Why this decision matters
AI work can be divided into requirement and data confirmation, risk validation, engineering implementation, target-environment retesting and handover.
Staged delivery exposes business, data, algorithm, interface and deployment risks progressively and uses evidence to decide whether to continue, adjust or stop.
Conditions to confirm before development
- Confirm objectives, scope, data and acceptance definitions
- Validate the highest-risk data, model, interface or hardware assumption
- Complete engineering implementation, integration and internal tests
- Retest on the target environment and hand over code, configuration and records
Implementation and delivery approach
Give each stage explicit inputs, outputs, reviewers and exit criteria. Expand implementation only after the preceding risk has a usable conclusion.
Keep prototypes, test data, review records, issue lists, change decisions and next-stage scope as stage evidence.
Acceptance boundary
Staging does not remove uncertainty or guarantee a fixed total schedule. Delayed client data, interfaces or environments require replanning.