Plenty of labs assume full automation is the finish line and manual pipetting is the thing to escape on the way there. It rarely works out that cleanly. The right level of automation depends on the work in front of you, and picking it well saves both money and aggravation. There are three broad options, and a handful of factors that sort between them.
The three levels
- Manual pipetting: a practiced hand and a pipette. Fast to set up for small jobs and needs no extra hardware, but it introduces technician-to-technician variability and, over time, repetitive strain.
- Semi-automation: a device that automates part of the work, such as setting volumes and tracking steps, while a person still moves a probe between vessels. A way to scale up and improve reproducibility incrementally.
- Full automation: a platform that runs hundreds of samples through a complex method with no human movements, for maximum throughput and consistency.
The factors that decide
A few questions usually settle which level fits.
- Sample count and throughput: a few samples an hour suit manual work; hundreds or more push toward full automation.
- Reproducibility: if run-to-run consistency is critical, automation removes the human variable.
- Dead volume: automated methods can tolerate more dead volume, while manual work minimizes it, which matters for precious reagents.
- Labor and repetitive strain: automation cuts hands-on time and the injury risk of repetitive pipetting.
- Safety: for hazardous or infectious samples, full automation keeps people away from the material.
Mixing levels is normal
Most labs do not pick one level for everything. They hand-pipette the small, one-off steps, semi-automate the medium jobs, and reserve full automation for the high-throughput, repetitive, or hazardous work. The goal is not maximum automation but the right tool for each task.
Automate what is repetitive, high-volume, or hazardous, and keep a pipette in hand for the rest.
Whatever mix you settle on, the calibration behind a reliable automated transfer is worth not losing. That is the case for keeping your liquid classes in a shared catalog: when a process that started out manual or semi-automated finally moves onto a full platform, the class comes with it and nobody has to tune it from scratch again.