Summary

Laboratory automation has a documented history stretching back more than four decades — but the people who practice it have never quite been recognized as belonging to a single, defined profession.

The techniques used to automate laboratory work have matured a long way: from early efforts in data acquisition and instrument control, through to today's instruments with built-in computing and communications. The author argues that if the field is going to keep advancing, the people who apply automation and computing technologies to lab work need an organized practice and a shared body of education behind them.

This ebook is built around that argument — an opening move in a larger conversation about defining and developing "laboratory automation engineering" (LAE) as a recognized field. Along the way, it covers:

• What laboratory automation engineering actually is
• Why it deserves to be treated as an engineering discipline in its own right
• How the field has developed over time
• What formalizing it would mean for practitioners, labs, and the profession as a whole
• The sub-disciplines that make up LAE
• The skills an LAE practitioner needs
• Where the field needs to go next

Introduction

Scientists across wildly different fields — chemistry, high-throughput screening, physics, quality control, electronics, oceanography, materials testing — are all applying automation technology to their work. Different disciplines, but a remarkably similar set of skills.

That overlap is the whole argument: there are enough shared traits and competencies among these practitioners to justify calling laboratory automation engineering (LAE) a field in its own right. Formalizing it benefits both the people doing the work and the organizations that depend on their skills — and it's the surest path to actually realizing the payoff that automation has always promised.

Laboratory automation engineering can be defined as the application of automation, information, computing, and networking technologies to solve problems in — or improve the practice of — a scientific discipline. That fits neatly inside the standard definition of "engineering": applying scientific knowledge to practical problems.

What LAE Practitioners Have in Common

How LAE Differs from Manufacturing Automation

Modern manufacturing lines are designed with automation baked in from day one. Laboratory automation almost never works that way — it's a replacement process. Manual techniques get automated only once they've matured, usually driven by economics and the need for more consistent results. If a testing or QC lab were designed from scratch as a fully automated facility, the main thing distinguishing it from a manufacturing plant would be that its "product" is data rather than physical goods.

That's the second key difference: the results of laboratory automation are ultimately data, knowledge, and information rather than tangible output. Done well, LAE opens the door to methods — like high-throughput screening and combinatorial approaches — that simply wouldn't be practical without it.

Developments in Laboratory Automation

Laboratory automation started with scientists who needed to work differently — and the people building those early systems had to be as fluent in computing as they were in their own science.

The first generation of lab automation was about interfacing instruments, sensors, and controllers to computers. From there came experimental control through robotics and direct computer integration, followed by a wave of control software, laboratory information management systems (LIMS), and — more recently — electronic laboratory notebooks (ELNs).

The Integration Problem

Walk into any conference today and it looks like automation has already taken over — most things are automated or will be soon. But one persistent problem remains: integrating products from different vendors. Individual procedures get automated in isolation, while the movement of data and information around the lab stays far less robust than it could be. Some vendors try to solve this by positioning their ELN as the hub of lab operations, pulling data in from instrument-level systems — but that flow is largely one-directional, a limitation of the current generation of products rather than a permanent ceiling.

From Isolated Systems to Connected Ones

Communication standards, cheap storage, and abundant computing power have removed most of the old constraints. Data analysis no longer has to happen at the instrument — a data file with its parameters can move to a central system for cataloging, storage, analysis, and reporting, while the raw data stays available for reanalysis as new techniques emerge. Bi-directional communication is what finally connects the dots on the automation map, and the field needs practitioners trained to take full advantage of that shift.

Laboratories Don't Stop at the Door

Lab systems are part of the broader corporate network, whatever kind of organization runs the lab. The purpose of a laboratory is to generate knowledge, information, and data; the purpose of automation is to make that process more effective and cost-efficient — while building in the security to protect it and the accessibility scientists actually need.

This is also where friction shows up between corporate IT and laboratory computing. IT owns corporate computing and is responsible for network security; labs are part of the corporation and need specialized tools IT often doesn't fully understand. Neither side is wrong — but "IT-speak" and "science-speak" rarely translate cleanly, and that gap only gets wider as more instruments become network-connected. A trained LAE practitioner is positioned to bridge exactly that divide.

Why Formalize the Field?

People have been doing lab automation work successfully for decades without a formal discipline behind it. So why change now?

Because "successful so far" isn't the same as "reliably successful." Alongside genuine wins, there have been projects that fell short or were cancelled outright — and even apparent early successes have sometimes locked labs into a platform that hit a dead end, or a product set too rigid to flex as requirements changed. Given how much lab automation could revolutionize scientific work, leaving outcomes to chance is a real cost.

