How to choose the right automation system for your laboratory

Two scientists in a research laboratory

As scientists faced restrictions in being able to access laboratories during the Covid-19 pandemic, the scientific community turned towards automation systems to keep experiments running. Anita Ramanathan uncovers the benefits this technology provides.  

“Automation and digitalisation went from being a convenience to becoming a necessity,” notes Dr Rob Brown, VP Product Marketing at Dotmatics “Leaders in the industry were exploring ways to keep teams productive remotely and maintain throughput goals even during the lockdown.” 

With only a fraction of the working staff on-site, projects continued, aided by digitally connected automation systems, online software controls, and cloud-based data capture. This new way of working has triggered a mindset shift among researchers. Slowly, but surely, the benefits of laboratory automation are now becoming more apparent, turning many traditionalists and sceptics into committed end-users. 

Not surprisingly, for many laboratories, investing in an automation system has moved back up the priority list. However, having to choose from dozens of different options and tackle lofty price tags, it can be challenging to make an informed decision about purchasing your first piece of laboratory automation. In this article, we break down the most important factors to consider before making an investment. 

Why consider lab automation: Short and long-term benefits 

Whether it’s automating a single task or implementing robotics to operate independently, at its core, automation brings tangible benefits both immediately and in the long run. 


  • Reduction of errors:  

When scientists are required to carry out repetitive tasks, such as pipetting samples into 96- or 384-well plates, fatigue sets in, causing inadvertent mix-ups or unnecessary repeats. Moreover, having to sequentially load samples and continually monitor experiments on a daily basis imparts variability to the process. Errors or omissions can also arise when manually recording data into notebooks. 

With automation, human errors and protocol fluctuations are eliminated. Upon programming and calibrating the system, automated liquid handlers or robots perform reliably for hours on end. Modern systems even include intuitive built-in features to detect plate types or read sample barcodes. The data produced then gets automatically stored in a shared drive, keeping data formats consistent across different users.  

  • Reproducible results: 

Reproducibility has become a critical issue within the scientific community. To make data more reproducible across laboratories – or even within the same research team – it’s integral to minimise variability and maintain detailed records of the optimised method. Thankfully, automation facilitates both. Not only are the methods more consistent from one experiment to another, every piece of data, both experimental and operational, along with the corresponding metadata, is recorded and stored. 

  • Improved laboratory efficiency: 

Requiring minimum to no supervision, an automated system continues to perform the specific task or workflow in the background, allowing scientists to walk away from their experiments and work on other projects. Additionally, automated imagers or analytical workflows can be programmed to run experiments overnight and send online reports in the morning. This ability to parallel process experiments during the day and use the ‘dead space’ at night significantly accelerates research projects. Without automation, it would be nearly impossible to use walk-away or out-of-office hours to make progress. 

The quality of data, too, is improved, increasing confidence in the observed results. For example, in real-time cell imaging studies, rather than manually position the cells under the imager each time, using an automated sequence to capture images at regular intervals – even at night – yields consistent, comparable data. Moreover, the additional data points collected over time can also reveal new insights that may go unnoticed if experiments were limited to working hours.  

  • Scientists can focus on higher-value tasks: 

Highly trained scientists with advanced degrees shouldn’t be spending valuable time in the laboratory engaging in drudgery. By automating mundane or laborious tasks, scientists can have more time to review literature, develop new hypotheses, design better experiments, and analyse data.  

While machines do the grunt work, we can take on higher-value activities that actually move the needle forward in research. “Automation lets us focus on the science,” says Rich Ellson, Chief Technology Officer at Beckman Coulter Life Sciences. “Honestly, scientists don’t need to know how to methodically move five nanolitres of a reagent into tiny microwells – let automation take care of that!”   


  • Data traceability: 

In science, previous research topics can sometimes resurface back to relevance, requiring current team members to pore over old lab notebooks or archived folders. Locating original data points from these manually kept records isn’t always reliable. On the other hand, data generated from automated systems can be readily traced back in time, making it easier to retrieve older protocols or re-analyse previous data for a new research question. 

  • Leveraging AI: 

The ultimate goal of automation is to, someday, generate enough data to be able to employ AI for decision-making. For this, however, the data being collected needs to be of very high quality as feeding AI with subpar datasets will only result in less-than-ideal outcomes. In many ways, automation helps keep data ‘clean’ by maintaining consistency and upholding data integrity. Most modern systems are now designed to follow the FAIR (findability, accessibility, interoperability, and reuse) data principles.  

