Transforming Lab Robotics: Industry Insights Part 1

How Robotic Automation is Shaping the Modern Laboratory Landscape

Today’s laboratories face growing pressure to deliver faster, more accurate results while also contending with stricter regulations, rising complexity, and chronic skills shortages. Traditional manual workflows are increasingly unable to keep pace with the demands of the modern lab, forcing organizations to look to robotic automation as a potential solution.

This white paper is the first in a series of three from ABB and METTLER TOLEDO drawing from a Voice of the Customer survey of professionals from the pharmaceutical, biopharmaceutical, chemical, and battery development industries. The survey assessed attitudes toward the current state of laboratory operations, as well as the opportunities and challenges of successfully implementing automated workflows at scale.

This first white paper shows the current state of laboratory operations. It identifies the main reasons for robotic automation and highlights the common challenges and issues that slow down adoption.

Future papers will explore opportunities for robotic automation. They will discuss how organizations can create an autonomous, data-rich, and connected “lab of the future.” The papers will also cover what to consider when choosing the right partner for this journey.

Click the link below to access the PDF version of the first white paper.

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Key Findings At-a-Glance

Robotic Automation Adoption Is Uneven

Most labs remain in their early stages, focused on repetitive tasks like prep and QC.

Fragmented Systems

Disconnected tools force manual data transfer, causing errors and delays

QC Leads, R&D Lags

Standardized QC is easier to automate while variable R&D processes is slowing adoption.

Early Progress Shows Promise

Pilots and retrofits show that automating improves speed, reproducibility, and cycles.

Talent Is Underused

Skilled scientists spend too much time on low-value tasks.

Bottlenecks Persist

Manual delays cascade across projects, cutting productivity.

Strategic Integration Is Essential

Phased rollout, interoperability, and leadership drive success.

Accelerating Lab Testing: Robotics for Speed, Safety, and Precision

Lab testing has changed significantly in recent years. New technologies, evolving markets, and skills shortages are shaping laboratories at an increasing pace. In particular, rising customer demands for faster results and time to market are pushing companies to find ways to do things more quickly, accurately, and efficiently, without compromising on safety. Traditional manual processes are often hard to speed up without sacrificing quality or affecting the integrity of results. Research and development (R&D) is highly complex, while quality control (QC) faces increasing demands for accuracy, reproducibility, and throughput.

Many processes also involve the handling of potentially dangerous and highly potent substances, which compromise the safety of lab workers if not properly handled.

As organizations the world over search for solutions to these challenges, many are increasingly looking to robotics as a potential answer.

New processes are emerging that combine robots with lab instruments to automate, enhance, and optimize critical lab workflows. These systems blend precision-engineered robotic platforms with specialized software, sensors, and modular instrumentation. They can also be optimized for highly specific applications and use cases such as sample preparation, formulation, mixing, analysis, and reporting. Crucially, they achieve these workflows with minimal manual input.

Key Factors Driving The Adoption of Laboratory Robotics

Growing Complexity

Advances in materials science, synthetic biology, and chemical engineering are enabling labs to manage more variables, handle and process larger data sets, and speed up iteration cycles.

Transforming Lab Robotics: Industry Insights Part 1

Higher Throughput

In industries like pharmaceuticals and battery development, the rapid screening and testing of candidate compounds or materials is essential to speed up development cycles while remaining competitive.

Regulatory Pressure

Robotic workflows enhance traceability and consistency, which are critical for meeting regulatory standards, and ensuring the validity of scientific outcomes.

Digital Transformation

The move toward “smart labs” and digitalized ecosystems has widespread benefits, but it also needs to be underpinned by effective technologies. Automated data capture, seam less integration with Laboratory Information Management systems (LIMS), and real-time analytics are all fundamental to achieving the lab of the future.

Labor Shortages

Rising retirements and too few new entrants are seeing many labs struggling to fill vacancies. Automation helps close the widening skills gap. It offloads repetitive or precision-sensitive tasks. Offloading repetitive tasks allows existing staff to focus on higher-level research activities that demand more human involvement.

