pack of traces and metrics, governance starting points for AI in front of customers, and a ninety-day plan for their function. The programme finishes on where the field is heading: agentic AI, where a support agent is given a goal and plans and executes the work under human supervision, with context engineering, agentic retrieval-augmented generation, the Model Context Protocol and voice agents.