Map an AI-assisted analytics task to CRISP-DM stages so the work does not stop at a clever model output.
The move: treat AI analysis as a lifecycle, not a clever output. Business Understanding Name the decision, owner, and success measure. AI analysis should serve a business action. Data Understanding And Preparation Ask what the data can and cannot support. Use AI to profile fields and spot anomalies, but verify definitions and leakage yourself. Modeling, Evaluation, Deployment Modeling is the middle, not the end. Evaluate against business usefulness, then define exactly how the client will use the result in a workflow.
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