  • Cost savings:  

It might not seem obvious at first, especially given the high price point, but automation can be cost-effective in the long term. In many instances, when workflows get automated, experiments are miniaturised to improve the speed of analysis. What was once a millilitre-level reaction in a tube now runs in nanolitres on a microwell plate, while keeping the data quality intact. This miniaturisation alone results in cost savings as the volume of reagents and samples used per experiment dramatically reduces. Another indirect cost benefit is realised when highly skilled scientists dedicate their working time towards meaningful tasks as a result of adding automation systems to workflows. 

  • Potential for commercialisation:  

Research groups have a higher chance of making their drug product commercially viable when they automate steps early on. Even if simple, benchtop systems are used, the fact that efforts were made to improve accuracy and throughput at the research phase makes it much easier to introduce advanced automation systems during development and manufacturing.  

Saving a few minutes earlier in the product’s lifecycle on a smaller scale ultimately amounts to hours saved later on when processes get scaled up. “Taking the leap from bench to bedside will require automation,” says Dr Ian Holland, Research Fellow, University of Edinburgh. “So, demonstrating that automation was considered during early stages of the drug discovery process makes the product much more commercially attractive.” 

Take the first step towards automation with these tips from industry experts 

Given the vast catalogue of automation systems offered by vendors, ranging from liquid handlers to auto-imagers, it can often seem overwhelming to make the best choice. Your laboratory’s unique application, future goals, and available budget will ultimately drive your purchase decision. 

If this is the first time an automation system is being introduced into the laboratory, industry experts advise starting small, with easy-to-use benchtop systems. Once the team has adjusted to this change and benefits are discernible, more sophisticated automation solutions can be gradually added. 

Here are a few preparatory steps to take when onboarding an automation system: 

Dr Ian Holland, Research Fellow, University of Edinburgh

“List out every single step, one by one. Then ask yourself, ‘Where can I save time or do better?’ This could be during the sample prep stage, the experimental method itself, or maybe during data processing. 

Now, based on your budget, look for relevant options. Request a demo to test the instrument. Make sure you take into account the costs for consumables or software licenses. Some systems may require vendor-specific consumables or monthly software subscriptions that can hike up the total price. Remember that it’s also possible to request bespoke systems for your unique needs. 

If funds are tight, don’t hesitate to approach the engineering department in your university. It’s highly likely that engineering teams have ongoing automation projects and need real-life applications to test it on. Take your ideas to the engineers on campus. You might be pleasantly surprised with the solutions they come up with.” 

Ian Yates, Director of Enterprise Science and Innovation Partnerships, Thermo Fisher Scientific 

“Take a step back and think about the big picture for your laboratory. What drives efficiency – or stops your team from achieving it? Map out existing workflows, from start to finish, and identify areas where efficiency can be improved in terms of time, resources, costs, personnel, or data quality.  

Once you’ve defined the problem i.e., identifying what step would benefit most from automation, define what success looks like. This could be achieving higher throughput for contract laboratories or eliminating error-prone tedious tasks in an academic laboratory.  

When you get clear on your automation goals – no matter how big or small they are – vendors can then suggest systems that match these goals. To achieve tangible results with automation, define your problem first, then find a solution that resolves it.” 

Rich Ellson, Chief Technology Officer, Beckman Coulter Life Sciences

“Talk to colleagues that have successfully incorporated automation into their workflows. They’ve likely gone through a similar journey and are better equipped to answer your questions about long-term benefits or budget constraints.  

Many industry leaders also share their experiences at conferences. Set up meetings with them so you get their first-hand insights on a particular technology. 

When it’s time to pick a system, remember that it’s not always required to start with high-end options. Most companies offer smaller entry-level systems as well. Find the right starting point for your laboratory by speaking with field application scientists at vendor companies. They can help you find the right system for your needs, and even connect you with other users so you can have peer-to-peer conversations. 

If you’re trying to acquire funding for automation, ask technology providers if their applications department can help you demonstrate the benefit of a chosen system. The data generated from these pilot projects can then be used in grant proposals to help you make a compelling case.” 

Dr Rob Brown, VP Product Marketing, Dotmatics 

“When implementing a laboratory automation project, data automation and data flow are also key considerations. 

Being able to automatically gather data from instruments and link it back to corresponding samples that were used to generate it in the first place can be a tremendous benefit, both in terms of productivity gains and increased data compliance.  

When analytical data can be automatically parsed and processed like so, team members don’t have to manually transfer data from instruments to databases. Parsing essentially means that instrument data can be stored in an instrument-agnostic form, which greatly enhances data longevity for the research group. 

