In recent decades, lab automation has moved from being a specialized tool to becoming a standard feature in many scientific environments. However, despite growing adoption, many research laboratories still find themselves abandoning automated systems and returning to manual processes. The problem is not a failure of automation itself, but rather a mismatch between the design of these systems and the realities of research workflows.
Many tools introduced into laboratories were originally built for high-throughput screening in manufacturing or clinical settings. These environments benefit from speed and repetition, whereas research settings require flexibility and adaptability. When automation systems are inflexible, they often create more obstacles than solutions. Scientists find themselves altering experiments to fit the system rather than using systems that support the way science is actually done.
In research and development (R&D) contexts, the ability to adapt is more valuable than maximum speed. Lab automation should support the creative and iterative nature of scientific discovery. When it becomes rigid or overly complex, it risks slowing down progress instead of accelerating it.
Why R&D labs need flexibility, not just speed
R&D labs move fast. Protocols shift quickly as new data emerges. Teams experiment with novel approaches and adapt based on results. These changes are signs of healthy, responsive science in action, but often move faster than their automated tools can handle.
Legacy automation systems have been designed to perform repetitive tasks at high speed. While this works well for production environments, research teams often need something different. Systems that can be adjusted quickly, configured to accommodate new types of labware, or adapted to work within the physical constraints of a biosafety cabinet tend to offer more value in these settings than tools designed for volume alone.
The most effective automation platforms in research environments are those that can change in step with evolving scientific needs. They make it easier for researchers to test new ideas, rather than locking them into fixed processes.
Designing automation for scientists, not engineers
Even when significant investment is spent on automation, many researchers fall back on manual methods because these tools are too rigid and inflexible. When a system requires advanced technical training or constant support from engineering staff, scientists are less likely to adopt it fully.
Scientists often long for tools that offer simplicity, ease of use, and accessibility. For example, liquid handling systems that allow users to upload and interpret CSV files directly, without specialized programming, can significantly reduce time and effort.
When researchers are able to run and adjust automation systems themselves, those systems become more than just tools—they become part of the lab’s day-to-day workflow. Usability becomes a key feature, not an afterthought.
Avrok BioSciences needed a solution to streamline nucleic acid normalization. Traditionally, this manual process required two technicians and 45 minutes per plate. To overcome bottlenecks, they decided to assess the performance of multiple liquid handlers. Unfortunately, many of the systems they tried required additional input from skilled programmers, adding to the financial burden. However, they were extremely pleased when they found a system that allowed for a CSV file to be loaded to streamline normalization. This is an example of how a system's features can support custom protocols, instead of forcing researchers to change their existing protocols to fit rigid prescriptions of an automated system.
Advanced Lab Management Certificate
The Advanced Lab Management certificate is more than training—it’s a professional advantage.
Gain critical skills and IACET-approved CEUs that make a measurable difference.
The results delighted both bench scientists, who were no longer required to oversee monotonous pipetting, and lab managers, who saw a time reduction per plate to 20–25 minutes. Furthermore, automation demonstrated a CV of under five percent for volumetric transfers and less than five percent for nucleic acid normalization, accompanied by a standard deviation of 0.11., comparable to the variability achieved with careful and professional manual transfers.
Where single-channel systems outperform
In environments where speed is the highest priority, the more common multi-channel pipetting systems offer clear advantages. However, these systems are often entirely inappropriate for an R&D context, especially when reagents are expensive or limited. In this case, R&D workflows often benefit more from precision and control than from the speed endowed by parallelization.
Specifically, scientific applications such as protein crystallography, nucleic acid quantification, and sensitive chemical reactions often require careful handling at the level of individual wells. In these contexts, far less common single-channel systems become the only type of automation that makes sense. Single-channel systems speed up processes while allowing for customized approaches to each sample, reducing the risk of contamination, and supporting unique conditions such as glovebox environments where space is limited and moisture sensitivity is critical.
Interested in lab design?
Is the form not loading? If you use an ad blocker or browser privacy features, try turning them off and refresh the page.
By completing this form, you agree to receive news updates and relevant promotional content from Lab Design News. You may unsubscribe at any time. View our Privacy Policy
It’s true that these systems are not the fastest, although they are still much faster than manual processes, but they are often more appropriate for researchers working with delicate processes or rare samples. In these scenarios, the goal is not to process as many samples as possible, but to ensure that each sample is handled with care and precision to avoid waste.
Such an approach proved extremely useful for the Late Stage Functional Group at Oxford University, which needed a system that could run experiments in an inert atmosphere. Other complicated steps in their protocol included dispensing volatile liquids and a wait time for layer separation. When they tried different tools, they often found that either the software was difficult to master or that the system had dripping issues. Finally, they found a system that ticked all the boxes; an enclosed system with nitrogen pressure control, user-friendly software, and drip-free single-channel dispensing.
A future built on adaptability
The research laboratories that get the most from automation are not necessarily the ones that invest in the most advanced or expensive systems. They are the ones that choose tools designed to fit their distinctive needs and evolve with them. For lab automation to be truly empowering, it needs to fit into existing lab environments, allow for rapid changes in workflow, and minimize the need for specialized reconfiguration or long setup times.
Adaptable automation systems are a boon to researchers, freeing up valuable time by reducing friction in the research process. They give scientists the freedom to adjust, explore, and respond quickly to new information. This flexibility is especially important in early-stage research, where time is limited, materials are often scarce, and the path forward is rarely obvious from the start.
Do we want a world where science becomes a factory? Or do we want a future where automation works hand in hand with human ingenuity to open more doors to faster progress? Automation should serve scientific thinking. It should enhance creativity, remove barriers to exploration, and help researchers do their best work. Choosing systems with adaptability at their core is one of the most reliable ways to ensure that technology supports, rather than restricts, the pace of discovery.
