*We customize the course outline and content to your specific needs and relevant use cases.
Module 1: Generative AI in the insurance business context
- What generative AI is and where it fits in insurance operations
- Differences between automation, predictive analytics, and generative AI
- Typical opportunities, limitations, and risk areas in regulated environments
- Practical criteria for deciding where AI adds business value
Module 2: Business cases and operating model implications
- Mapping generative AI to front office, middle office, and support workflows
- Typical value levers speed, consistency, service quality, and workload reduction
- Human in the loop design and decision accountability in insurance processes
- Choosing between quick wins, pilot cases, and broader transformation initiatives
Module 3: Generative AI for underwriting
- Summarizing broker submissions, questionnaires, and supporting documents
- Drafting risk notes, coverage comparisons, and decision support summaries
- Assisting with data extraction, clarification requests, and case preparation
- Guardrails for bias, explainability, escalation, and final underwriting judgment
Module 4: Quality, governance, and responsible use
- Data privacy, confidentiality, and handling of sensitive customer information
- Review and approval patterns for AI assisted business outputs
- Common error patterns hallucinations, omissions, and overconfident language
- Governance basics policies, ownership, usage rules, and documentation expectations
Module 5: Generative AI for claims handling
- Intake support for first notice of loss and case documentation
- Summarizing claim files, correspondence, and evidence packages
- Drafting next step communications and internal status updates
- Supporting consistency in triage, routing, and case preparation without replacing adjuster authority
Module 6: Generative AI for customer service
- Drafting customer responses with tone, clarity, and policy sensitivity
- Supporting service agents with knowledge retrieval and response suggestions
- Improving self service content, FAQs, and guided customer interactions
- Balancing efficiency with fairness, empathy, and escalation to human support
Module 7: Workflow redesign and role impact
- Identifying where AI fits into existing processes and where it should not
- Redesigning handoffs, review points, and exception handling
- Clarifying role changes across business teams, supervisors, and quality functions
- Keeping process ownership, auditability, and accountability intact
Module 8: Prompting and business interaction patterns
- Writing effective business prompts for summaries, comparisons, explanations, and drafting
- Structuring context, constraints, tone, and output format for better results
- Reusable prompt templates for underwriting, claims, and customer service tasks
- Quality checks that improve reliability before outputs are used operationally
Module 9: Use case evaluation and prioritization
- Selecting use cases based on effort, value, risk, and organizational readiness
- Distinguishing high frequency tasks from high impact specialist use cases
- Evaluating feasibility across data quality, process maturity, and stakeholder support
- Building a simple prioritization framework for insurance functions
Module 10: Risk, compliance, and trust considerations
- Managing customer trust, fairness, and transparency in AI supported workflows
- Aligning AI usage with internal controls, legal review, and compliance expectations
- Monitoring for inappropriate outputs, misuse, and operational drift
- Setting escalation paths for cases where human intervention is required
Module 11: Adoption, capability building, and change management
- Preparing teams, managers, and support functions for AI enabled work
- Training, awareness, and communication strategies that reduce resistance
- Building a practical operating model for rollout, support, and continuous learning
- Creating leadership sponsorship for safe and useful adoption
Module 12: Measuring impact and building a roadmap
- Defining meaningful business metrics for speed, quality, service, and productivity
- Tracking output quality, error rates, customer experience, and employee acceptance
- Structuring a phased implementation roadmap across functions and maturity levels
- Creating a practical action plan for the next 90 days
