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Duration 7 hours
Course Outline
Discovery Prior to Development
- Reasons why most AI implementations fail at the scoping stage
- Viewing discovery as a structured engineering practice
- Defining time boundaries and exit criteria for discovery activities
Interpreting Workflows
- Observing operations in their real-world context
- Mapping processes, handoffs, and exception paths
- Conducting interviews with users, managers, and control functions
Validating Use Cases
- Applying filters for feasibility, value, and readiness
- Scoring entire portfolios rather than isolated ideas
- Documenting evidence for rejecting unsuitable use cases
Value Architecture and Business Cases
- Establishing baselines, identifying benefits, and calculating cost to serve
- Selecting metrics acceptable to finance and audit departments
- Registering risks, compliance issues, and dependencies
Commitments, Gateways, and Transition
- Defining scope statements, assumptions, and exclusions
- Setting stage gates and termination criteria
- Preparing handover briefs for the development team
Requirements
- Professional experience in customer-facing technical or delivery roles
- Understanding of business processes in at least one industry sector
- Proficiency in basic spreadsheet and document creation for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leads evaluating AI portfolios