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Duration 7 hours
Course Outline
Discovery Prior to Development
- Why scoping errors are a primary cause of AI deployment failures.
- Viewing discovery as a core engineering practice.
- Establishing time limits and clear exit criteria for discovery phases.
Analyzing Workflows
- Observing tasks in their actual operating environment.
- Mapping process flows, handoffs, and exception handling paths.
- Conducting interviews with end-users, managers, and control function teams.
Evaluating Use Cases
- Applying filters for feasibility, value, and organizational readiness.
- Scoring and comparing a portfolio of ideas rather than focusing on a single concept.
- Providing evidence-based reasons for rejecting certain use cases.
Value Architecture and Business Case Construction
- Defining baselines, projected benefits, and cost-to-serve metrics.
- Selecting metrics that meet the standards of finance and audit teams.
- Maintaining a register of risks, compliance requirements, and dependencies.
Commitments, Milestones, and Handover
- Formulating scope statements, documenting assumptions, and defining exclusions.
- Setting up stage gates and defining conditions for project termination.
- Preparing a comprehensive handover brief for the development team.
Requirements
- Hands-on experience in customer-facing technical or delivery roles.
- Working knowledge of business processes within at least one specific industry.
- Proficiency in spreadsheets and document creation for modeling and reporting.
Target Audience
- Forward-deployed engineers and solution consultants.
- Pre-sales specialists and solution architects.
- Product owners and delivery leads involved in AI initiatives.
- Transformation leads evaluating an AI portfolio.
