Course Objective & Scope
In many organisations PoCs end with an impressive demo but fail to reach production because real-world data messiness, edge cases and user habits were never tested. This course distinguishes a technical feasibility PoC from an operational value pilot and teaches teams how to design both around falsifiable hypotheses.
Topics include ruthless scope bounding, curating a representative golden dataset, shadow-mode versus controlled live rollout patterns, user feedback loops and safety guardrails. In a workshop tailored to the organisation, participants author a complete pilot charter for one of their own priority use cases.
Who Should Attend?
- Product managers and project leads running AI experiments in the field
- Business unit managers and operations leads sponsoring use cases
- Business analysts, QA specialists and change management practitioners
- Data and AI team leads co-designing PoCs with business stakeholders
Key Takeaways
- Distinguish the purpose and scope of a PoC, prototype, MVP and production pilot
- Bound testable business and technical hypotheses to a minimum viable scope
- Curate a golden evaluation dataset covering typical and edge-case scenarios
- Plan the pilot user cohort, onboarding, feedback channel and human-in-the-loop workflow
- Write a pilot charter complete with exit criteria and scaling prerequisites
Curriculum
PoC, Prototype, MVP and Pilot: Choosing the Right Vehicle
- Technical feasibility (PoC) versus operational value validation (Pilot)
- Common traps: endless PoC loops and cherry-picked demo data
- Framing falsifiable hypotheses with a hypothesis card
Scope Bounding and Golden Dataset Design
- Drawing explicit in-scope and out-of-scope boundaries
- Building a test set across routine cases, hard edge cases and adversarial inputs
- Creating an evaluation rubric and ground truth with domain experts
Pilot Operating Mode and Human Oversight
- Shadow mode, copilot mode and bounded autonomy rollout patterns
- Fallback procedures and escalation paths when the model fails
- Data privacy, access control and audit logging during the pilot
User Cohort Selection, Onboarding and Feedback Loops
- Selecting a representative user group balancing champions and sceptics
- Managing expectations, quick-start guides and in-workflow feedback capture
- Weekly pilot triage and iterative prompt or workflow tuning
The Pilot Charter and Exit Criteria
- Timeboxing, resource allocation and RACI matrix
- Assembling the evidence pack for the Go / Pivot / Stop gate review
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