Policy compliance in enterprise conversational AI is not binary: the same response can satisfy one stakeholder’s policy while violating another’s. Existing evaluations largely treat compliance as a single-policy problem, which makes cross-role conflict cases difficult to construct and assess. GRACE (Generating Conflict-Aware Cases for Evaluating Policy Compliance) addresses this gap by generating matched normal and conflict cases from the same underlying scenario, assigning role-wise expected verdicts through independent per-role policy evaluation, and scoring responses independently for each role rather than collapsing compliance into a single binary label. Across four domains, this process yields 1,109 labeled scenarios, including 309 cross-role conflicts. Evaluating five LLMs shows that deny cases are easiest, allow cases are depressed by over-refusal, and conflict cases are consistently hardest, revealing failures that single-label evaluation obscures. Per-persona analysis further shows that these errors are localized to specific domain-role combinations rather than uniform across models. GRACE therefore supports both policy review and deployment-time model selection.