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Duration 7 hours
Course Outline
Discovery Preceding Development
- Understanding why the majority of AI projects fail at the scoping stage
- Treating discovery as a core engineering function
- Establishing time boundaries and exit criteria for discovery phases
Analyzing Workflows
- Observing tasks in their actual operational context
- Creating process maps, identifying handoffs, and tracing exception paths
- Conducting interviews with end users, managers, and oversight functions
Validating Use Cases
- Applying filters for feasibility, value, and organizational readiness
- Evaluating a portfolio of ideas rather than focusing on a single concept
- Utilizing evidence to reject unsuitable use cases
Value Frameworks and Business Justification
- Defining baselines, benefits, and cost-to-serve metrics
- Selecting metrics that satisfy finance and audit standards
- Maintaining registers for risks, compliance, and dependencies
Agreements, Milestones and Transition
- Defining scope statements, assumptions, and exclusions
- Implementing stage gates and stop-work criteria
- Preparing handover documentation for the development team
Requirements
- Experience in technical or delivery roles with client interaction
- Understanding of business processes within at least one industry sector
- Competence in basic spreadsheet and document creation for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales engineers and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leaders evaluating AI portfolios
