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Duration 7 hours
Course Outline
Discovery Before Development
- Understanding why most AI deployments fail at the scoping stage
- Approaching discovery as a core engineering activity
- Establishing time-boxes and exit criteria for discovery phases
Analyzing Workflows
- Observing work processes in their natural environment
- Documenting process maps, handoffs, and exception paths
- Conducting interviews with users, managers, and control functions
Evaluating Use Cases
- Applying feasibility, value, and readiness filters
- Scoring a portfolio of ideas rather than focusing on a single concept
- Rejecting use cases with supporting evidence
Value Architecture and Business Cases
- Defining baselines, benefits, and cost-to-serve models
- Selecting metrics acceptable to finance and audit teams
- Maintaining a register of risks, compliance issues, and dependencies
Commitments, Gates, and Handover
- Defining scope statements, assumptions, and exclusions
- Implementing stage gates and kill criteria
- Preparing a handover brief for the build team
Requirements
- Professional experience in a customer-facing technical or delivery capacity
- Knowledge of business processes within at least one specific industry
- Proficiency in basic spreadsheet and document 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 leads evaluating AI portfolios