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Duration 7 hours
Course Outline
Discovery Prior to Implementation
- Understanding why most AI projects fail during the scoping phase.
- Viewing discovery through the lens of engineering practice.
- Establishing time boundaries and exit criteria for discovery efforts.
Analyzing Workflows
- Observing tasks as they are performed in real-world settings.
- Mapping processes, handoffs, and exception handling paths.
- Conducting interviews with users, managers, and oversight functions.
Evaluating Use Cases
- Applying filters for feasibility, value, and readiness.
- Scoring a portfolio of ideas rather than focusing on a single concept.
- Using evidence to justify the rejection of unsuitable use cases.
Value Architecture and Business Justification
- Defining baselines, benefits, and cost-to-serve metrics.
- Identifying metrics that meet financial and audit standards.
- Maintaining a register of risks, compliance issues, and dependencies.
Agreements, Milestones, and Transition
- Formulating scope statements, assumptions, and exclusions.
- Defining stage gates and criteria for project termination.
- Preparing a handover brief for the implementation team.
Requirements
- Background in customer-facing technical or delivery roles.
- Understanding of business processes within at least one industry sector.
- Proficiency in basic spreadsheet and document creation tools for modelling and reporting.
Target Audience
- Forward-deployed engineers and solution consultants.
- Pre-sales specialists and solution architects.
- Product owners and delivery leads involved in AI projects.
- Transformation leads responsible for evaluating AI portfolios.
