Automation In The Lab: Lessons From Successes And Setbacks

By Ander Chapartegui Arias

Automating the analytical lab can have profound effects on efficiency and throughput.

In one striking example, a department managed to run more than 25,000 experiments in a single year using an automated system — far more than they could ever have achieved manually. This wasn’t just a record for throughput; it fundamentally changed how the team thought about their science. With that amount of data, they could finally train machine learning models, explore strategies that were impossible before, and make decisions faster.

That’s the kind of benefit automation can have: it saves time, multiplies output, and produces data sets robust enough to open new scientific doors.^3,4

In preparing to write this article, I spoke with several teams from leading companies in the industry. With their permission, I draw here upon both their experiences and my own. The following considerations are a primer on laboratory automation — its potential and challenges and the regulatory, cultural, and workplace aspects that most often come up.

Replacing Manual Experiments Altogether

Applications of automation are everywhere. The advantages are clear: high-throughput systems can run thousands of reactions in parallel. Stress-testing platforms for biologics expose samples to controlled temperature, pH, and agitation. Automated visual inspection reduces human subjectivity (and the debates about whether “that speck is a bubble or a particle”). Solubility and polymorph screening benefit from precise dosing and thermal control. And automated ELISA improves reproducibility in QC labs while linking directly into LIMS.

Automation doesn’t just speed things up, it reshapes workflows. In some labs, manual screenings disappeared altogether, replaced by automated runs that now cover entire departments. The scale speaks for itself: “This amount of data is finally useful for machine learning,” as one team put it.

Across the industry, adoption is rising. The global pharmaceutical automation market in 2023 was valued at about $11.16 billion, and it’s projected to reach $25.11 billion by 2031, growing at a compound annual growth rate (CAGR) of about 10.8%. Market researchers expect lab automation value in 2025 of around $2.5 billion, increasing to above $6.3 billion by 2035.^1,2

The Hidden Side: Infrastructure And Cost

But let’s not romanticize it — automation comes with serious demands. Platforms are large, sensitive, and expensive. They need stable benches, compressed air, reliable power, waste lines, and sometimes glove boxes or cleanroom setups. Physical delivery across continents introduces customs delays, missing parts, or small transport accidents that can delay installation for weeks.

Several teams admitted they had underestimated the IT side. “Preferably, we would have a team with more internal IT support. This would have made orchestration easier and reduced our dependency on the vendor,” one said. Looking back, they would have hired a software engineer early, built instrument-agnostic architecture, and chosen the best modules for each job. Many early adopters make the same mistake: they focus on the hardware but forget automation lives and dies by data flow.

The Regulatory Landscape

Regulation is another crucial aspect. On the manufacturer side, it is mostly about safety and compliance: CE/CH marking in Europe or UL/FCC in the U.S., plus standards like IEC/EN 61010 (lab safety) and IEC/EN 61326 (EMC). If the platform is marketed as diagnostic, FDA and IVDR rules also apply.

On the user side, especially in GMP labs, the most relevant rules are EU GMP Annex 11 (computerized systems), Annex 15 (qualification), and FDA 21 CFR Part 11 (electronic records). USP <1058> and ALCOA+ cover instrument qualification and data integrity. And while not legally binding, GAMP 5 has become the expected playbook for system validation.

As one company summarized: “Have a clear strategy for hardware, software, and data structure from day one.” Manufacturers must build safe equipment, but it is always the lab’s responsibility to prove that the system actually works within its own quality framework.

From Purchase Order To Production

On paper, the stages look neat: planning, delivery, installation, implementation, testing, and training. In reality, they are messy.

One team described their first year bluntly: “Initially poor performance of hardware and software, with many issues that should not have been shipped.” On top of that, management expected the platform to be efficient almost immediately. Scientists, however, reported that it took five to 10 times longer than planned to reach stable operation.

This mismatch is common: management often looks for ROI in months, but in practice, it usually takes years. Unless that gap is managed openly, frustration builds on both sides.

Delivery is fragile. Customs delays, small damages, and missing modules are frequent. Installation often reveals extra needs nobody thought of beforehand, and mid-installation requests inevitably push timelines further. Testing, led by the vendor, shows basic functionality, but training is the real turning point. A powerful system is useless if the people running it don’t feel confident.

