Why this decision matters
AI project acceptance should cover business workflows, test samples, outcome metrics, system performance, exception handling and handover materials.
Criteria should be agreed before development and repeatable in the target environment; a single demo, one accuracy number or a subjective review is not sufficient evidence of delivery.
Conditions to confirm before development
- Define workflows, user roles and observable success conditions
- Freeze test-data versions, sample scope and metric calculations
- Agree latency, concurrency, resource, permission and exception requirements
- List source-code scope, configuration, deployment records and third-party dependencies
Implementation and delivery approach
Translate the requirement into observable business results, then define separate tests for software, algorithms and deployment. Validate high-risk assumptions before full implementation.
Retain input samples, system versions, configuration, outputs, issue records and retest results so differences can be traced to data, models, software or environment.
Acceptance boundary
Targets must match the supplied data and target environment. Data drift, third-party changes and defects outside the agreed system scope require separate treatment.