While various AI tools still have room to grow, as of now, they provide computational tools that contain research domain knowledge. By exploring the use of various tools, this study analyzes how AI tools influence data interpretation, experimental design, workflows, research and/or analytical work, and productivity. An illustrative dataset was created by the researcher using 400 biologically credible researcher profiles and was not compiled from surveyed or institutional datasets. Descriptive statistics, correlation, Cronbach's alpha, t-tests, ANOVA, regression, multiple and hierarchical regression, and mediation analysis via bootstrapping were performed on the multiple-item constructs. AI positively influenced data interpretation and the design of experiments and productivity. Integrated records led to better outcomes in the main metrics, and using responsible AI did not yield a significant gender difference. Regression models indicated that the variables associated with the research process and the workflow efficiency accounted for a significant portion of the outcome, and all variables did not need to be included. The workflow efficiency variable fully mediated the relationship of use and productivity. Based on the results, AI can assist biological research in the current state, as long as it is used to further the thinking and rationalizes the structure of the scientific framework, and is verifiable, reproducible, and communicated to the researcher.