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Duration 7 hours
Course Outline
Discovery Before Development
- Common reasons AI deployments fail during the scoping phase
- Treating discovery as a core engineering discipline
- Setting time limits and exit criteria for discovery phases
Interpreting Workflows
- Observing operations in their actual working environment
- Analyzing process maps, handoff points, and exception handling paths
- Conducting interviews with end users, managers, and control functions
Evaluating Use Cases
- Applying filters for feasibility, value, and organizational readiness
- Scoring entire portfolios rather than isolated ideas
- Dismissing unsuitable use cases based on solid evidence
Value Architecture and Business Justification
- Establishing baselines, quantifying benefits, and assessing service costs
- Identifying metrics acceptable to finance and audit departments
- Maintaining registers for risks, compliance, and dependencies
Agreements, Milestones, and Transition
- Defining scope, stating assumptions, and listing exclusions
- Implementing stage gates and kill criteria
- Preparing handover briefs for the development team
Requirements
- Background in customer-facing technical or delivery roles
- Understanding of business processes within at least one industry sector
- Proficiency with basic spreadsheet and document tools for modeling and reporting
Target Audience
- Frontline engineers and solution consultants
- Pre-sales specialists and solution architects
- Product owners and delivery leads managing AI projects
- Transformation leaders evaluating AI portfolios
