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 Duration 7 hours

Course Outline

Discovery Prior to Development

  • Understanding why most AI projects fail at the scoping phase
  • Viewing discovery as a core engineering function
  • Setting time limits and exit criteria for discovery efforts

Analyzing the Workflow

  • Observing work in its actual environment
  • Examining process maps, handoff points, and exception handling
  • Conducting interviews with users, managers, and control functions

Evaluating Use Cases

  • Applying filters for feasibility, value, and readiness
  • Scoring a portfolio of ideas rather than focusing on a single concept
  • Discarding 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 teams
  • Maintaining a register for risks, compliance issues, and dependencies

Commitments, Gates, and Transition

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

Requirements

  • Hands-on experience in a customer-facing technical or delivery position
  • Working knowledge of business processes in at least one industry
  • Competency in spreadsheet tools and documentation for modeling and reporting

Target Audience

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

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