**Usage, time saved, productivity and satisfaction form one tightly linked cluster. Cost, income, age and work hours are essentially unrelated to everything else.** These are Pearson correlations between the numeric columns. Several columns (`AI_Usage_Hours_Per_Day`, `Time_Saved_Hours_Per_Week`, `Age`, `Monthly_Income` and others) are stored as text and contain unit strings like "2 hrs". I cleaned these before computing, as the step-0 sample shows. The coefficients are in the step-1 result. **Strongest relationships** - `AI_Usage_Hours_Per_Day` and `Time_Saved_Hours_Per_Week` have r = 0.785, the strongest pair, across 307 pairs. More daily AI use goes with more weekly time saved. - `Productivity_Score` and `Time_Saved_Hours_Per_Week` have r = 0.75. - `AI_Usage_Hours_Per_Day` and `Productivity_Score` have r = 0.661. - `Productivity_Score` and `Satisfaction_Score` have r = 0.642. - `Accuracy_Rating` and `Satisfaction_Score` have r = 0.546. - `Satisfaction_Score` and `Time_Saved_Hours_Per_Week` have r = 0.478. - `AI_Usage_Hours_Per_Day` and `Satisfaction_Score` have r = 0.413. **Weak or no relationship** - `Age` and `Work_or_Study_Hours_Per_Day` have a small positive correlation (r = 0.24). Age is slightly negative with time saved (-0.128) and satisfaction (-0.116). - `Monthly_AI_Cost` is essentially uncorrelated with everything: productivity 0.001, satisfaction -0.017, time saved 0.051, usage hours 0.039. Spending more on AI is not associated with better outcomes here. - `Monthly_Income` is also near zero against every other column (all within ±0.05). - `Accuracy_Rating` is almost unrelated to productivity (0.086) and time saved (0.061). It connects only to satisfaction. **Caveats** - These are observational correlations, so they don't show that more usage causes more time saved. - Some `n_pairs` values (307, 310) exceed the 300 rows the card reports for the table. I haven't explained that discrepancy, so treat the pair counts with some caution. - I only measured numeric-to-numeric relationships. Categorical columns such as `AI_Tool`, `AI_Purpose`, `User_Type` and `Education_Level` were not tested against the outcomes.