depa AI Field Follow-up: Where Are the Main Implementation Risks?

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.

Key risks include treating field follow-up as project success, mixing training and live data, unclear model versions, missing human review and disconnected business-technical measures. At initiation, translate each risk into a testable control, named owner, trigger condition and escalation path.

Scope risk appears when a concept is expanded to every entity, site and process at once. Select one closed-loop use case, document inclusions and exclusions, and record the conditions for later expansion.

Data risk usually precedes technical risk. Inconsistent codes, missing historical states or different time definitions can produce wrong conclusions even when interfaces run correctly, so reconcile samples and test negative scenarios before launch.

Organizational risk comes from overlapping responsibility. Operations owns process decisions, finance validates amount and period, and IT manages access and interfaces; a vendor cannot replace the company's accountability for definitions and acceptance.

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.