Course Taxonomy: Data & AI

Tier 1: AI Foundations 

3 hours · 2 workshops 

W1.1 — What is AI (Really?) 

Cut through the hype and build a clear, grounded understanding of what AI is, how it works, and where it can — and cannot — be applied in your organisation. Participants explore the difference between AI, machine learning, generative AI, and large language models, and learn how to evaluate AI tools with confidence. 

W1.2 — How Generative AI Works 

Go deeper into generative AI — how it produces outputs, why hallucinations happen, and what that means for safe use at work. Participants build their first repeatable AI workflow templates and leave with a proven prompting framework they can apply immediately. 

Tier 2: AI Productivity 

4.5 hours · 3 workshops 

W2.1 — Building Your AI Toolkit 

Explore the landscape of AI tools available today and learn how to choose the right one for any given task or risk profile. Participants leave with a personal AI toolkit matched to their role and day-to-day responsibilities. 

W2.2 — Identifying AI Workflow Use Cases 

Learn to spot high-value AI opportunities within existing workflows using a structured prioritisation approach. Participants apply the Assist · Automate · Optimise classification model and complete a Value × Feasibility portfolio exercise for their own team context. 

W2.3 — Designing Agentic AI-Enabled Workflows 

Move from individual AI tasks to end-to-end agentic workflows. Participants design human-in-the-loop AI workflows and apply the AI Initiative Readiness Canvas to assess whether an initiative is ready to move forward — or not. 

Tier 3: AI for Strategic Leaders 

4.5 hours · 3 workshops 

W3.1 — AI Operating Model & Readiness 

Assess your organisation’s readiness across five key pillars and determine the right AI operating model for your context — including whether to build, buy, or partner. Participants map AI value drivers to business KPIs and choose the right Centre of Excellence model for their organisation. 

W3.2 — AI Strategy & Investment 

Move from AI curiosity to a credible, sequenced investment roadmap. Participants build an H1/H2/H3 strategic AI roadmap, prioritise initiatives against business outcomes, and develop the language and frameworks to make confident AI investment decisions. 

W3.3 — AI Risk, Governance & Ethics 

Understand the governance landscape and build a framework for responsible AI deployment. Participants explore key regulatory frameworks including the EU AI Act, NIST RMF, and ISO 42001, and leave with a governance posture appropriate for their organisation's risk profile. 

Module 1: GenAI 101 for Insurance

• LLM fundamentals (transformers, context, hallucinations)
• Insurance-specific use cases:
• Underwriting decision support
• Claims automation and summarization
• Fraud detection insights
• Policy document generation and analysis
• Customer service automation (chatbots, call summaries)
• Risks: bias in underwriting, regulatory violations, data privacy

Hands-on Lab: Identify 5 high-value use cases (e.g., claims triage, underwriting risk scoring); 

classify by risk, compliance impact, and ROI

Module 2: Governance, Compliance & Model Risk

• Regulatory landscape (NAIC guidelines, data privacy laws, internal compliance)
• Model risk management (MRM) and explainability
• AI ethics: fairness, bias detection, auditability

Workshop: Create an Insurance AI Governance Framework including:

• Acceptable use policies, Model validation checkpoints, Audit trails and explainability requirements

Module 3: Prompt Engineering for Insurance Workflows

• Prompt design for business scenarios:
• Claims summarization
• Underwriting analysis
• Policy interpretation
• Human-in-the-loop validation patterns

Lab: Use prompts to:

• Summarize claims documents
• Generate underwriting insights
• Draft policy explanations for customers

Module 4: AI for Engineering & Product Development

• AI-assisted development (Copilot, code generation)
• Accelerating API development for insurance platforms
• Documentation automation for compliance

Lab: Generate:

• Claims processing API
• Test cases and documentation using AI tools

Module 5: AI in SDLC & DevOps

• AI integration across:
• Requirements → user stories → acceptance criteria
• Code → testing → deployment
• Traceability and auditability in regulated environments

Lab: Use AI to:

• Convert business requirements into user stories and test cases
• Generate code and track outputs for compliance

Module 6: Testing, QA & Validation

• AI-assisted testing:
• Test case generation
• Edge case detection
• Regression automation
• Validation requirements for insurance systems

Lab: Generate and execute:

• Test scenarios for claims workflows
• Validate underwriting rules

Module 7: DevOps, Observability & Risk Monitoring

• AI-enhanced monitoring and anomaly detection
• Detecting fraud patterns and system anomalies
• AI-assisted incident management

Lab: Simulate:

