This dataset captures a single snapshot of 105 polling responses on AI usage collected in July 2026, delivered as a standalone table with perfect structural hygiene—every field is populated, no missing values, and no orphaned references because no foreign keys exist to violate. The data is analytically self-contained: you can segment sentiment, adoption patterns, and demographic breakdowns within this cohort without needing external lookups or cross-table enrichment. The headline risk is architectural, not operational—this is a point-in-time survey with no mechanism to track respondents over time, compare against historical waves, or link to transaction systems that would reveal whether stated AI usage translates into measurable business outcomes. You cannot trend adoption, measure retention, or validate survey claims against behavioral data. **What this enables:** Immediate cross-tabulation of attitudes and behaviors within the July 2026 sample—identify which demographic segments express enthusiasm versus skepticism, which use cases dominate, and where adoption clusters. **What this cannot support:** Longitudinal analysis, causal inference about what drives adoption, or validation of self-reported usage against system logs. If strategic decisions hinge on understanding whether AI sentiment is improving, whether pilot users become power users, or whether survey responses predict actual deployment success, you will need to append time-series polling data or integrate with operational telemetry that this table does not reference.