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Duration 7 hours
Course Outline
Discovery Prior to Development
- Understanding why the majority of AI rollouts fail during scoping
- Viewing discovery as a core engineering activity
- Defining time limits and exit criteria for the discovery phase
Workflow Analysis
- Observing tasks in their actual operational context
- Mapping processes, handoffs and exception handling paths
- Conducting interviews with users, managers and control functions
Validating Use Cases
- Applying filters for feasibility, value and organisational readiness
- Scoring a portfolio of ideas rather than isolating a single concept
- Dismissing 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 departments
- Maintaining a register of risks, compliance issues and dependencies
Commitments, Milestones and Transition
- Defining scope statements, assumptions and out-of-scope items
- Implementing stage gates and kill criteria
- Preparing a handover brief for the build team
Requirements
- Practical experience in a customer-facing technical or delivery capacity
- Working knowledge of business processes within at least one industry sector
- Proficiency in basic spreadsheet and document management for modelling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads managing AI projects
- Transformation leads evaluating AI portfolios