And then comes validation, including IQ, OQ, PQ.

• Installation qualification ensures the platform is set up and documented properly: modules, utilities, safety interlocks, calibration records, and environmental conditions.
• Operational qualification checks performance: does the stress-testing unit hold pH and temperature stable, does the inspection system detect defects reliably, does the solubility platform dose and mix correctly?
• Performance qualification shows the system works in practice: automated ELISA must match or outperform manual runs, and high-throughput platforms must reproducibly handle thousands of reactions with full traceability. Teams agreed: validation takes time, but it is the backbone of regulatory trust.

Human And Cultural Reality

This is where automation either succeeds or fails. Humans are creatures of habit, and automation forces them to change. Reactions differ, and one company said: “Some jumped right on it, some had to warm up, some ignored it.” Resistance was strongest among chemists who preferred their traditional optimization methods. There was also reluctance to dedicate staff to automation, based on the misconception that once the robot arrives, it will just run itself.

In reality, jobs shift rather than disappear. Associates with classical process backgrounds move into managing automated workflows and data. Hiring priorities also change. As one manager said: “We will not consider candidates without IT flair and automation skills, plus the capacity to track several projects in parallel.”

Trust has to be earned. It grows with repeated demos, internal presentations, and, most importantly, results. “We showed the reactions done, and the system outperformed the chemists.” That was what finally convinced the skeptics.

Morale tends to stay strong. As one company said : “We had few low moments, because everyone strongly believes in the benefit of HTE (high throughput experimentation).” Belief in the long-term value, plus open communication, helps teams push through the slow ramp-up phase.

What Automation Brings

Once stabilized, automation changes the rhythm of the lab. Manual screenings disappear, freeing staff to focus on more creative strategies. But higher throughput also means higher overall workload — the lab can now support many more projects. Analytical support adapts, and new positions emerge: dedicated HTE teams, machine-learning support, even internships.

Far from replacing jobs, automation creates them. And the biggest change is conceptual. As one scientist said: “It changes my whole approach to chemistry and chemistry problems.” Automation makes it possible to test hypotheses at a scale once unimaginable. It also reduces variability and human subjectivity by enforcing standard processes. These two factors — scale and consistency — are what really change the game. They not only accelerate projects but also allow for new kinds of science, including closed-loop experimentation and AI-guided discovery.

Conclusion

Automation is not a product you buy once and forget. It’s a journey that needs planning, patience, and cultural adaptation. The first year will almost always be harder than expected — systems arrive with bugs, workflows take months (not weeks) to stabilize, and management’s expectations are often unrealistic. But with transparency, IT investment, and the right people, the payoff is massive and sustainable: faster discovery, reproducible data, structured data sets for modeling, and entirely new ways of doing science.

The advice from those already working with automation is clear:

• Have a strategy for hardware, software, and data.
• Bring IT support in early. Accept that processes will take longer than you hope.
• Take validation seriously — IQ, OQ, and PQ are not just paperwork but the backbone of regulatory trust.
• Above all, remember that automation doesn’t replace scientists — it changes their role.

Done well, it is not just faster science; it is better science.

Editor’s note: The author wishes to thank the industry companies that helped with this article and Dr. Sarah Kuhn for her input.

References:

1. Pharmaceutical Automation Market Size & Forecast: USD 11.16B in 2023 → USD 25.11B by 2031, ~10.8% CAGR meticulousresearch.com
2. Lab Automation Market Outlook 2025-2035: USD 2.5B in 2025 rising to over USD 6.3B by 2035 Future Market Insights
3. Time savings in sample preparation (lab automation): up to ~94% in some cases Biosero
4. Clinical labs: automation reported to reduce staff time per specimen by ~10% Diagnostics

About The Author:

Ander Chapartegui Arias, Ph.D., is a scientist and project leader with a background in analytical chemistry, laboratory automation, and GMP quality systems. He has validated, installed, and integrated complex automation platforms across Europe, assisting customers in aligning these systems with their internal GMP frameworks. He received his Ph.D. from Humboldt University in Berlin, where he carried out his research at the BAM Institute. He is based in Basel, Switzerland.