**The strongest relationships are all among AI usage, time saved, productivity and satisfaction. Cost, income and age are almost unrelated to everything else.** These are Pearson correlations over all 300 rows. Several numeric columns were stored as text with stray unit suffixes like "hrs", so I cleaned them before correlating (step-4). Earlier passes on slightly different row sets gave nearly the same figures. **Strong relationships (r ≈ 0.65 or higher)** - **AI usage hours per day and time saved per week:** r ≈ 0.78, the strongest pair in the dataset. - **Productivity score and time saved per week:** r ≈ 0.75. - **AI usage hours per day and productivity score:** r ≈ 0.66. - **Productivity score and satisfaction score:** r ≈ 0.65. **Moderate relationships (r ≈ 0.3–0.55)** - **Accuracy rating and satisfaction:** r ≈ 0.53. Accuracy is linked to satisfaction but not to productivity (about 0.07) or time saved (about 0.06). - **Satisfaction and time saved:** r ≈ 0.49. - **AI usage hours and satisfaction:** r ≈ 0.42. - **Age and work/study hours per day:** r ≈ 0.30, the only moderate link involving a demographic variable. **Essentially unrelated (|r| < 0.06)** - **Monthly AI cost** has near-zero correlation with productivity (0.013), satisfaction (0.009), time saved (0.055) and usage (0.036). Paying more is not associated with better outcomes here. - **Monthly income** is unrelated to cost (−0.05), productivity (0.02) and satisfaction (0.03). - **Age** has weak negative links with time saved (−0.12) and satisfaction (−0.12). **Takeaways** - More daily AI use goes with more time saved, higher productivity and higher satisfaction. These are correlations in observational survey data, so they don't show that usage causes the gains. - Spending more on AI tools is not associated with better results in this sample. - I only checked numeric pairs. Relationships between categorical columns (AI tool, purpose, education level, user type) were not tested here, though the gold tables could answer that.