depa AI Field Follow-up: How to Design a 90-Day Delivery Checklist

The public source states that depa continues on-site follow-up of AI Transformation projects across SME and agricultural operating scenarios. This article uses that fact as context for field operations for AI projects; the operating recommendations are analysis, not evidence that the source organization implemented, accepted or endorsed a system. During days 1–15, confirm pilot scope, operating objective, source boundary and owners, and list claims the available source cannot establish.

During days 16–30, create a data dictionary for use cases, business objects, data sources, model versions, owners, pilot scope and acceptance measures, sample current records for completeness and uniqueness, and define access, retention and change approval.

During days 31–45, map the end-to-end chain business use case → input data → model version → output → human review → business action and feedback; identify systems, manual work, interfaces, evidence and exception branches, then walk through a small set of real documents.

During days 46–60, configure the minimum viable process and reconciliation rules. Test normal, late, duplicate, reversal and interface-failure cases, assigning every issue an owner and retest result.

During days 61–75, run operations and finance in parallel within the controlled scope. Track completeness, traceability coverage, exception closure and reconciliation variance; do not expand until thresholds are met.

During days 76–90, complete acceptance records, operating ownership, training material and the next-stage decision. Before implementation, business, finance, IT and local owners should confirm definitions and review current contracts, authority requirements and real operating boundaries. The source supports only the stated public context; it does not establish an undisclosed budget, vendor choice or implementation result.