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 Duration 7 hours

Course Outline

Discovery Before Build

  • Why most AI deployments fail at scoping
  • Discovery as an engineering activity
  • Time-boxing and exit criteria for discovery

Reading the Workflow

  • Observing work where it actually happens
  • Process maps, handoffs and exception paths
  • Interviewing users, managers and control functions

Qualifying Use Cases

  • Feasibility, value and readiness filters
  • Scoring a portfolio rather than a single idea
  • Rejecting use cases with evidence

Value Architecture and the Business Case

  • Baselines, benefits and cost to serve
  • Metrics that finance and audit will accept
  • Risk, compliance and dependency register

Commitments, Gates and Handover

  • Scope statements, assumptions and exclusions
  • Stage gates and kill criteria
  • Handover brief for the build team

Requirements

  • Experience in a customer-facing technical or delivery role
  • Familiarity with business processes in at least one industry
  • Basic spreadsheet and document skills for modelling and write-up

Audience

  • Forward deployed engineers and solution consultants
  • Presales and solution architects
  • Product owners and delivery leads on AI engagements
  • Transformation leads assessing an AI portfolio

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