depa AI Field Follow-up: What Data Should the Company Prepare First?

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.

Start with master data covering use cases, business objects, data sources, model versions, owners, pilot scope and acceptance measures. Every field needs a unique code, effective date, owner and retirement rule so the same object does not acquire conflicting versions across departments.

Then capture business events across data extraction, model run, human review, exception feedback, parameter adjustment, result use and retrospective review. Each event should retain time, quantity, unit, location, status and upstream document number so analysis can return to the underlying action.

Assign data accountability: operations defines meaning, finance confirms measurement and accounting treatment, and IT manages interfaces and access. Apply required-field, format, duplicate and cross-table consistency checks as continuing controls.

Before a pilot, sample one complete operating cycle and measure missing, duplicate, late-entry and unlinked-record rates. If records cannot be tied to a document or accountable owner, repair capture before building dashboards or AI use cases.

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.