Advantages and Considerations of Automation

Automating workflows offers substantial gains. Chief among the benefits is a reduction in human errors. By minimizing manual intervention, robotics reduces the likelihood of mistakes. This helps to improve reproducibility while also ensuring uniform data quality.

Running processes 24/7 or across several workflows in parallel improves both throughput and scalability. This speeds up testing and development cycles, helping to make better data-based decisions.

Robotic arm performing automated lab testing

Tailored Workflow Automation for Labs

Unlocking the vast potential of these opportunities cannot be achieved overnight. Nor are there any ‘one-size fits all’ solutions. Organizations must consider a wide range of specific factors such as initial capital investment, system interoperability, change management, and the need for customization to fit unique lab environments.

Successfully deploying increased workflow automation also requires thoughtful integration with existing processes, proper validation, and a clear understanding of how both human and automated workflows complement each other to the greatest effect.

This white paper is the first in a series of three papers. It comes from ABB Robotics and METTLER TOLEDO. The paper examines the major trends and concerns that drive robotic automation in laboratories. It also discusses key available technologies and their benefits. Finally, it explains how to implement strategies effectively to achieve the best outcomes.

Voice of Customer Project

Approach

The contents of this document are based on a targeted global Voice of the Customer survey featuring extensive and detailed interviews with professionals from the pharmaceuticals, biopharmaceutical, chemical, battery development, and electronics industries.

These industries were selected primarily because of their heavy reliance on lab operations to deliver both innovation and regulatory compliance. As such, many are ideal candidates for a greater deployment of robotic technologies. The survey design attempted to ensure a balance of perspectives across different functions, capturing viewpoints from both centralized R&D hubs and QC testing disciplines.

This holistic approach gives us confidence that the conclusions and recommendations presented reflect the real world needs of organizations working at the cutting edge of science and technology.

Methodology

Questions

Covering multiple aspects, including:

1.	Future vision	The future vision for your lab and where robotics fit in.
2.	Current situation in the lab or workplace today	How well does your lab currently run?
3.	Current robotic automationl evels and opportunities	Could automating workflows help you use your people more effectively?
4.	Challenges and barriers tor obotics	Could automating workflows help you use your people more effectively?
5.	Short-term plans for tackling challenges	How could automation help you meet your current challenges?
6.	Desired features and benefits	What do you look for when it comes to robotic assisted workflows?
7.	Implementation	Addressing concerns and getting what you want from automated processes.
8.	Decision-making process	How do you decide on the best supplier and who makes the decisions?
9.	Decision factors	How important are cost, reputation and integration when deciding?
10.	Return on Investment	How do you measure the Return on Investment (ROI) of lab equipment and robotics?
11.	Staying up to date	How do you keep up to date on laboratory trends and innovation?

Current State of Robotic Laboratory Automation

Many in the field share a common vision for fully autonomous, digitally integrated labs. However, survey respondents showed a more uneven picture relating to how they currently deploy automation in their lab applications. The survey showed that most labs remain in the early stages of robotic automation, with notable variations across and within different industries.

“It’s not that we don’t want to automate. Most of us are desperate to, but until we can, we’re stuck doing things by hand that really should have been automated years ago.”
VoC Contributor’s Insight

A Snapshot of Current Adoption

The survey revealed important findings. In many cases, current automation efforts focus on specific, repetitive manual tasks. These tasks include liquid handling, sample preparation, and simple analytical routines. They do not focus on integrated workflows from start to finish.

Often, automation is applied selectively in areas where throughput and precision are of critical importance, while more complex or interpretive tasks remain manual.

Some labs have advanced further. One reported a pilot-scale, fully automated workflow in a high-throughput lab setting, demonstrating how significant progress can be achieved with the right infrastructure and strategic support. However, such cases currently tend to be the exception rather than the norm.

Researcher operating precise automated laboratory equipment

.Scientist holding test tubes in busy laboratory setting.

Robotic Reach and Limitations

Currently, automation is most commonly deployed in QC, sample management, and specific formulation or testing steps. More complex applications, such as material synthesis, advanced analytics, and experimental design, remain largely manual. Adding robotics in R&D environments is often more challenging than in QC, due to their unpredictability and the greater need for flexibility.

