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Duration 7 hours
Course Outline
Discovery Prior to Development
- Understanding why most AI projects fail at the scoping stage
- Viewing discovery as a core engineering activity
- Establishing time boxes and exit criteria for discovery phases
Analyzing the Workflow
- Observing operations in their actual working context
- Mapping processes, handoffs, and exception handling paths
- Conducting interviews with users, managers, and compliance functions
Validating Use Cases
- Applying filters for feasibility, value, and organizational readiness
- Scoring entire portfolios rather than isolated ideas
- Documenting evidence-based reasons for rejecting specific use cases
Value Architecture and Business Case Development
- Defining baselines, benefits, and cost-to-serve models
- Selecting metrics acceptable to finance and audit teams
- Managing registers for risk, compliance, and dependencies
Commitments, Gates, and Transition
- Drafting scope statements, assumptions, and exclusions
- Defining stage gates and criteria for project termination
- Preparing a handover brief for the development team
Requirements
- Practical experience in customer-facing technical or delivery positions
- Understanding of business processes within at least one industry vertical
- 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 managing AI projects
- Transformation leads evaluating AI portfolios
