Sepsis is the leading cause of death among patients in intensive care units (ICUs) requiring an early diagnosis to introduce efficient therapeutic intervention. Rapid identification (ID) of a causative pathogen is key to guide directed antimicrobial selection and was recently shown to reduce hospitalization length in ICUs. Direct processing of positive blood cultures by MALDI-TOF MS technology is one of the several currently available tools used to generate rapid microbial ID. However, all recently published protocols are still manual and time consuming, requiring dedicated technician availability and specific strategies for batch processing. We present here a new prototype instrument for automated preparation of Vitek®MS slides directly from positive blood culture broth based on an “all-in-one” extraction strip. This bench top instrument was evaluated on 111 and 22 organisms processed using artificially inoculated blood culture bottles in the BacT/ALERT® 3D (SA/SN blood culture bottles) or the BacT/ALERT VirtuoTM system (FA/FN Plus bottles), respectively. Overall, this new preparation station provided reliable and accurate Vitek MS species-level identification of 87% (Gram-negative bacteria = 85%, Gram-positive bacteria = 88%, and yeast = 100%) when used with BacT/ALERT® 3D and of 84% (Gram-negative bacteria = 86%, Gram-positive bacteria = 86%, and yeast = 75%) with Virtuo® instruments, respectively. The prototype was then evaluated in a clinical microbiology laboratory on 102 clinical blood culture bottles and compared to routine laboratory ID procedures. Overall, the correlation of ID on monomicrobial bottles was 83% (Gram-negative bacteria = 89%, Gram-positive bacteria = 79%, and yeast = 78%), demonstrating roughly equivalent performance between manual and automatized extraction methods. This prototype instrument exhibited a high level of performance regardless of bottle type or BacT/ALERT system. Furthermore, blood culture workflow could potentially be improved by converting direct ID of positive blood cultures from a batch-based to real-time and “on-demand” process.