As one contributor noted, “Automation is more advanced in QC than R&D, because the processes are more stable and easier to standardize.” From this and other similar responses, we conclude that automation gains traction faster where processes are well-defined and outcomes are tightly controlled.

Early Progress and Standardization

Progress is rising as organizations adopt modular automation strategies. By prioritizing standardization, they are efficiently integrating robotic workflows into repeatable research processes.

Complete redesigns are often unnecessary. Many labs now favor retrofitting and phased integration, successfully adapting existing facilities by working within established physical constraints.

In contrast, some are beginning to plan robotics-ready lab spaces, with connectivity, scalability, and accessibility for both robotics and scientists in mind.

.Laboratory technician using pipette with robotic arm

Crucially, the benefits of robotic automation also help to speed up the various processes involved in developing and bringing new products to market. In the words of one respondent, “Automated systems generate more data in a short time than was possible over the past decade. By combining this with the enhanced speed delivered by automated processes, we can now accelerate idea-to-experiment cycles at the research and development stage, and test and validate concepts faster.”

Key Challenges and Focus Areas

While the benefits of automation are broadly recognized, many labs continue to face day-to-day inefficiencies that put a strain on their time, talent, and resources. Too many scientists and technicians are still spending too much time on low-complexity activities that could be automated.

“The way we work today might have made sense 20 years ago. But it doesn’t work anymore, especially if we want to grow, compete, and innovate.”
VoC Contributor’s Insight

Risk Factors

Risks and Inefficiencies of Manual Processes

“Highly skilled scientists are spending hours doing basic prep work, chasing equipment availability, or reformatting data just to get through the workflow. That’s not what we hired them for.” This is echoed by other respondents: “It’s not that we don’t want to automate. Most of us are desperate to, but until we can, we’re stuck doing things by hand that really should have been automated years ago."

Aside from wasting skills that could be deployed more effectively within the lab, having staff stuck on carrying out repetitive tasks also increases the potential for human error. Mistakes arising from inattention, boredom, or fatigue may not be immediately obvious and can propagate through the workflow, resulting in out of specification (OOS) results, or flawed data which can then feed into flawed decision-making.

Scientist working with lab equipment and samples

Sample Prep Delays Impact Productivity

Several professionals shared examples of delays in delivering critical analyses due to backlogs in sample preparation or resource bottlenecks. Others pointed to situations in which their teams had been forced to compromise on experimental design because there was not enough capacity available to support more complex protocols. For teams that frequently work to tight deadlines, these delays often result directly in lost productivity and increased operational risks.

Data Variability in Manual Processing

Manual processing can also lead to variations in the quality of data. In situations where different individuals execute procedures with varying levels of experience and expertise, even minor variations in technique or interpretation can lead to inconsistencies. This variability can undermine confidence in results and make it more difficult to establish standardized workflows. This is particularly the case in environments that rely on strict regulatory compliance or where there is a need to transfer knowledge frequently between global teams. Without automation to enforce consistency, delivering reproducible results is challenging.

scientists working in lab using automated processes

“We sometimes don’t catch mistakes until several steps down the line, when we realize something’s gone wrong and we have to start again. It’s frustrating and costly,” said one interviewee. The linear nature of many manual workflows also increases the risk of bottlenecks. A single delay, such as late sample preparation or instrument calibration, can potentially hold up an entire day’s work, forcing teams to reschedule planned experiments or reallocate their time to other tasks. The impact of these interruptions cascades, delaying not just individual experiments, but entire projects or development cycles."

.Scientist handling complex lab equipment

Challenges of Integration and the Future of Lab Workflows

Even advanced automated systems can pose challenges if systems aren’t properly integrated. Many labs operate with a mix of hardware and software platforms. These platforms were never designed to work together or to communicate with each other. Data must be manually extracted, reformatted, and uploaded across systems, wasting time and increasing the likelihood of transcription errors. This inherent fragmentation makes it difficult for labs to build streamlined workflows and hampers their efforts to scale. One respondent observed that their lab had “state-of-the-art instruments that can do incredible things, but no way to connect them in a meaningful process. So, we still rely on a person to carry samples from one bench to another, and another to stitch together the results.” When combined, these issues have an operational and strategic impact that highlights the hidden cost of manual workflows.

