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Duration 7 hours
Course Outline
Discovery Prior to Implementation
- Why the majority of AI projects fail during the scoping phase
- Viewing discovery as a core engineering activity
- Establishing time limits and exit criteria for the discovery process
Analyzing the Workflow
- 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 a portfolio of ideas rather than isolated concepts
- Documenting the rationale for rejecting specific use cases
Value Architecture and Business Justification
- Defining baselines, benefits, and cost-to-serve
- Selecting metrics acceptable to finance and audit teams
- Maintaining a register of risks, compliance issues, and dependencies
Commitments, Milestones, and Transition
- Defining scope statements, assumptions, and exclusions
- Setting stage gates and clear termination criteria
- Preparing a handover brief for the implementation team
Requirements
- Background in a client-facing technical or delivery position
- Understanding of business processes within at least one industry
- Basic proficiency with spreadsheets and documentation tools for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads managing AI initiatives
- Transformation leaders evaluating AI portfolios
