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Duration 7 hours
Course Outline
Discovery Prior to Development
- Understanding why most AI initiatives fail at the scoping stage
- Viewing discovery as a core engineering activity
- Applying time-boxing and establishing exit criteria for discovery
Analyzing the Workflow
- Observing work in its natural environment
- Mapping processes, handoffs and exception handling paths
- Interviewing end-users, managers and control functions
Evaluating Use Cases
- Applying filters for feasibility, value and readiness
- Scoring a portfolio of ideas rather than isolated concepts
- Justifying the rejection of use cases with supporting evidence
Value Architecture and Business Rationale
- Establishing baselines, quantifying benefits and calculating cost to serve
- Selecting metrics that meet finance and audit standards
- Maintaining registers for risk, compliance and dependencies
Commitments, Gates and Transition
- Defining scope statements, assumptions and exclusions
- Implementing stage gates and kill criteria
- Preparing a handover brief for the development team
Requirements
- Professional experience in a client-facing technical or delivery position
- Working knowledge of business processes within at least one industry sector
- Foundational skills in spreadsheets and document creation for modelling and reporting
Target Audience
- Forward-deployed engineers and solution consultants
- Pre-sales specialists and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leaders evaluating AI portfolios