In a highly competitive research landscape with uncertain funding, automation has become a necessity rather than an option. It enables laboratories to navigate funding uncertainties, attract and retain talent in appealing learning and working environments, and accelerate cycles for quicker publication or discoveries. Despite these advantages, many researchers remain reluctant to adopt automation tools for repetitive and time-consuming tasks, such as liquid handling or robotic arm integration. This hesitation stems from concerns about the high costs associated with adding or scaling automation, as well as the belief that deploying automation requires specialized knowledge and skills, potentially delaying current projects and increasing risks to deliverables.

In this article, we will address common myths surrounding automation adoption and demonstrate how they can be addressed through a practical, “build-on-what-you-have” approach rather than an “all or nothing” implementation.

Myth 1: Automation is unaffordable, unattainable, or has little return on investment.

A recent survey of biopharma research and development executives revealed 53 percent of respondents reported increased laboratory throughput, 45 percent saw a reduction in human error, 30 percent achieved greater cost efficiencies, and 27 percent noted faster therapy discovery as a direct result of lab modernization efforts.¹ While there is a cost for new equipment, the value that it brings can more than make up for this additional expense—any amount of automation added to existing workflows can provide immediate value to labs and research institutions.

Taking an inventory of what you may already have or have access to is an easy way to begin implementing automation without any capital investments. Entry-level benchtop automation instruments—such as liquid handlers, pipetting assistants, automated plate washers, etc.—offer a low barrier to entry and can be a cost-effective way to introduce automation into the lab for most researchers, whether they are in pharma, biotech, or academia. 

Myth 2: Only a specialist can implement the technology and/or my team doesn’t have the experience. 

Many companies are heavily focused on user experience to provide their customers with a more intuitive interface that can be utilized by everyone in the lab. This trend continues with AI-generated code, which lessens the need for power users and, instead, labs can work to upskill their current staff with automated solutions. 

Myth 3: I only get value if I build a fully automated process. 

For most labs, achieving full automation is not essential to maximize the value of their investments. While complete automation can be an objective, even small, incremental improvements can significantly enhance assay performance and reproducibility. These improvements can reduce manual, repetitive tasks that often lead to ergonomic strain, allowing researchers to focus on more valuable activities. Additionally, increased throughput in sample preparation and analysis can accelerate data generation, facilitating quicker grant applications and publications. Furthermore, the ongoing skill development of lab members provides a high return on investment, benefiting both laboratory advancements and the long-term career goals of team members.

Lab Management Certificate

The Lab Management certificate is more than training—it’s a professional advantage.

Gain critical skills and IACET-approved CEUs that make a measurable difference.

A strategic, phased approach to lab automation

We advocate for three phases in which labs can start adopting automation into their workflows, with an emphasis on yielding the highest value return at the most achievable initial cost of implementation.

Phase I should include identifying workflows ideal for automation, getting institutional buy-in, and demonstrating quick returns in assay timing and performance. Start with identifying assays in which manual processes are known, data accuracy and reproducibility could be improved, difficult or complex manual steps are required, and there are a high number of non-valued manual steps. Next, we recommend identifying the appropriate stakeholders and getting institutional buy-in. This may include leadership, department chairs, IT, QA, and core facility heads. By demonstrating the added value of implementing automation early on, stakeholders are more likely to approve and support the capital expenses required.  Start with the easiest benchmark to provide value-add: hands-off time and assay results, including improved accuracy and reproducibility. Finally, consider what types of benchtop or entry-level automation could be utilized. Look across the lab to what may already be available, or if purchasing, consider what instruments may fit into existing lab workflows. These may include liquid handling, plate handling or washing, feeding into plate readers, sample labeling or barcoding, and sample identifying/tracking. By integrating the types of instruments, you can remove as many manual steps as possible. Also, look to shared equipment that is underutilized to integrate into such workflows.

In Phase II of your implementation, add complexity through scheduling/monitoring and notification for walk-away operations. Consider who you can consult with to connect systems into ELN and LIMS to manage experimental data and facilitate common practices across the laboratory. With automation, consider expanding by addressing additional steps where practical. Ideally, labs can expand their experimental hours by focusing on automating overnight or weekend work without burdening staff.  

At this point, you should start to be able to measure and communicate your success. It is time to communicate how you have accelerated experiments and the outcomes of your efforts. By socializing your achievements, your lab can demonstrate how thoughtful automation can unlock both scientific and operational gains, from improved reproducibility to greater efficiency. Internally, celebrate the wins to foster a culture of innovation where team members are continuously exploring new workflows and ideas for future automation.

Phase III builds on the successes of previous phases, where the lab focuses on extending automation flexibility and usability. Significant barriers to the adoption of automation in the lab include tools that are overly complex for the task or too purpose-driven and closed by design. Striking the balance between too much and too little flexibility is difficult. The recommendation from here on out is to take a pragmatic approach focused on areas of assay improvement, allowing the tools that can be integrated into your automation plans to be aligned to the assay.  Additional value in artificial intelligence, machine learning, and even augmented/virtual reality are considerations at this point, as these tools can provide new perspectives, modeling, and optimizations that would be difficult to identify.  

Regardless of the stage or pace of implementation, avoiding pitfalls while adopting new automation technologies into your workflows should be top of mind. Often, projects are over-scoped and have unreasonable metrics or timelines. Focus on keeping the project scope simple and aim for quick wins around reproducibility of experiments or time savings by removing manual steps. Be sure to measure those successes to show demonstrable added value before advancing to more complex workflows.  

In summary, we advocate that by embracing a systematic upscaling approach, laboratories can leverage growth in their current automation more effectively, paving the way for accelerated research outcomes without the need for extensive infrastructure changes.