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Duration 7 hours
Course Outline
Discovery Prior to Development
- Understanding why scoping errors are the primary cause of AI deployment failures.
- Viewing discovery as a core engineering activity.
- Establishing time boundaries and exit criteria for discovery phases.
Interpreting Workflows
- Observing work in its actual operational environment.
- Analyzing process maps, handoffs, and exception handling paths.
- Conducting interviews with users, managers, and control function stakeholders.
Validating Use Cases
- Applying filters for feasibility, value, and readiness.
- Scoring a portfolio of ideas rather than focusing on a single concept.
- Using evidence to reject unsuitable use cases.
Value Architecture and Business Justification
- Defining baselines, benefits, and costs to serve.
- Selecting metrics acceptable to finance and audit teams.
- Maintaining registers for risks, compliance, and dependencies.
Commitments, Milestones, and Transition
- Drafting scope statements, assumptions, and exclusions.
- Setting stage gates and criteria for project termination.
- Creating a handover brief for the development team.
Requirements
- Professional experience in a customer-facing technical or delivery capacity.
- Knowledge of business processes within at least one industry sector.
- Proficiency with spreadsheets and documentation tools for modeling and reporting.
Target Audience
- Forward deployed engineers and solution consultants.
- Presales and solution architects.
- Product owners and delivery leads managing AI projects.
- Transformation leads evaluating AI portfolios.
