Identify the lifecycle stage where an AI data quality issue should be prevented or controlled.
Place each AI data issue in the lifecycle stage where the strongest fix belongs. Plan Collect or acquire Prepare and maintain Use and monitor Dataset was collected without naming the AI decision it may support No standard exists for customer tier across product and finance Sales reps type free-form industry names into the CRM Signup form allows role and team size to be skipped Account records duplicate when regional subsidiaries are joined A source field changes name without breaking the pipeline A model approved for coaching is reused for performance scoring A recommendation model keeps running after user behavior shifts
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