Data analysts often tediously create visualization sequences to derive insights about what they see. While recent AI-driven approaches generate sequences to optimize visualization appeal and individual user preferences, extended cognitive fit theory suggests that expertise and insight type will affect the visualizations that analysts prefer. To investigate the role of expertise on insight generation from visualization sequences, we asked data scientists and accountants to report their insights as they investigated two business datasets. We found that both groups frequently followed the visualization sequences in order. However, expertise played a role in predicting the types of visualizations that each group chose to visit when they had finished the sequence but had time remaining. We also found significant interaction effects of visualization type, insight type, and expertise when assessing the numbers of insights generated per participant. Based on these results, we recommend that AI-driven data visualization tools should incorporate expertise as a feature for predicting new visualizations to produce.