Experiment design
Define what “good” looks like and how success will be measured.
- Success criteria and evaluation plan
- Data and access requirements
- Risk controls and guardrails
Approach
Step 2: Explore
Validate the best opportunities with fast, controlled experiments. This step turns assumptions into evidence and reduces delivery risk before committing to full-scale implementation.
What we do in this step
Small, time-boxed activities designed to test feasibility and value using real workflows, data, and constraints.
Prototype build
Build a minimal end-to-end prototype to test the core idea.
- Rapid prototypes with real constraints
- Integration stubs where needed
- Hands-on stakeholder demos
Validation and iteration
Collect feedback and iterate quickly based on evidence.
- Quality and performance assessment
- Operational considerations captured early
- Decision on proceed / refine / stop
Delivery blueprint
Translate learning into a practical plan for production delivery.
- Implementation approach and milestones
- Dependencies and delivery risks
- Resourcing and ownership