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Duration 7 hours
Course Outline
Discovery Prior to Development
- Understanding why most AI projects fail at the scoping phase
- Viewing discovery as a core engineering function
- Setting time limits and exit criteria for discovery efforts
Analyzing the Workflow
- Observing work in its actual environment
- Examining process maps, handoff points, and exception handling
- Conducting interviews with users, managers, and control functions
Evaluating Use Cases
- Applying filters for feasibility, value, and readiness
- Scoring a portfolio of ideas rather than focusing on a single concept
- Discarding use cases with data-driven justification
Value Architecture and Business Justification
- Establishing baselines, benefits, and cost-to-serve metrics
- Selecting metrics acceptable to finance and audit teams
- Maintaining a register for risks, compliance issues, and dependencies
Commitments, Gates, and Transition
- Defining scope statements, assumptions, and exclusions
- Implementing stage gates and kill criteria
- Preparing a handover brief for the build team
Requirements
- Hands-on experience in a customer-facing technical or delivery position
- Working knowledge of business processes in at least one industry
- Competency in spreadsheet tools and documentation for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales specialists and solution architects
- Product owners and delivery leads managing AI projects
- Transformation leads evaluating AI portfolios