Tightly integrating data acquisition from instruments, data storage and data analysis systems would ultimately help all laboratory members to access data faster. Automating data flow also ensures that the laboratory’s data and the context of this data are readily available to support data-driven decision-making. 

Having access to high-quality experimental data can facilitate the reuse of existing knowledge to employ AI and machine learning in the future.” 

Pick the right automation system for your lab: Key considerations  

Automation goals are driven by the research output and unique needs of a laboratory. From basic to highly advanced, different automation systems are designed to serve different purposes. 

What level of automation is desired?  

In general, automation efforts can be split into three tiers:  

  • Automating a task: 

This is the basic level of automation where a time-consuming task is simply replaced by an instrument. For example, instead of a user manually loading new samples every 45 minutes into an incubator, an automated sample mover is loaded onto the front end and programmed to perform the sample loading and unloading. This type of automation eliminates mundane, repetitive steps and allows scientists to confidently walk away from the experiment to carry out other tasks.  

  • Automating a workflow: 

At this level, two or three devices are connected to automate a process. For example, an ELISA immunoassay workflow can be automated by connecting a liquid handler, an incubator/plate shaker, and a microplate reader. In addition to the extended ‘walkway’ capabilities, automating workflows can also improve overall productivity due to minimal human intervention. 

  • Integrating and automating multiple workflows: 

An advanced stage of automation, this level is most applicable to higher-throughput laboratories that need to run multiple experiments simultaneously. For example, a configured automation platform can contain a liquid handler connected to the sample prep system and the HPLC system. The entire analytical workflow, from sample prep to analyte detection, is triggered and monitored through a dedicated workflow scheduling software. 

For laboratories in the research phase of the R&D pipeline, automation goals are directed towards boosting accuracy and generating high-quality data. During development and manufacturing phases, however, laboratories need to prioritise increasing the speed of operation, achieving higher throughput, and maintaining compliance. At this stage, advanced levels of automation might be most suitable. 

“The journey of a laboratory towards achieving digital transformation has three key milestones”, says Ian Yates, Director of Enterprise Science and Innovation Partnerships at Thermo Fisher Scientific. “First, the connected laboratory, next, the automated laboratory, and, finally, the intelligent laboratory. It is only after connecting all data pipelines and implementing automation workflows that a laboratory can be ready to make use of data intelligence and AI.” 

What types of tasks need automation?  

Another way to approach automation decision-making is to note down every single step of the workflow and then pinpoint the most time-consuming or tedious ones. Based on the different steps in a workflow, automation systems can be added to pre-analytical, analytical, or post-analytical stages. 

Pre-analytical automation would typically involve sample prep systems, sample handlers, or sample loaders. Automating the analytical section of the workflow would require application-specific systems, such as microplate readers or cell imagers. Finally, post-analytical solutions help simplify data processing and accelerate analyses. 

Are there recurring costs associated with consumables and licensing? 

Monthly or annual software licensing fees and vendor-specific consumables can increase the overall maintenance cost of a system. These can also interrupt existing workflows if files can’t be accessed, or consumables can’t be used with other instruments. Requesting a demo is the safest way to test for compatibility and also gauge recurring expenses before making a commitment.  

 How to overcome common barriers 

The most frequently asked questions before purchasing an automation system include: 

“How much will it cost?” 

“How big is it?”  

“Will we really use it?”  

These are all valid concerns that most laboratories tend to have… and rightly so. 

Non-commercial researchers can find it challenging to put a monetary value on seemingly intangible, longer-term benefits, such as data centrality. Plus, there’s no guarantee that team members will actually use the new equipment. In some other instances, the constraint is much more tangible, like working with limited laboratory space. 

Below, we list commonly experienced barriers to automation, and provide actionable ways to address them: 

  • Costs and funding 

The most obvious barrier to purchasing automation systems is the significant upfront investment required. While laboratories can occasionally seek funding for equipment, scientific leaders in academia tend to allocate their funds towards training students, postdocs, and technicians. Moreover, funding bodies award grants based on the scientific question being asked and expected research output – not necessarily the intent to purchase an instrument. 

How to overcome this: Consider an entry-level automation system that is specifically designed for first-time users. Much like the evolution of computers, the price point of automation systems, too, is gradually coming down as technologies continue to mature and become more mainstream.  

Approach different vendors to request quotes for both off-the-shelf and bespoke systems. To justify the investment on a grant proposal, reach out to field application scientists at vendor companies and partner with them to generate pilot data.  

