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Duration 7 hours
Course Outline
Discovery Before Development
- Understanding why most AI projects fail at the scoping stage
- Viewing discovery as a core engineering task
- Establishing time limits and exit conditions for the discovery phase
Analyzing the Workflow
- Observing work in its real-world context
- Documenting process maps, handovers, and exception handling
- Conducting interviews with end-users, managers, and compliance functions
Validating Use Cases
- Applying filters for feasibility, value, and readiness
- Scoring a portfolio of ideas rather than focusing on a single concept
- Using data to justify the rejection of non-viable use cases
Value Architecture & The Business Case
- Defining baselines, benefits, and service costs
- Selecting metrics that satisfy finance and audit requirements
- Maintaining a register of risks, compliance issues, and dependencies
Commitments, Milestones & Handover
- Defining scope statements, key assumptions, and exclusions
- Setting stage gates and criteria for terminating the project
- Preparing a clear handover brief for the development team
Requirements
- Professional experience in a customer-facing technical or delivery position
- Understanding 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 involved in AI projects
- Transformation leaders evaluating AI portfolios
