I need to explore the dataset systematically to identify surprising or counterintuitive patterns. Let me start by examining the AI usage patterns across different demographic and occupational groups. ```json { "action": "tool_call", "reasoning": "To find surprising patterns, I should look at AI usage rates across occupations. I'd expect tech workers to have the highest usage, but counterintuitive findings might show unexpected leaders or laggards.", "tool": "query_sql", "args": { "sql": "SELECT Question, Response, \"Occupation: Software & IT\", \"Occupation: Managers\", \"Occupation: Business, finance & legal\", \"Occupation: Education & social services\", \"Occupation: Engineering & science\", \"Occupation: Healthcare\", \"Occupation: Sales, office/admin & creative\", \"Occupation: Manual & in-person services\" FROM raw.polling_on_ai_usage_jul_2026 WHERE Question = 'Any AI Use' AND Response = 'Yes'", "purpose": "Compare AI adoption rates across occupations to find surprising patterns", "source": "raw" } } ```