Alternatively, if automation is only required on an ad-hoc basis, establish collaborations with peer laboratories that have already successfully implemented automation. Additionally, universities also offer core laboratory services that are equipped with start-of-the-art instruments. These technologies can now be accessed for a small usage fee without having to purchase the whole system. Based on the laboratory’s research goals, outsourcing selected projects to independent service providers can also be an economical option.  

“When researchers see an urgent need, they tend to get creative”, notes Ellson. “We recently worked with a virology research group that needed to accelerate their research during the coronavirus pandemic. Instead of waiting for the grant cycle to acquire an automation system, they set up a Kickstarter campaign. Within a week, they had enough funds to place the order.” 

Dr Holland agrees: “In my experience, when academics start thinking about a problem, they automatically find different avenues to raise the necessary funds to solve it.”  

  • Laboratory space: 

Most academic laboratories and smaller biotechs face space constraints. Sizeable systems, such as liquid handling robots, taking up valuable working space can be a genuine concern for team members. Plus, each instrument requiring its own computer can further add to the tech clutter in an already packed laboratory. 

How to overcome this: Ask for benchtop versions of automation systems that have a smaller laboratory footprint. Where possible, choose multi-functional or multi-application systems that can replace two or more single-application instruments. To reduce tech clutter, check if the automation system can be connected to user PCs or laptops via cloud-based methods. 

  • Building infrastructure: 

Occasionally, some older research buildings may be considered infrastructurally incompatible with modern instruments because they aren’t connected to a central network. While this is a rare occurrence, it must be noted that the lack of network connectivity can make it rather impractical to install automation systems. 

How to overcome this: It is possible to custom order an external hardware system that provides network connectivity for your laboratory. This can also be useful in the future when the laboratory would like to connect different instruments within the same facility to establish seamless end-to-end automation. 

“Most organisations are now taking advantage of cloud-based capabilities that can be implemented wherever there is web connectivity”, says Dr Brown. “Integrating laboratory automation systems is a necessary step towards ‘lab of the future’ initiatives.”  

  • User unwillingness: 

Embracing change is fundamentally hard for all of us – and more so when we’re being asked to change the way we’ve always done something. When a new automation system enters the laboratory, team members trained in traditional, hands-on methods are quietly thinking ‘what’s in it for me?’  

As with any new process, learning how to operate a newly onboarded automation system comes with a steep learning curve. During this period, it can seem like automation is more of a time sink than a time-saver, making users feel like it’s much easier to just do it manually.  

There is also an unspoken fear that an instrument can potentially ‘replace’ a scientist’s job. When unaddressed, these concerns cause team members to resist using the new equipment and revert to familiar, manual ways.  

How to overcome this: During the planning stage, in addition to highlighting the key benefits of automation for the team or the company as a whole, also communicate with end-users and discuss how this upgrade can have a positive impact on their contributions. 

Engage the whole team in early discussions to make sure their ideas and concerns are heard. Involve end-users in demo experiments and, upon purchase, during the installation process. This way, the team gradually eases into the change instead of resisting it. 

“We also recommend identifying and appointing ‘super users’”, says Yates. “If a team member is enthusiastic to learn about the new automation system, assign them as the ‘system owner’. This motivates them to take on a leadership role within the laboratory and become the go-to person for that system.” 

The hidden risks of late adoption 

Compared with the tech industry, the scientific community is yet to fully embrace the potential of automation. At the moment, those leveraging laboratory automation gain a competitive advantage in the industry. In the near future, however, resistance to adopting automation can quickly turn into a disadvantage. “Science waits for no one”, reminds Ellson. “If there’s a better and faster way to do something, we owe it to ourselves to at least try it. The same scientific rigour can be applied towards exploring automation as well.” 

Highly capable laboratories risk being left behind if they’re unable to keep up with throughput demands or fail to improve their research output. In an academic setting, where throughput might not be a priority, the inability to reproduce or retrieve data can lead to redundant experiments, increasing research costs, and stretching timelines. 

Besides efficiency, the biggest benefit of replacing drawn-out, tedious, and repetitive tasks with a fit-for-purpose automation system is freeing up a scientist’s time. Rather than being burdened with time-consuming and laborious methods, team members can focus on higher-value activities. 

Simply put, investing in automation not only future-proofs laboratories from the fast-approaching digital wave but also inspires improved job satisfaction among the staff, providing them with the time and opportunity to do what they enjoy most – engage in scientific thinking. 

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About the author 

Anita Ramanathan is a science writer and award-winning speaker based in Bristol, UK. In her capacity as a science writer/editor at several digital publications, including NIH Research Matters, she has crafted dozens of stories buried under numbers and scientific findings. A storyteller at heart, Ramanathan also delivers science communication workshops. 

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