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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