What Is Lab Automation?

Whether you're a lab leader, scientist, or automator, you're likely seeing workloads increase and become more complex. Many labs are turning to lab automation solutions. But what is lab automation, how does it work, and what benefits does it bring? Let's explore the topic.

In this guide, we'll explain the different levels of lab automation, from simple tasks to full walkaway automation, plus the benefits it brings, and the challenges it can present.

We'll also look at how AI is now making it possible to generate new design candidates faster than ever before, examine future trends, and consider the infrastructure required to build a fully automated lab.

What Is Lab Automation

Lab automation uses hardware and software to perform various tasks faster and better. These tasks can range from automating a single repetitive process, like pipetting, to integrating entire workflows that handle sample preparation, analysis, data management, and reporting.

The goal of lab automation is to streamline operations, reduce errors, and free up lab scientists and technicians to focus on experimental design and analysis. When fully implemented, automated labs can even conduct hundreds or thousands of experiments in parallel.

Common applications for lab automation include catalyst screening, solvent selection, candidate design and validation, reaction optimization, and library synthesis. Biotech and pharma companies are increasingly using lab automation to build workflows that are both flexible and scalable.

Types of Lab Automation

Lab automation can be implemented at different scales, depending on the size and setup of your lab. These include:

• Task-Level Automation: This automates specific steps, such as liquid handling or sample tracking, within "islands" of automation. It usually makes sense to start here.
• Workflow Automation: This connects automated steps, but still requires "human-in-the-loop" oversight and manual input. It commonly links multiple devices with robotics, such as moving lab samples.
• Full “Walkaway” Automation: This enables end-to-end processes to run with little to no human intervention, and dramatically increases throughput. Many labs at this stage are integrating machine learning and AI for predictive analysis and self-directing experiments.

It's worth noting that some influential industry voices talk about different levels of lab automation, from zero to fully AI-driven autonomy. Huge gains can be made by shifting up a level, which requires an infrastructure layer to connect devices reliably and easily.

For example, Dutch-Swiss biotech company Cradle managed to save thousands of human hours and quadruple efficiency by integrating lab automation into their protein candidate testing with UniteLabs.

“To run a truly automated lab, you need to bridge the gap between instruments, LIMS, and data lakes," says Harmen van Rossum, Co-Founder of Cradle. "Connecting UniteLabs with Benchling enables this and brings us closer to becoming one of the fastest labs in the industry by letting us iterate faster and generate high-quality data for our AI models.”

Main Benefits of Lab Automation

Adopting automation in the lab offers several big advantages. These include increased efficiency and experimental throughput, as automated systems can process samples in less time, and even run continuously.

It also offers labs better accuracy and reproducibility, by reducing human error and variability. This matters a lot as labs struggle with a replication crisis that sees over half of scientists failing to reproduce even their own work, let alone the work of others.

Boring but important – lab automation can also bring significant cost savings, as it lowers human labor and reduces lab waste. Scientists can spend more of their time on experimental design, analysis, and innovation.

Finally, lab automation brings better data management, with automated data capture and integration making it easier to analyze results and maintain compliance.

Key Challenges of Lab Automation

There's a vast array of technologies within lab automation, including: automated liquid handlers and pipetting systems; robotic arms for moving samples and plates; sample tracking systems; and integrated software platforms for scheduling, data capture, and analysis.

A key challenge however is integration. Different devices and software can be hard to connect, especially when they come from different companies and require significant training. Which makes it doubly frustrating when integrations break and expensive hardware sits unused, leaving lab leaders unable to realize the long-term savings that automation offers.

There is a solution though – giving lab automators the tools to connect devices and build workflows in code. With UniteLabs, this can be done in Python code, and without risking any warranties. It also unlocks a variety of new capabilities, such as parallelization of tasks.

"We remove vendor software from the middle," says Robert Zechlin, Co-CEO of UniteLabs. "That gives customers better observability into what the instrument is actually doing, and makes it possible to control different instruments in a more consistent way."

Labs can even build closed-loop systems that design, execute, and learn from experiments autonomously with AI support. They can ensure experiments run consistently, manage change control with true versioning in Git, and trace outcomes back to specific system configurations.

Applications Across Industries

Lab automation is transforming a variety of sectors, from biotech and pharma companies looking to accelerate drug discovery and quality control, to clinical and diagnostic companies seeking to increase testing accuracy and speed.

It also supports better environmental and food testing, enabling labs to manage high sample volumes and ensure compliance. Within chemical and industrial labs, automation can reduce risk, improve consistency, and save time.

To take one example of the latter, Matthias Pursch, a Fellow at Dow Chemical Company, recently told the Agilent Lab Automation Days event that he has seen projects with time savings of around 50% in data processing, and an 80% reduction in time spent on report generation.

Future Trends and Innovations

As more labs start leveraging automation technology, we expect to see huge strides in the coming 12 months. Emerging technologies like AI-driven experiment design, modular automation platforms, and enhanced data integration are making automation more accessible and more powerful.

Emerging trends such as collaborative robotics, intelligent workflow optimization, and tighter LIMS integration are likely to become widespread. Labs will start to accelerate their discoveries, dramatically improve data quality, and respond confidently to new challenges.

"The ultimate goal would be that we can connect all these beautiful instruments... and make them interoperable and talking to each other," says Tom Kissling, Lab Automation Partner at Roche. “I'm convinced that with the tools we have at hand and the experts we have in the field, we're able to do that soon.”

Getting Started with Lab Automation

Lab automation doesn’t always require a complete "rip-and-replace" overhaul. Many labs can begin by automating one or two of their key processes, and expand as they see positive results. The best approach will depend on your lab's specific needs, goals, and the existing technology stack.

At UniteLabs, we believe that biology is becoming software-like on the design side (AI proposes designs in hours), but remains hardware-bound on the validation side, as labs can take months to validate these designs. Why? Because devices struggle to connect.

Which is why we built a lab operating system that connects to the firmware of any device. It turns lab automation from bespoke projects into a compounding software layer, lowering the cost per decision-quality data point, and accelerating validation cycles.

With UniteLabs, scientists can stay in their Lab Information Management System (LIMS) or Electronic Lab Notebook (ELN) of choice, as they run workflows and access experimental data. This frees them to focus on discovery and innovation.

Lab automators can get walkaway workflows running reliably, without manual data transfers, endless debugging, or black boxes. They get the toolset needed to build solid, scalable lab automation, with reusable scripts and reliable connectors.

And lab leaders can start scaling throughput, without vendor lock-in preventing them from pivoting experimental designs. AI-enabled discovery becomes realistic.