In PMI-CPMAI–aligned practice, a go/no-go assessment is a formal checkpoint where technology, data, governance, risk, and stakeholder factors are evaluated against predefined criteria. If this assessment uncovers that multiple technology and data factors are insufficient, the appropriate response is not to proceed, but to pause and address those deficiencies. The project manager’s role is to coordinate further analysis of data readiness (availability, quality, completeness, relevance) and verify that stakeholder expectations and commitments are still aligned with the AI initiative’s constraints and risks.

Option A—verify data quality and stakeholder alignment—captures this corrective step. It reflects the PMI principle that AI projects must be based on trustworthy data and shared understanding; otherwise, model outcomes may be unreliable, non-compliant, or misaligned with business value. Options B, C, and D effectively ignore or downplay the red flags discovered in the assessment, which violates disciplined, risk-aware AI governance. Proceeding despite known gaps, focusing only on technology while neglecting data, or launching without further assessment directly contradicts structured go/no-go decision logic and could expose the organization to operational, ethical, or regulatory failure.

Therefore, the appropriate action after an unfavorable go/no-go outcome is to re-verify and remediate data quality issues and ensure stakeholder alignment (option A).