Other technical fields — chemical synthesis, aerospace, computing, civil engineering — followed the same arc: individual pioneers, then small groups, then formal training and standardized design methods once the need to scale became clear. That shift from "art" to actual engineering is what let those fields build things far beyond what any lone practitioner could achieve. Laboratory automation is due for the same transition.

"Engineering" a project means trained people have analyzed and defined it, laid out plans against clear goals, and identified and evaluated risk — a deliberate, confident path from requirements to completion. That's what formalizing LAE would bring to automation work in science.

The Benefits, by Stakeholder

Sub-Disciplines Within LAE

Laboratory automation lets scientists pursue work that would otherwise be physically or economically out of reach — high-throughput screening and combinatorial methods being the clearest examples. As the field matures, it will splinter into distinct specialties.

These three areas overlap considerably rather than sitting in neat isolation, and they all share underlying technologies — computing, programming, networking, and communications. In some projects, separating "data acquisition" from experiment control is genuinely difficult, though attempting that separation can still surface useful insights for system design. Moving across the three, from sample handling toward knowledge management, involvement with the underlying laboratory science tends to decrease while the skill demands shift accordingly.

Skills Required for LAE

In the 1960s, "programming" an instrument meant needle-nose pliers and a stopwatch, adjusting cams and micro-switches by hand. Today's automation systems are built around computers, so a solid computer science and programming background is table stakes — alongside engineering fundamentals, management skill, regulatory literacy, and strong organizational habits. Just as important: a real understanding of the science being automated, since the practitioner has to translate scientists' needs into a working functional requirements document and, eventually, a functioning system.

Five core skill areas show up across every LAE sub-discipline:

6.1 Project and Change Management

Project management covers the expected ground — budgets, schedules, documentation — but change management deserves equal billing. Until labs are built with automation baked in from the start, laboratory automation work is simultaneously a replacement project and a set of new processes imposed on the people already working there.

Thorough documentation is non-negotiable: project goals, structure, milestones, the reasoning behind material and equipment choices, and clear acceptance criteria all give regulators something to review and give the team an objective way to know when the project is actually done. Good LAE practice borrows directly from other engineering disciplines — functional requirements specifications (FRS), user requirements specifications (URS), and design specs drafted before development starts, along with a working familiarity with project and software life-cycle management.

Replacement projects also add real stress to a lab: cost pressure while development is underway, disrupted operations, and cramped space during installation, followed by a validation phase where the new system has to be proven against the old one before cutover — during which both systems typically have to run in parallel, doubling the workload for a time.

6.2 Strong Communication Skills

Communication ties directly back to the people issues above — making sure everyone understands the work, the benefits, the costs, the schedule, the risks, and the implications of any changes. Most project misdirection, delay, and frustration traces back to poor communication. It's a two-way responsibility: the LAE practitioner needs to explain the project clearly and also genuinely listen to what lab staff need.

6.3 Strong Understanding of Regulatory Issues

Regulatory compliance touches every industry and, increasingly, every department — not just manufacturing-focused rules but organization-wide practices spanning finance, HR, and beyond. It can feel like one more burden, but most regulatory requirements simply formalize good system design: proving a system works, is supportable, well-documented, and built from suitable, reliable equipment. Ad-hoc "tinkering" might be fine for a prototype, but it has no place in a production system. At bottom, regulations and standards exist to make sure a system can hold up to long-term use and genuinely fits a well-defined, documented need.

6.4 General Systems Theory and Beyond

Frank Zenie, a co-founder of Zymark Corporation, used to open his robotics courses with a simple line: you can only automate a process, not a thing. Recognizing that a process exists — and that it can be automated — is the starting point for any project, and general systems theory supplies the tools to document that process along with its triggers and state changes, especially in systems with many interdependent parts.

Recognizing a process is only half the job; the other half is whether it can actually be automated. Lab equipment is designed for people, and adapting it for automated or robotic use can call for substantial re-engineering — which in turn raises real questions about the economics and payoff of the project.

Process engineering in this context should also fold in statistical process control, statistical quality control, and productivity measurement. Robotic systems are, in effect, small-scale manufacturing systems even when their output is data rather than a physical product, and as more of the lab becomes automated and integrated, it should be managed with the same rigor as a manufacturing process. Productivity measurement matters too — it's how a lab proves an automation system is actually delivering the efficiency gains it was built for, and builds the case for further investment.

