### 2.1 Integration Assessment The dataset exists as a single self-contained table (`polling_on_ai_usage_jul_2026`) with 105 rows capturing polling responses on artificial intelligence usage. This flat structure is appropriate for survey data but limits the ability to contextualize responses against external factors such as demographic segments, geographic regions, industry verticals, or temporal trends. Enrichment opportunities exist by linking respondent attributes to reference datasets that would enable deeper segmentation and comparative analysis across market dimensions. ### 2.2 Recommended Actions - **Link to demographic reference data** to segment AI usage attitudes by age cohort, education level, or income bracket, enabling targeted messaging strategies for different audience profiles - **Integrate temporal benchmarking datasets** from previous polling cycles or industry surveys to identify sentiment shifts and track adoption trajectory over time - **Connect to industry classification systems** if respondent occupation or sector data exists, allowing analysis of AI perception variance across professional domains - **Append geographic reference tables** to enable regional comparison and identify markets with higher AI acceptance or resistance, informing go-to-market prioritization