.Scientist pipetting samples at laboratory

The Risks of Relying on Outdated Manual Processes

Labs that continue to rely upon outdated, manual processes will increasingly fall behind, lacking the infrastructure to deliver faster, more reliable results, scale their efforts, or redirect staff toward innovation.

So, we still rely on a person to carry samples from one bench to another, and another to stitch together the results.” When combined, these issues have an operational and strategic impact that highlights the hidden cost of manual workflows. Labs that continue to rely upon outdated, manual processes will increasingly fall behind, lacking the infrastructure to deliver faster, more reliable results, scale their efforts, or redirect staff toward innovation.

Key Takeaways

Robotic Automation's Role in Growth

Everyday friction, wasted talent, and fragmented systems highlight robotic automation’s future role in unlocking lab potential and laying the foundations for growth.

Boosting Lab Innovation and Efficiency

When done well, adding robotic automation speeds up innovation. It helps laboratories follow rules. It allows skilled workers to focus on more important tasks. This approach enables laboratories to deliver results faster and more reliably. They can also remain competitive.

Streamlining Labs

To achieve this, organizations should look to streamline processes, expand automation beyond basic instrumentation, integrate data solutions, and simplify reporting. Retrofitting existing labs with modular, interoperable systems, and designing new ones with scalability in mind enables progress without major disruption.

Navigating Adoption Challenges

While the long-term rewards of robotic lab automation are clear, there are often many obstacles to overcome before they can be realized. Conversations with the survey respondents revealed three overarching categories: cultural and organizational resistance, technical limitations, and financial or strategic hesitation.

“Mindset is one of the biggest obstacles. People are used to being in control of every step. When you introduce automation, some see it as losing control, not gaining capability.”
VoC Contributor’s Insight

Challenges

Cultural and Organizational Resistance

automated processes in daily tasks of scientists

Respondents repeatedly mentioned that they manage change from both cultural and operational perspectives.

Many lab workers are understandably concerned by the ability of robots to outperform and/or replace them, carrying out certain tasks faster, more accurately, and more consistently, without stopping or needing a break.

Some professionals also expressed concern that robotics could displace roles or devalue traditional expertise. In many labs, teams have relied on long-established methods and manual routines. Therefore, any disruption to the conventional ways of working, however well-intended, is often met with skepticism, discomfort, or pushback both by workers and, sometimes, unions.

Robotics is also often misunderstood at the ground level. People widely associate it with full-scale robotics, or assume it applies only to high-throughput industrial environments. In such cases, staff may be unaware of smaller, incremental forms of collaborative robotics that can ease workloads without dramatically altering their roles.

In these cases, the challenge is not just introducing a new system but also managing change. Some respondents use a technique to gain staff buy-in for robotics. They start with small lighthouse projects that have a high impact. These projects directly benefit human workers by, for example, automating repetitive tasks such as sample preparation.

Such “lighthouse” projects help to demonstrate value quickly and can also be instrumental in gaining wider acceptance and building momentum generally. “I started small with impactful projects. When people saw real benefits, such as more time for analysis and fewer mistakes, they became much more engaged,” said one contributor.

Technical limitations and Integration Barriers

Scientist manually handling lab equipment and samples

In situations where lab operators are operating a patchwork of instruments, data systems, and software platforms, such barriers can constrain capabilities, particularly if personnel have little or no knowledge of using robots. This will then limit the ability of operators to create seamless, automated workflows.

In brownfield sites, especially, lab facilities were not designed with either robotics or automation in mind, and lack physical space, environmental controls, or connectivity to accommodate robotic systems or cloud-based platforms. Legacy instruments used in those labs may still deliver reliable results, but offer no APIs (Application Programming Interfaces), digital outputs, or remote monitoring capabilities, making them incompatible with many modern robotic frameworks. This incompatibility leaves teams with a choice between expensive upgrades or time-consuming workarounds.

The complexity of integrating automation with legacy systems remains one of the most significant technical barriers facing the industry.