What Sets LAE Apart from Other Engineering Fields

• It operates in a laboratory or scientific environment
• Automation is an enabling technology that opens the door to new scientific methods, including discovery-based science
• It's usually a replacement for existing manual work, which makes change management central rather than optional
• Its scope spans materials handling (robotics), data acquisition, analysis, reporting, and database integration
• It demands both automation expertise and a strong grounding in the science being practiced in the lab

That last point matters most: lab personnel can describe what they want to accomplish, but it's the LAE practitioner's job to figure out how — and to understand the downstream implications for system design. In effect, the practitioner acts as translator, turning scientists' needs into a project plan and, eventually, a working system — not unlike the role of a software engineer.

What Comes Next?

Two major tasks stand between where the field is today and a genuinely formalized discipline — and both fall under the heading of education.

Building University Curricula

The first task is developing a university-level curriculum for laboratory automation engineering, which will need support from both industry and organizations like the Association for Laboratory Automation (ALA). No university will build a program — even one largely assembled from existing courses — without confidence that graduates will find jobs, so employment demand and curriculum development will need to develop hand in hand. Open questions remain: how much laboratory science an LAE practitioner really needs to know, and whether this belongs at the undergraduate or graduate level.

Existing science programs, meanwhile, should weave in automation literacy the same way computer literacy became part of high school curricula — not to turn scientists into engineers, but to familiarize them with how modern science actually gets done. The ALA could take a lead role developing course materials and outlines for instructors who want to bring this into their programs, alongside expanding short courses, certificate programs, and even a dedicated "Institute for Laboratory Automation."

Organizing a Body of Knowledge

The second task is assembling an organized "body of knowledge" for laboratory automation engineering — texts, websites, knowledge bases — with contributions from practitioners across the field. That starts with a framework for organizing the material, which publications like the Journal of the Association for Laboratory Automation could then use to key future papers to a shared structure.

Two design principles matter here. First, core techniques and technologies — analog data acquisition, robotics fundamentals, interfacing, project management — apply consistently across scientific disciplines and can be treated as a shared foundation, even where application-specific caveats exist. Second, once the framework moves into discipline-specific applications, parallel sections are likely to emerge with a similar outline but different equipment and implementation details. The framework should make those parallels visible, so practitioners can recognize cross-fertilization between similar technologies applied in different fields.

Conclusion

Laboratory automation is still early in its development. Progress has been real, but slow and incremental compared to how fast automation and information technology have moved in other fields.

What's needed now is for the practitioner community to embrace building a discipline focused on envisioning, creating, and improving the tools and techniques of laboratory automation. That, in turn, is what will finally deliver on the long-standing promise automation has offered: giving scientists the room to do better science. Laboratory automation needs to be driven by people who want to do good work and are properly trained to do it.

Demand for LAE practitioners will follow the market, but the underlying skill set should travel well — much like computer science professionals move fluidly between industries. LAE practitioners should be able to move from one scientific application to another, with the underlying science being the main thing they'd need to relearn.

Abbreviations & Terms

A quick-reference glossary of the abbreviations used throughout this guide.

About the Original Work

This ebook is an editorial adaptation — reorganized, illustrated, and paraphrased for readability — of a work published by LIMSWiki.

About the Original Author

Joe Liscouski is a laboratory automation and computing professional with more than forty years of experience, including the design of both custom and commercial automation systems, LIMS, robotics, and data interchange standards. Originally trained as a chemist, he has taught and presented on validation and laboratory automation in the U.S., Europe, and Japan, and has worked across pharmaceutical, biotech, polymer, medical, and government laboratories. His book Laboratory and Scientific Computing: A Strategic Approach (Wiley Interscience, 1995) explores related themes in greater depth.

Selected References from the Original

• Hallock, N. (2005). "Creative Combustion: A History of the Association for Laboratory Automation." SLAS Technology, 10(6), 423–431.
• Liscouski, J. (1985). "Laboratory Automation." JCOMP, 25(3), 288–292.
• Matey, J.R. (1999). "History of Laboratory Automation." Centennial Meeting of the American Physical Society.
• Zuboff, S. (1988). In the Age of the Smart Machine. Butterworth–Heinemann.
• Sterling, J.D. (2004). "Laboratory Automation Curriculum at Keck Graduate Institute." SLAS Technology, 9(5), 331–335.