Why this decision matters
An AI feasibility assessment should begin with the business objective, available data, system environment, acceptance method and priority risks rather than a model name.
The assessment determines whether the problem needs AI, whether current conditions support a test and whether technical and business risks can be controlled in stages.
Conditions to confirm before development
- Observable business goal and current operating method
- Available data, documents, images, interfaces or device inputs
- Repeatable samples, metrics, workflows or human review
- Error, latency, cost, security and third-party boundaries
Implementation and delivery approach
Translate the business request into testable questions and choose full development, a focused MVP or further preparation according to the unresolved risks.
Retain the assessed inputs, assumptions, highest-risk items, proposed validation and recommended next action.
Acceptance boundary
This guidance applies to the agreed data, system and environment. Project-specific scope, dependencies and acceptance conditions must be confirmed separately.