Organizations often lack connectivity tools to enable data to flow between systems. “Integration is the hardest part,” said one respondent. “Different platforms use different languages, and there’s no common data layer. You either have to build something custom or rely on manual handoffs.” In such environments, while automating a single task may be achievable, automating an entire workflow often requires substantial IT support, third-party tools, or even replacement systems, increasing the levels of complexity and risk.

Organizations that are successfully moving towards robotic workflows often use a modular approach. They identify areas of the lab that are ready for automation now. At the same time, they plan infrastructure updates that allow for future scalability.

Financial Constraints and Strategic Uncertainty

importance of centralized robotic automation strategy

Securing the investment needed for both purchasing and implementing robotic workflows can be a significant barrier to adoption, especially for organizations operating with limited budgets or short planning cycles.

As such, securing buy-in from decision-makers can often hinge on the ability to explain, and if possible, quantify potential returns up-front. This can be difficult to do, particularly in R&D and early-stage environments where automation delivers quality, reproducibility, or innovation, rather than immediate tangible cost savings. One respondent noted, “It’s not that leadership doesn’t see the value. They do. But it’s hard to put a number on it until you’ve already made the investment. That makes it hard to justify.”

The lack of organizational alignment complicates the situation. There may be no centralized robotic automation strategy. This situation leaves individual teams or departments to advocate for projects independently.

A fragmented approach can make it harder to secure funding, coordinate efforts, or achieve interoperability across systems. One participant explained that while there was interest in automation at multiple levels of the organization, “there wasn’t a clear owner for it, so nothing moved forward in a coordinated way.”

Strategically, robotic automation is also often in competition with other priorities that may be seen as having greater urgency or importance. Some organizations are therefore beginning to embed automation into wider strategic roadmaps, reframing it as a foundational capability that supports multiple business outcomes, and tying it to goals around scalability, digital maturity, and operational resilience.

Key Contributors' Insights

“Some team members were unaware that the tools they were already using were part of a broader automation initiative. Once they realized it was there to help and not replace them, their attitudes changed.”

“Integration is the hardest part. Different platforms use different languages, and there’s no common data layer. You either have to build something custom or rely on manual handoffs.”

One participant explained that while there was interest in automation at multiple levels of the organization, “there wasn’t a clear owner for it, so nothing moved forward in a coordinated way.”

Overcoming the Barriers

While these challenges are significant, they are not insurmountable. Our research highlights that successful implementation is as much about people and planning as it is about hardware and software.

Change management, internal education, and cross-functional coordination are essential to overcome resistance. Technical integration must be approached pragmatically, with modular planning and future-proof infrastructure.

Financial and strategic alignment fundamentally requires leadership and a long-term vision.

Key Takeaways

Correct Automation

Defining the right automated solution and understanding how to articulate its benefits clearly to management is of key importance in getting buy-in from all stakeholders.

Communication

Clear communication is also essential when rolling out the solution to users. Transparent communication, inclusivity, and leadership support are all key tools for overcoming resistance. Starting small and demonstrating the direct benefits can often be the best way to win acceptance from skeptical or reluctant staff.

Transforming Labs

Ultimately, labs that treat robotic automation not merely as a tool but as a transformational initiative that encompasses culture, infrastructure, and business strategy, will be in the best position to lead the next era of scientific innovation.

Conclusion - Laying the Foundations

While robotic lab automation adoption is advancing, its progress remains uneven and constrained by fragmented systems, underused talent, and cultural or technical barriers. Yet the benefits are clear. Robotics, when properly planned and executed, has the potential to deliver faster cycles, more reproducible data, and the ability to free skilled scientists to focus on higher-value work. Early adopters have demonstrated that with phased implementation, modular solutions, and strong leadership, automation can transform workflows without major disruption.

This first paper has outlined the current drivers and barriers affecting robotic lab automation. The next paper in this series will look ahead, exploring how organizations can move beyond incremental gains to realize autonomous, data-rich, and seamlessly connected “labs of the future.”

