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Duration 7 hours
Course Outline
Discovery Before Build
- Understanding why most AI deployments fail during the scoping phase
- Treating discovery as a core engineering activity
- Establishing time-boxes and exit criteria for discovery efforts
Reading the Workflow
- Observing work processes in their natural environment
- Analyzing process maps, handoffs, and exception paths
- Conducting interviews with end users, managers, and control functions
Qualifying Use Cases
- Applying filters for feasibility, value, and readiness
- Scoring a portfolio of ideas rather than focusing on a single concept
- Rejecting use cases based on empirical evidence
Value Architecture and the Business Case
- Defining baselines, benefits, and cost to serve
- Utilizing 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 comprehensive handover brief for the build team
Requirements
- Experience in a customer-facing technical or delivery role
- Familiarity with business processes in at least one industry
- Basic skills in spreadsheet management and documentation for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads involved in AI engagements
- Transformation leads evaluating AI portfolios
