During the initial phase of an AI project, the team is assessing project success criteria.

In the PMI-CPMAI view of AI project governance, one of the earliest and most critical responsibilities in the lifecycle is the identification of all applicable legal, regulatory, and policy requirements, especially those related to data usage, storage, transfer, and retention. When a project reaches the stage of defining success criteria and only then discovers that it may be violating compliance rules, this is characterized as a failure to identify data and AI-related regulations early in the project.

PMI-CPMAI stresses that regulatory scoping must be done in the initiation and planning phases, before detailed design and implementation, because regulations fundamentally constrain what data can be used, how it can be processed, and which AI techniques are permissible. Missing this step leads to rework, redesign, and in some cases project stoppage. It is not primarily a problem of unclear business objectives, nor of separating cognitive vs noncognitive components, nor simply a missing go/no-go gate. Instead, the core issue is that the team did not perform a sufficiently thorough regulatory and compliance assessment at the outset, so non-compliant practices surfaced only later. Hence, the problem is best described as failure to identify applicable data regulations early on.

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