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Duration 7 hours
Course Outline
Discovery Prior to Development
- Why the majority of AI projects fail at the scoping stage
- Treating discovery as a core engineering discipline
- Establishing time limits and exit criteria for discovery
Interpreting Workflows
- Observing work in its natural environment
- Documenting process maps, handoffs, and exception handling
- Interviewing end-users, managers, and oversight functions
Validating Use Cases
- Applying filters for feasibility, value, and organisational 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 the Business Case
- Defining baselines, benefits, and cost to serve
- Selecting metrics acceptable to finance and audit teams
- Maintaining a register for risks, compliance, and dependencies
Commitments, Gates, and Transition
- Defining scope statements, assumptions, and exclusions
- Implementing stage gates and clear kill criteria
- Preparing a handover brief for the development team
Requirements
- Hands-on experience in a client-facing technical or delivery capacity
- Knowledge of business processes within at least one industry sector
- Proficiency in basic spreadsheet and document management for modelling and reporting
Target Audience
- Forward deployed engineers and solution consultants
- Presales and solution architects
- Product owners and delivery leads involved in AI projects
- Transformation leads evaluating AI portfolios