• Fraud detection scenario
• AI-driven anomaly analysis

Module 8: Data, RAG & Intelligent Insurance Systems

• Retrieval-Augmented Generation (RAG) for:
• Policy documents
• Claims history
• Regulatory guidelines
• Secure data access and governance

Lab: Build a RAG-based assistant for:

• Claims adjusters
• Underwriters

Module 9: Adoption Strategy, Metrics & Scaling

• AI adoption roadmap:
• Pilot → scale → enterprise rollout
• Organizational readiness and change management
• KPIs:
• Claims processing time reduction
• Loss ratio improvement
• Customer satisfaction (NPS)
• Fraud detection accuracy

Workshop: Create a 90-day AI adoption roadmap for:

• Claims
• Underwriting
• Customer experience

Module 1: GenAI 101 for Manufacturing

• LLM fundamentals (transformers, context, hallucinations)
• Manufacturing-specific use cases:
• Predictive maintenance and asset monitoring
• Production planning and scheduling optimization
• Quality inspection and defect analysis
• Digital work instructions and SOP automation
• Supply chain demand forecasting
• Risks: safety, operational downtime, data leakage, system reliability

Hands-on Lab: Identify 5 high-value use cases (e.g., predictive maintenance, quality 

automation); classify by risk, operational impact, and ROI

Module 2: Governance, Compliance & Safety

• Industry regulations (ISO standards, OSHA, quality compliance)
• Cybersecurity in manufacturing (OT/IT convergence risks)
• AI governance frameworks (model validation, auditability)

Workshop: Create a Manufacturing AI Governance Framework including:

• Safety controls and human oversight
• Data classification and usage policies
• Operational risk mitigation strategies

Module 3: Prompt Engineering for Operations

• Prompt design for manufacturing scenarios:
• Root cause analysis
• Equipment troubleshooting
• Production optimization
• Human + AI collaboration in shop floor environments

Lab: Use prompts to:

• Diagnose equipment issues
• Generate corrective actions
• Summarize production reports

Module 4: AI for Engineering & Development

• AI-assisted coding for manufacturing systems (MES, ERP integrations)
• Automating technical documentation (SOPs, work instructions)
• Legacy system modernization

Lab: Generate:

• API for production tracking
• Automated documentation for processes

Module 5: AI in SDLC & DevOps

• Integrating AI across:
• Requirements → engineering design
• Development → testing → deployment
• Traceability in regulated manufacturing environments

Lab: Use AI to:

• Convert production requirements into system features
• Generate code and validation artifacts

Module 6: Testing, QA & Quality Assurance

• AI-assisted quality testing:
• Defect detection
• Test case generation
• Regression testing
• Quality assurance in manufacturing systems
• Lab: Generate and execute:
• Test cases for production workflows
• Quality validation scenarios

Module 7: DevOps, Observability & Smart Factory

• AI-driven monitoring and anomaly detection
• Predictive analytics for equipment and production lines
• Incident detection and resolution

Lab: Simulate:

• Production anomaly detection
• AI-driven root cause analysis

Module 8: Data, RAG & Intelligent Manufacturing

• RAG with:
• Machine sensor data
• Maintenance logs
• Supply chain data
• Secure data access across systems (MES, ERP, IoT platforms)

Lab: Build a RAG-based assistant for:

• Maintenance engineers
• Production supervisors

Module 9: Adoption Strategy, Metrics & Scaling

• AI adoption roadmap:
• Pilot → scale → enterprise rollout
• IT/OT alignment and workforce readiness
• KPIs:
• Downtime reduction
• Yield improvement
• Quality defect reduction
• Supply chain efficiency

Workshop: Create a 90-day AI adoption roadmap for:

• Production
• Maintenance
• Supply chain

Module 1: GenAI 101 for Utilities

• LLM fundamentals (transformers, context, hallucinations)
• Utilities-specific use cases:
• Grid load forecasting
• Outage prediction and restoration support
• Asset maintenance insights (predictive maintenance)
• Customer service automation (billing, outage inquiries)
• Regulatory reporting automation
• Risks: grid reliability, safety, cybersecurity, data exposure

Hands-on Lab: Identify 5 high-value use cases (e.g., outage management, asset monitoring); 

classify by risk, regulatory impact, and ROI

Module 2: Governance, Compliance & Security

• NERC CIP compliance considerations
• Data privacy (customer data, smart meter data)
• Cybersecurity risks in critical infrastructure
• AI governance frameworks (model risk, auditability)

Workshop: Create a Utility AI Governance Framework including:

• Allowed/blocked use cases
• Data classification policies
• Human-in-the-loop controls

Module 3: Prompt Engineering & Operational Decision Support

• Prompt design for operational scenarios
• AI-assisted troubleshooting and incident response
• “Human + AI” collaboration for grid operators

