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Duration 7 hours
Course Outline
Discovery Before Build
- Understanding why most AI deployments fail at the scoping stage
- Treating discovery as a core engineering activity
- Establishing time-boxes and exit criteria for the discovery phase
Reading the Workflow
- Observing work in its actual operational context
- Analysing process maps, handoffs, and exception paths
- Conducting interviews with users, managers, and control functions
Qualifying Use Cases
- Applying feasibility, value, and readiness filters
- Scoring a portfolio of use cases rather than focusing on a single idea
- Using evidence to justify the rejection of unsuitable use cases
Value Architecture and the Business Case
- Defining baselines, benefits, and cost to serve
- Selecting metrics accepted by finance and audit teams
- Maintaining a register for risks, compliance, and dependencies
Commitments, Gates, and Handover
- Drafting scope statements, assumptions, and exclusions
- Defining stage gates and kill criteria
- Preparing a handover brief for the build team
Requirements
- Background in a customer-facing technical or delivery role
- Knowledge of business processes in at least one industry
- Basic proficiency in spreadsheets and document creation for modelling and reporting
Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads involved in AI engagements
- Transformation leads evaluating an AI portfolio
