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Duration 7 hours
Course Outline
Discovery Prior to Development
- Reasons why scoping errors lead to AI deployment failures
- Treating discovery as a core engineering function
- Establishing time limits and exit criteria for discovery phases
Workflow Analysis
- Observing operations in their actual context
- Documenting process maps, handoff points and exception handling
- Conducting interviews with end-users, managers and compliance teams
Use Case Qualification
- Applying feasibility, value and readiness criteria
- Scoring portfolios holistically rather than focusing on isolated ideas
- Using evidence to reject unsuitable use cases
Value Architecture and Business Case
- Defining baselines, expected benefits and cost-to-serve metrics
- Selecting indicators accepted by finance and audit departments
- Managing risk, compliance and dependency registers
Commitments, Milestones and Transition
- Defining scope statements, key assumptions and exclusions
- Implementing stage gates and termination conditions
- Preparing handover documentation for the development team
Requirements
- Professional experience in customer-facing technical or delivery roles
- Understanding of business processes within at least one industry sector
- Competence in spreadsheet and document tools 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
