AI Customer Service Strategy Training Course

Participants can build a prioritised portfolio of AI customer service use cases.

• Value-feasibility-risk matrix ranks candidate chatbot and agent-assist use cases
• Estimate deflection potential without treating containment as successful resolution
• Cost-to-serve modelling separates automation savings from implementation and governance costs
• Prioritise high-volume transactional requests before complex advisory conversations
• Service blueprint identifies frontstage, backstage, systems and ownership dependencies
• Define benefit hypotheses using customer, operational, financial and risk measures

Participants can design customer conversations that guide resolution and preserve customer choice.

• Intent taxonomy groups customer requests into manageable conversational categories
• Entities, slots and context variables capture information needed for resolution
• Conversation flows define opening, clarification, action, confirmation and closure
• Plain-language microcopy reduces ambiguity, jargon and unnecessary customer effort
• Design repair prompts for misunderstood requests and incomplete customer information
• Accessibility requirements cover reading level, language, format and assistive technology

Participants can specify how chatbots, service platforms and human channels should work together.

• Rule-based, retrieval-based and generative architectures serve different service requirements
• Channel selection considers web chat, messaging, voice and authenticated applications
• Authentication patterns protect account actions, personal data and transaction requests
• API integration requirements connect customer records, orders, cases and payments
• Session context rules determine what information persists across customer interactions
• Omnichannel hand-off preserves conversation history, intent and completed verification

Participants can prepare governed knowledge sources for accurate AI-supported service responses.

• Knowledge audit identifies duplicate articles, gaps, expired content and conflicting policies
• Content chunking separates procedures, eligibility rules, exceptions and supporting evidence
• Metadata taxonomy improves retrieval by product, journey, region, language and audience
• Retrieval-augmented generation grounds answers in approved internal knowledge sources
• Citation patterns show customers and agents the source behind critical answers
• Content ownership assigns authors, approvers, review dates and retirement responsibilities

Participants can define prompts and controls for safe, useful customer-facing AI responses.

• System prompts define role, scope, tone, prohibited actions and escalation triggers
• Few-shot examples demonstrate approved answers for recurring service scenarios
• Guardrails restrict unsupported claims, sensitive advice and unauthorised account actions
• Refusal patterns explain limitations while offering appropriate human support options
• Response rubrics assess factuality, relevance, clarity, empathy and policy compliance
• Temperature and output constraints balance consistency with conversational naturalness

Participants can design handoffs that protect continuity, accountability and agent productivity.

• Escalation decision trees distinguish customer preference, risk, failure and complexity triggers
• Warm transfer design passes transcript, intent, verification status and attempted actions
• Agent-assist prompts generate summaries, next-best actions and knowledge suggestions
• Supervisor interventions handle escalations, vulnerable customers and potential service failures
• Workforce implications cover skills, queue design, quality assurance and staffing assumptions
• Service recovery scripts address incorrect responses, delays and customer frustration

Participants can establish proportionate controls for customer-facing AI service operations.

• NIST AI RMF identifies govern, map, measure and manage activities
• ISO/IEC 42001 roles support accountable AI management system governance
• Privacy impact assessments identify personal data, purpose, retention and access risks
• Bias testing examines unequal outcomes across language, disability and customer segments
• Incident management defines detection, containment, customer communication and remediation steps
• Approval gates control model changes, content releases and material policy updates

Participants can test AI service performance and manage improvement through operational evidence.

• Test suites cover expected questions, edge cases, adversarial prompts and policy exceptions
• Conversation analytics identifies abandonment, confusion, repeat contacts and failed containment
• Quality sampling combines automated checks with calibrated human evaluator reviews
• A/B testing compares conversation designs against predefined customer and service measures
• Balanced scorecards connect customer outcomes, operational efficiency, risk and employee experience
• Root-cause analysis separates knowledge gaps, design flaws, integration failures and model errors

Participants can produce a phased plan for launching and managing AI-enabled customer service.

• Minimum viable service scope defines channels, intents, integrations and exclusions
• RACI matrix assigns accountability across service, technology, risk, legal and operations
• Pilot design sets cohort, duration, success thresholds and rollback criteria
• Change management prepares agents, supervisors, communications and revised operating procedures
• Vendor evaluation compares data controls, integration capability, observability and support commitments
• Ninety-day action plan sequences decisions, dependencies, owners and measurable milestones