Glossary of Acronyms and Terminology

AI-Powered Full Glossary

Term/Acronym	Definition	Short Description.
VoC	Voice of Customer	Direct input/feedback from customers or end users
Lab report	Analytical documentation for laboratory work	Record of lab procedures, results, and analysis
KPI	Key Performance Indicator	Quantitative measure of performance against goals
NPS	Net Promoter Score	Customer loyalty metric: measures willingness to recommend
Stakeholder	Individual/group affected by the project	Anyone with interest or influence in a project
Gap analysis	Implementation	Addressing concerns and getting what you want from automated processes.
Root cause	Decision-making process	How do you decide on the best supplier and who makes the decisions?
Workflow	Decision factors	How important are cost, reputation and integration when deciding?
Action plan	Return on Investment	How do you measure the Return on Investment (ROI) of lab equipment and robotics?
Sample throughput	Staying up to date	How do you keep up to date on laboratory trends and innovation?
QC	Quality Control	Lab function ensuring accuracy and reliability
SLA	Service Level Agreement	Contractual commitment on service standards
Metrics	Measurable indicators for assessment	Values tracked for performance/quality monitoring
Pain point	Specific challenge or difficulty encountered	Issue causing operational inefficiency or dissatisfaction
Lighthouse project	A flagship or pilot	A showcase project intended to lead or inspire broader change, new approaches or technologies
LIMS	Laboratory Information Management System	Digital system for managing lab data, workflows, and samples
ELM	Electronic Lab Manual	Digital version of manual lab record keeping
ENB	Electronic Notebook	Digital notebook for recording lab observations and data.
LES	Lab Execution System	Software system for managing, guiding, and documenting lab procedures and workflows to ensure compliance and reproducibility

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​Transforming Lab Robotics: Industry Insights Part 1​

​How Robotic Automation is Shaping the Modern Laboratory Landscape​

​How Robotic Automation is Shaping the Modern Laboratory Landscape​

Key Findings At-a-Glance

Robotic Automation Adoption Is Uneven

Fragmented Systems

QC Leads, R&D Lags

Early Progress Shows Promise

Talent Is Underused

Bottlenecks Persist

Strategic Integration Is Essential

Accelerating Lab Testing: Robotics for Speed, Safety, and Precision

Key Factors Driving The Adoption of Laboratory Robotics

Growing Complexity

Higher Throughput

Regulatory Pressure

Digital Transformation

Labor Shortages

Advantages and Considerations of Automation

Tailored Workflow Automation for Labs

Voice of Customer Project

Approach

Methodology

1-hour interviews with respondents representing QC, R&D, and Testing Services functions

Questions

Covering multiple aspects, including:

Example of a Digitized Laboratory Ecosystem

Current State of Robotic Laboratory Automation

A Snapshot of Current Adoption

Robotic Reach and Limitations

Early Progress and Standardization

Key Challenges and Focus Areas

Risk Factors

Risks and Inefficiencies of Manual Processes

Sample Prep Delays Impact Productivity

Data Variability in Manual Processing

Challenges of Integration and the Future of Lab Workflows

The Risks of Relying on Outdated Manual Processes

Key Takeaways

Robotic Automation's Role in Growth

Boosting Lab Innovation and Efficiency

Streamlining Labs

Navigating Adoption Challenges

Challenges

Cultural and Organizational Resistance

Technical limitations and Integration Barriers

Financial Constraints and Strategic Uncertainty

Key Contributors' Insights

“Some team members were unaware that the tools they were already using were part of a broader automation initiative. Once they realized it was there to help and not replace them, their attitudes changed.”

“Integration is the hardest part. Different platforms use different languages, and there’s no common data layer. You either have to build something custom or rely on manual handoffs.”

One participant explained that while there was interest in automation at multiple levels of the organization, “there wasn’t a clear owner for it, so nothing moved forward in a coordinated way.”

Overcoming the Barriers

Key Takeaways

Correct Automation

Communication

Transforming Labs

Conclusion - Laying the Foundations

Glossary of Acronyms and Terminology

AI-Powered Full Glossary

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Transforming Lab Robotics: Industry Insights Part 1

How Robotic Automation is Shaping the Modern Laboratory Landscape

How Robotic Automation is Shaping the Modern Laboratory Landscape