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Duration 7 hours
Course Outline
Discovery Before Development
- Understanding why most AI implementations fail at the scoping stage
- Viewing discovery as a core engineering activity
- Defining time-boxes and exit criteria for the discovery phase
Analyzing the Workflow
- Observing operations in their natural environment
- Mapping processes, handoffs, and exception handling paths
- Conducting interviews with users, managers, and control functions
Evaluating Use Cases
- Applying filters for feasibility, value, and readiness
- Scoring a portfolio of initiatives rather than a single concept
- Discarding unsuitable use cases with supporting evidence
Value Architecture and Business Cases
- Establishing baselines, projected benefits, and cost-to-serve metrics
- Selecting metrics acceptable to finance and audit departments
- Registering risks, compliance issues, and dependencies
Commitments, Gate Reviews, and Handover
- Defining scope statements, assumptions, and exclusions
- Implementing stage gates and termination criteria
- Preparing a handover brief for the development team
Requirements
- Practical experience in a customer-facing technical or delivery role
- Familiarity with business processes in at least one specific industry
- Basic proficiency in spreadsheets and documentation for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales engineers and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leaders evaluating AI portfolios
