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 Duration 7 hours

Course Outline

Discovery Prior to Development

  • Reasons why most AI implementations fail at the scoping stage
  • Viewing discovery as a structured engineering practice
  • Defining time boundaries and exit criteria for discovery activities

Interpreting Workflows

  • Observing operations in their real-world context
  • Mapping processes, handoffs, and exception paths
  • Conducting interviews with users, managers, and control functions

Validating Use Cases

  • Applying filters for feasibility, value, and readiness
  • Scoring entire portfolios rather than isolated ideas
  • Documenting evidence for rejecting unsuitable use cases

Value Architecture and Business Cases

  • Establishing baselines, identifying benefits, and calculating cost to serve
  • Selecting metrics acceptable to finance and audit departments
  • Registering risks, compliance issues, and dependencies

Commitments, Gateways, and Transition

  • Defining scope statements, assumptions, and exclusions
  • Setting stage gates and termination criteria
  • Preparing handover briefs for the development team

Requirements

  • Professional experience in customer-facing technical or delivery roles
  • Understanding of business processes in at least one industry sector
  • Proficiency in basic spreadsheet and document creation for modeling and reporting

Target Audience

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

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