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 Duration 7 hours

Course Outline

Discovery Prior to Implementation

  • Why most AI initiatives fail during scoping
  • Treating discovery as an engineering discipline
  • Establishing time boundaries and exit criteria for discovery

Interpreting Workflows

  • Observing work in its actual operational context
  • Process mapping, handoffs, and exception handling
  • Conducting interviews with users, managers, and control functions

Validating Use Cases

  • Applying filters for feasibility, value, and readiness
  • Scoring a portfolio of ideas rather than a single concept
  • Declining use cases with supporting evidence

Value Architecture and Business Case

  • Establishing baselines, benefits, and cost-to-serve
  • Defining metrics acceptable to finance and audit
  • Maintaining registers for risk, compliance, and dependencies

Commitments, Gates, and Transition

  • Defining scope statements, assumptions, and exclusions
  • Setting stage gates and termination criteria
  • Preparing a handover brief for the build team

Requirements

  • Background in a client-facing technical or delivery capacity
  • Understanding of business processes within at least one sector
  • Fundamental skills in spreadsheets and documentation for modelling and reporting

Target Audience

  • Forward deployed engineers and solution consultants
  • Presales and solution architects
  • Product owners and delivery leads on AI projects
  • Transformation leads evaluating AI portfolios

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