Apart from the software, some well- known options can be of high value, such as a sample preview- or overview-camera, a hardware-based autofocus and automatically adaptive optics. Integrated barcode- reading can be a real game-changer, when barcoding has already been introduced in the research lab environment.
What is true for software, also applies to hardware: interfaces to interact with newest hardware and accessories, such as trigger out- and input and well-documented optical connections, are crucial and will make any automation effort more seamless and successful.
A maximum of automation in every aspect might not always be desirable. In some ca- ses, it will be tempting for the researcher to not think about the experiment and leave it all to the machine. However, machines are developed and programmed by humans who can also make mistakes. A wrong entry in a dye database can for example lead to an imaging configuration that produces crosstalk that is mistaken as a valid signal by the researcher. A system might be properly configured for one kind of experiment to discard and even delete useless images. However, for another kind of experiment or another user, this could result in loss of precious data, if not adapted in the right way. The same is true for image analysis. Automated exclusion of certain objects from the analysis will be essential for one type of assay, while it will remove all of the information from other assays.
Despite all trust in technology and automation, reviewing configuration and setups and carrying out quick plausibility checks from time to time can therefore be very valuable.
Certainly automation does not just begin at the imaging system and does also not stop after images have been acquired. The challenge to remove the need for human intervention in imaging are very similar to the challenges in laboratory automation in general. Some 20 years ago, millions of samples per year and per lab were required, that investment of a laboratory automation environment could be justified. With increasing standards in documentation and reproducibility, even more interdisciplinary research projects and continued short-term researcher contracts, automation also enters the realm of smaller labs.
Recent technologies like Smart Connected Products and the Internet of Things have the potential to change the game completely. Microscopy must then be ready to play this game new.
 Baker M.: Nature, 452, vol 533, (2016)
* H. Wolff: Carl Zeiss Microscopy GmbH, 07745 Jena