Overview
This 5-day in-person workshop enables a cross-functional AI Task Force to operationalise enterprise AI governance and design secure, pilot-ready Generative AI (GenAI) implementations. The programme strengthens alignment between AI ambition, organisational risk appetite, and accountable operating models, while building practical capability in GenAI solution patterns (Prompt-only, RAG, Tool-Use, Agentic workflows). Participants will apply OWASP LLM Top 10-informed controls, establish minimum viable AI data governance and AI Data Pack discipline, define enforceable release gates, and design evaluation/monitoring frameworks. The programme concludes with a capstone deliverable pack, including a 90-day execution plan and a 12-month institutionalisation roadmap.
Aims of the course
The aim of this course is to enable you to gain a pass mark in the PRINCE2 Foundation certification. In order to pass the exam, you will need to:
- know and understand the PRINCE2 method well enough to be able to work effectively with, or as a member of, a project management team working within an environment supporting PRINCE2
- recall the common language and key concepts of PRINCE2
- gain an overall understanding of how to manage a PRINCE2 project.
Exam voucher is included with this training.
Course Objectives
On completing this course, the participants will:
- Understand key concepts relating to projects and PRINCE2
- Understand how the PRINCE2 principles underpin the PRINCE2 method
- Understand the importance of people in successful projects
- Understand the PRINCE2 practices and how they are applied throughout the project
- Understand the PRINCE2 processes and how they are carried out throughout the project
Target Audience
The Rapid Track AITF-303 target audience includes but is not limited to:
- Cross-functional AI Task Force members from IT and non-IT functions (e.g., Sales, HR, Operations, Sustainability, Risk/Compliance, Security, Data Governance, Enterprise Architecture).
- Executive sponsors and business owners responsible for AI pilot oversight (recommended for key sessions).
Pre-Requisites
Knowledge:
- AI fundamentals
- Basic prompting concepts
- Introductory understanding of organisational governance structures
Tooling / Access:
- Access to organisation-approved GenAI tools (if practical exercises are included)
- Use of synthetic or non-sensitive sample data for exercises
Module 1: AI Transformation Strategy & Task Force Operating Model
Key Topics
- AI as enterprise infrastructure
- AI maturity assessment (People, Process, Data, Technology, Governance)
- AI ambition levels (Assistive → Autonomous)
- AI Task Force vs Steering Committee structure
- AI lifecycle governance (Intake → Design → Build → Deploy → Monitor → Retire)
- Decision rights, escalation paths, reporting rhythm
Hands-on / Lab
- Conduct AI maturity self-assessment
- Draft AI Ambition Statement
- Develop AI Task Force Charter
- Create RACI covering lifecycle governance
Expected Outcomes
- Clear enterprise AI ambition
- Formalised AI operating model
- Defined accountability and escalation structure
Assessment / Checkpoint
Governance walkthrough: each group presents operating model and lifecycle accountability.
Module 2: Enterprise GenAI Use-Case Design
Key Topics
- Use-case intake elements
- Workflow integration and stakeholder mapping
- Human-in-the-loop design
- Risk classification per use case
- Acceptance criteria and pilot boundaries
Hands-on / Lab
- Produce structured use-case one-pager
- Define oversight checkpoints
- Prioritise shortlist of pilot candidates
Expected Outcomes
- Pilot-ready use cases
- Clear scope and operational guardrails
Assessment / Checkpoint
Use-case review board simulation (approve / conditional approve / defer).
Module 3: Enterprise AI Architecture & GenAI Solution Patterns
Key Topics
- Enterprise AI architecture layers (Interface, Orchestration, Model, Retrieval, Governance, Monitoring)
- Integration with CRM, ERP, Data Warehouse
- Prompt-only vs RAG vs Tool-Use vs Agentic patterns
- Retrieval engineering basics (chunking, metadata, access control)
- Logging and observability design
Hands-on / Lab
- Design architecture blueprint for selected use case
- Select appropriate GenAI deployment pattern
- Define minimal RAG plan (conceptual)
Expected Outcomes
- Architecture blueprint suitable for IT/security review
- Clear deployment pattern justification
Assessment / Checkpoint
Architecture review against governance and risk requirements.
Module 4: Secure GenAI – OWASP LLM Controls & Threat Modeling
Key Topics
- OWASP LLM Top 10
- Prompt injection attacks
- Data leakage risks
- Insecure output handling
- Over-reliance and hallucination risk
- Defence-in-depth approach
- Kill-switch governance and incident response linkage
Hands-on / Lab
- Prompt injection tabletop exercise
- Develop AI Security Baseline checklist
Expected Outcomes
- Minimum viable GenAI security control framework
- Defined incident escalation flow
Assessment / Checkpoint
Tabletops debrief and control validation session.
Module 5: AI Data Governance & AI Data Pack Discipline
Key Topics
- Data classification model
- Permitted vs restricted AI data
- Access control and retention rules
- Provenance and lineage requirements
- AI Data Pack structure
Hands-on / Lab
- Build AI Data Pack for selected pilot use case
- Populate inventory, owners, retention, and quality checks
Expected Outcomes
- Reusable AI Data Governance framework
- Completed AI Data Pack per pilot
Assessment / Checkpoint
Peer review of AI Data Packs.
Module 6: Responsible AI & Release Gates
Key Topics
- Translating Responsible AI principles into practice
- Human oversight models
- Transparency expectations
- Documentation requirements
- Release gate checklist structure
Hands-on / Lab
- Define release gate checklist
- Draft Responsible AI policy pack (v1)
Expected Outcomes
- Operational release gate model
- Structured Responsible AI policy draft
Assessment / Checkpoint
Gate review simulation for candidate pilot.
Module 7: Evaluation & Monitoring Framework (Advanced)
Key Topics
- Grounded accuracy evaluation
- Harmful content and bias detection
- Privacy leakage indicators
- Drift detection and regression testing
- Red-teaming prompt design
- Monitoring cadence and escalation criteria
Hands-on / Lab
- Build evaluation and monitoring plan for selected use case
- Define pass/fail thresholds
Expected Outcomes
- Measurable quality & safety framework
- Monitoring KPI dashboard design
Assessment / Checkpoint
Evaluation framework sign-off review.
Module 8: 90-Day Execution & 12-Month Institutionalisation Roadmap (Capstone)
Key Topics
- Pilot sequencing
- Governance rhythm
- Owner assignment
- Risk tracking
- Institutional capability development
- Reporting structure for executive oversight
Hands-on / Lab
Deliverable pack per group including:
- Use-case specification
- Architecture blueprint
- Security baseline
- AI Data Pack
- Release gate checklist
- Evaluation & monitoring plan
- 90-day action plan
- 12-month institutionalisation roadmap
Expected Outcomes
- Complete, enterprise-ready AI blueprint
- Structured transformation roadmap
Assessment / Checkpoint
Final presentation and executive challenge simulation.
End-of-Programme Deliverables
Participants leave with:
- AI Ambition Statement
- AI Governance & Operating Model
- Pilot Use-Case Portfolio
- Architecture Blueprint
- AI Security Baseline
- AI Data Governance Framework
- AI Data Pack (Pilot-Ready)
- Responsible AI Release Gates
- Evaluation & Monitoring Framework
- 90-Day Execution Plan
- 12-Month Institutionalisation Roadmap
Programme scope, delivery arrangements and suitability are confirmed during enquiry.