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Duration 7 hours
Course Outline
Discovery Preceding Development
- Why the majority of AI initiatives fail at the scoping stage
- Treating discovery as a core engineering function
- Establishing time limits and exit conditions for discovery phases
Analyzing Workflows
- Observing tasks in their actual operational context
- Documenting process maps, handoff points, and exception handling
- Conducting interviews with users, managers, and oversight functions
Vetting Use Cases
- Applying filters for feasibility, value, and readiness
- Evaluating a portfolio of ideas rather than focusing on a single concept
- Declining use cases supported by concrete evidence
Value Frameworks and Business Cases
- Defining baselines, benefits, and cost-to-serve metrics
- Selecting metrics acceptable to finance and audit departments
- Managing risks, compliance issues, and dependency registers
Commitments, Milestones, and Transition
- Defining scope, assumptions, and exclusions clearly
- Implementing stage gates and criteria for project termination
- Preparing handover briefs for the development team
Requirements
- Professional experience in customer-facing technical or delivery positions
- Understanding of business processes within at least one industry
- Competence in using spreadsheets and document tools for modeling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Pre-sales representatives and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leaders evaluating AI portfolios