Lab: Use prompts to:

• Analyze outage scenarios
• Generate restoration plans
• Summarize field reports

Module 4: AI for Engineering & Development (Copilot / Automation)

AI-assisted coding for utility systems (SCADA integrations, APIs)

Documentation automation for compliance and audits

Code modernization (legacy systems → cloud)

Lab: Generate:

• API service for outage reporting
• Unit tests and documentation using AI tools

Module 5: AI in the Utility SDLC & DevOps

• Integrating AI into:
• Requirements (use case modeling)
• Development (code generation)
• Testing (automation)
• Deployment (CI/CD pipelines)
• Ensuring traceability for regulated environments

Lab: Use AI to:

• Convert requirements into test cases and code
• Track outputs for compliance and audit

Module 6: Testing, QA & Reliability

• AI-assisted test generation (functional, regression, edge cases)
• Testing critical infrastructure systems
• Reliability and resilience testing

Lab: Generate and execute:

• Test scenarios for outage management systems
• Performance and reliability validation

Module 7: DevOps, Observability & Grid Reliability

• AI-enhanced monitoring and alerting
• Predictive anomaly detection in grid systems
• Incident management with AI insights

Lab: Simulate:

• Grid anomaly detection
• AI-driven root cause analysis

Module 8: Data, RAG & Smart Grid Intelligence

• Using Retrieval-Augmented Generation (RAG) with:
• Asset data
• GIS systems
• Smart meter data
• Secure data access and governance

Lab: Build a RAG-based assistant for:

• Asset maintenance queries
• Field technician support

Module 9: Adoption Roadmap & Metrics

• AI adoption strategy for utilities:
• Pilot → scale → enterprise rollout
• Organizational readiness (IT + OT alignment)

KPIs:

• Outage reduction time
• Customer satisfaction
• Maintenance cost savings
• Operational efficiency

Workshop: Create a 90-day AI adoption roadmap for:

• Grid operations
• Customer experience
• Asset management

Module 1: GenAI 101 for Biopharma 

• LLMs/transformers, context windows, grounding/RAG, hallucinations.
• High-value biopharma use cases: protocol parsing, validation docs, SOP helpers, QMS 
• tooling, CSV test scaffolds, PV triage, MFG exception analysis.
• Risks: data leakage, IP, bias, safety.

Hands-on Lab: Identify 5 internal use cases; classify by value/risk; map to guardrails.

Module 2: Compliance & Guardrails 

• GxP/Part 11, data residency, access control, audit trails, model risk tiers.
• “Allow/deny” patterns, red-team prompts, record retention & attribution.

Workshop: Draft a 1-page team “AI Use Policy” + prompt safety checklist.

Module 3: Prompt Engineering & Vibe Coding 

• Task decomposition, role prompting, chain-of-thought proxies, critique loops.
• Vibe coding patterns: co-creation sessions, driver/navigator with AI, guardrail breaks, 
• acceptance criteria alignment.

Lab: Turn a user story into design notes, stubs, and tests via vibe coding.

Module 4: GitHub Copilot Essentials 

• Copilot Chat, inline completions, test generation, code refactors, doc blocks.
• Repo policy, telemetry settings, secret hygiene, license/IP considerations.

Lab: Demo Copilot in a sandbox repo; generate a service + unit tests; log what was AI-generated 

for audit.

Module 5: AI in the SDLC 

• Requirements → acceptance criteria → code → tests → docs → reviews.
• Traceability with issues/PRs; storing prompts/outputs as validation artifacts.

Lab: How can you use AI to aid value delivery in your lifecycle?

Module 6: Testing, QA & Validation 

• AI for unit/integration tests, boundary & property tests, mutation testing.
• CSV/CSA alignment: objective evidence, independence, change control.

Lab: Generate test suites with Copilot, run, capture evidence in pipeline.

Module 7: DevOps, CI/CD & Observability

• AI-assisted pipelines (lint, SAST/DAST, SBOM), policy-as-code, gated deploys.
• ChatOps for PR review and post-deploy checks.

Lab: Add AI-generated pipeline steps; enforce policy gates; store build artifacts for audit.

Module 8: RAG, Data Safety & Domain Grounding 

• Safe retrieval (vector stores, ACLs), PHI/PII handling, prompt shielding.
• When to prefer patterns over free-form generation (templates, controlled gen).

Lab: Demo a RAG helper showcasing how agents can support delivery

Module 9: Adoption Plan, Metrics & Next Steps 

• Roles & responsibilities, ambassador model, training paths, sandbox → pilot → scale.
• KPIs: lead time, escaped defects, validation effort saved, rework, security findings.