Programme overview
Technical depth: Practitioner · Practical mode: Case study
Introduction
Most organisations now have AI tools and enthusiastic teams, but few business units turn that interest into pilots that scale. Managers are asked to sponsor AI work without a reliable way to judge which problems suit AI, what data they need, or when a pilot has proved its value. This Core Concept course gives business leaders a structured method for finding, prioritising, piloting and scaling AI use cases with their data science and risk colleagues. Case material spans several sectors, and participants leave with an AI Opportunity Portfolio and Pilot Charter for their own business unit.
Course Objectives
- Explain AI, machine learning, generative AI and AI agents in business terms and match each to the kinds of problems it solves
- Assess a business unit's readiness for AI across data, skills, processes and technology
- Identify and prioritise AI use cases with a use case canvas and an impact-feasibility matrix
- Brief data science teams with clear problem statements, success metrics and data requirements
- Design pilots with baselines, controls and scale-up criteria, and redesign the workflows around them
- Identify and assign accountability for AI risks including bias, hallucination, drift and data protection
Target Audience
- Business unit and functional managers accountable for performance and improvement targets
- Operations and service delivery managers looking to apply AI to processes and customer journeys
- Product and digital managers who own solutions that increasingly embed AI features
- Strategy, planning and transformation managers who coordinate improvement portfolios
- Commercial, marketing and sales managers responsible for revenue and customer growth
Course Outline
Day 1: AI Foundations and Business Unit Readiness
- AI Terminology Using ISO/IEC 22989: Machine Learning, Generative AI and AI Agents
- AI Capability Map: Prediction, Classification, Generation, Optimisation and Automation
- Cross-Sector Use Case Review: Retail, Energy, Healthcare and Public Services
- Business Unit AI Readiness Assessment Across Data, Skills, Process and Technology
- Task-Level Work Analysis for Augmentation Versus Automation
Day 2: AI Project Lifecycles and Selection Frameworks
- CRISP-DM Lifecycle for Leading Data Science Projects
- ISO/IEC 5338 AI System Life Cycle Stages for Business Owners
- AI Use Case Canvas: Problem, Users, Data, Decision and Value
- Impact-Feasibility Prioritisation Matrix for AI Opportunities
- Human-in-the-Loop Decision Design Patterns
Day 3: Sponsoring and Running AI Pilots
- Problem Statement and Success Metric Drafting for a Data Science Team
- Data Availability and Quality Checks Using ISO/IEC 5259 Characteristics
- Pilot Design with Baselines, Control Groups and A/B Testing
- Swimlane Process Mapping for Human-AI Workflow Redesign
- Generative AI Assistant Rollout Checklist for Team Productivity
Day 4: Risks, Failure Patterns and Scale-Up
- AI Risk Identification Using the NIST AI RMF Map Function
- Bias, Hallucination and Model Drift Warning Signs for Business Owners
- Data Protection Screening for AI Use Cases Using ISO/IEC 27701 Controls
- Stage-Gate Scale-Up Criteria and Common Pilot Failure Patterns
- RACI Accountability Matrix for Business, Data, IT and Risk Functions
Day 5: Case Work and the AI Opportunity Portfolio
- Customer Service Case Study: A Chatbot Pilot That Failed to Scale
- Supply Chain Case Study: Adoption of a Demand Forecasting Model
- AI Opportunity Portfolio Build for an Own Business Unit
- Pilot Charter Drafting with Metrics, Risks and Owners
- Leadership Panel Review and Charter Defence
Skills You Will Gain
- AI Opportunity Identification
- Use Case Prioritisation
- Data Science Project Sponsorship
- Pilot Design and Evaluation
- Human-AI Workflow Design
- AI Risk Awareness
- Data Readiness Assessment
- Cross-Functional Accountability Design
Why Attend This Course
- Return to work with an AI Opportunity Portfolio and Pilot Charter your business unit can act on next quarter
- Hold productive conversations with data scientists and vendors because you can frame problems and metrics in terms they can build to
- Recognise early whether a pilot is heading for scale or for the shelf, and why
- Learn from managers in other sectors and countries who are sponsoring similar AI initiatives
Conclusion
AI changes business results only when leaders choose the right problems, prepare the data and redesign the work around the tool. This course moves from AI foundations and readiness, through the lifecycles and selection frameworks that structure AI projects, to the pilot design, risk and scale-up decisions that separate lasting use cases from abandoned experiments. The final day turns that material into an AI Opportunity Portfolio and Pilot Charter that participants take back to their business unit.