LabAthena - Blog

Lab automation: the missing layer in laboratory transformation

Tim Aulike, Head of Sales & Partnerships — Mon, 28 Sep 2026 15:41:30 GMT

Laboratories are generating more data than ever before. Yet for many organisations, the biggest challenge is no longer collecting data. It is connecting people, devices and software in a way that turns data into reliable scientific insight.

Laboratory digital transformation has accelerated significantly in recent years. Research organisations, pharmaceutical companies and quality control laboratories are increasingly adopting digital technologies, including Laboratory Information Management Systems (LIMS), Electronic Laboratory Notebooks (ELNs) and advanced laboratory instruments, to improve data management, collaboration and operational efficiency. The Organisation for Economic Co-operation and Development (OECD) identifies digitalisation as fundamentally changing the way scientific research is conducted, while the National Institute of Standards and Technology (NIST) highlights the growing need for robust laboratory data management as modern scientific instruments generate ever increasing volumes of data.

However, one part of the laboratory has often remained surprisingly manual.

At the laboratory bench, scientists still follow paper-based Standard Operating Procedures (SOPs), manually transfer data between instruments and software, and spend valuable time recording results instead of focusing on science. This disconnect creates inefficiencies, increases the risk of human error and makes it more difficult to maintain complete data traceability.

The question for many laboratories is no longer whether to digitise. It is how to connect every part of the laboratory into one efficient and reliable digital environment.

That is where a Lab Execution System (LES) plays a critical role.

Data is not the challenge

Laboratories already have the systems they need to manage samples and record scientific work. A LIMS provides end-to-end management of samples, analytical data, and quality records. An ELN enables scientists to document experiments, record observations and collaborate with colleagues. Alongside these systems sits another essential part of the laboratory: the instruments that generate scientific data. From balances and chromatography systems to spectrometers and PCR instruments, laboratories rely on equipment from multiple manufacturers. Each instrument often uses its own communicationprotocol and data format, making it difficult to exchange information automatically.

According to the Fraunhofer Institute, laboratories typically operate instruments from multiple manufacturers, making seamless communication between equipment and software one of the biggest challenges in laboratory digitalisation. The result is a disconnect between laboratory instruments and digital systems, leaving scientists to bridge the gap through manual data entry. The issue is not a lack of technology. It is a lack of connectivity.

How the modern laboratory comes together

A laboratory is made up of several connected layers, each with a different purpose. The information layer manages samples, experiments, documentation and reporting through systems such as LIMS and ELNs. These applications manage samples, experiments, documentation and reporting. The instrument layer includes the laboratory equipment that generates analytical and experimental data. Between these sits the operational layer, where scientists carry out laboratory procedures, prepare samples, operate instruments and generate results. This operational layer is where a Lab Execution System fits.

Rather than replacing existing laboratory software, an LES connects scientists, laboratory instruments and digital systems into one continuous workflow. It enables data to move automatically between the laboratory bench and the wider digital infrastructure while guiding scientists through every stage of the process.

More than SOPs

The role of a Lab Execution System extends well beyond replacing paper-based SOPs. While digitising procedures is an important capability, an LES delivers much more. A modern LES guides scientists through laboratory workflows step by step, ensuring that procedures are followed consistently across teams, laboratories and locations. At the same time, it automatically captures data directly from connected laboratory instruments as work is performed.

This removes the need to manually record results before entering them into anothersystem. The outcome is a more consistent laboratory process, improved data quality and complete traceability from start to finish. Instead of asking scientists to adapt to disconnected systems, an LES brings systems together around the scientist.

Why connectivity matters more than ever

Digital transformation is delivering measurable benefits across laboratories, but only when systems are connected. As laboratories continue to digitise, the ability of instruments and software to communicate has become just as important as the technologies themselves. Yet connecting laboratory instruments and software remains a persistent challenge. Equipment is often supplied by different vendors, each using proprietary interfaces and data formats. Without commonstandards, integrating new devices can be slow, costly and difficult to scale.

This is why standards such as LADS (Laboratory and Analytical Device Standard), based on OPC UA, and SiLA 2, developed by the SiLA Consortium are becoming increasingly important. Although they take different approaches, both provide standardised methods for laboratory instruments and software to exchange information, reducing the need for bespoke integrations and making it easier to create connected laboratory environments. These advances are about more than introducing new software.

They depend on creating connected workflows in which data moves seamlessly between instruments, scientists and digital systems. As laboratories embrace automation and AI, these foundations will become increasingly important for improving efficiency, strengthening data integrity and enabling more flexible, scalable laboratory operations. Without connectivity, laboratories continue to rely on manual processes that slow productivity and increase operational risk.

LES, LIMS and ELN each have a different purpose

One of the most common misconceptions is that a Lab Execution System replaces a LIMS or an ELN. In reality, these systems work together. A LIMS focuses on managing samples, tracking their progress through the laboratory and storing analytical results. An ELN focuses on documenting experiments, recording scientific observations and supporting collaboration.

A Lab Execution System focuses on the execution of laboratory work itself. It guides scientists through workflows, connects laboratory instruments and captures data automatically as work is carried out.

Each system solves a different challenge, but together, these systems create a connected laboratory where information flows seamlessly between people, instruments and software.

Building a connected laboratory without replacing existing systems

The first step is understanding how existing systems communicate and identifying any gaps in connectivity. A modern Lab Execution System can integrate existing laboratory instruments with LIMS, ELNs and other digital platforms, allowing laboratories to modernise while protecting previous technologyinvestments.

This approach reduces disruption, supports future growth and creates a scalable foundation for automation, artificial intelligence and advanced data analytics. Modernisation is not about replacing what already works. It is about connecting it.

Turning strategy into execution

The laboratories that will benefit most from digital transformation are not necessarily those with the newest technology, but those whose systems work together. A Lab Execution System provides the missing operational layer at the bench, connecting scientists, laboratory processes, and instruments to enable more efficient and data driven workflows. As laboratories continue to invest in automation, artificial intelligence and digital transformation, this connected approach will become essential for improving efficiency, ensuring data integrity and supporting regulatory compliance.

Laboperator helps research, pharmaceutical and quality-control laboratories connect instruments, digitise laboratory workflows and automate data capture, enabling organisations to modernise existing environments without replacing established systems. Whether you are taking the first steps towards laboratory digital transformation or looking to optimise your existing laboratory infrastructure, our specialists are here to help. Get in touch with the Laboperator team to discover how we can help transform your laboratory into a connected, future ready digital environment: https://laboperator.com/contact

Scientific breakthroughs connected: enabling smarter healthcare and medical research

Ignatius Reza, CTO — Thu, 24 Sep 2026 15:13:51 GMT

Healthcare and medical research are increasingly becoming increasingly automated, connected and data-driven. Laboratories are utilising more instruments, digital systems and experimental data, while artificial intelligence (AI) is creating new opportunities across drug discovery, diagnostics, and the development of new healthcare technologies. The cost of developing a new drug is substantial before it reaches a patient. A 2025 analysis in JAMA Network Open estimated the median R&D cost of an FDA-approved medicine at $708 million, with the average reaching $1.31 billion after accounting for the cost of capital and discontinued programmes.

Therefore, the incentive for making medical research and development more efficient is considerable. However, AI is only one aspect of this transformation. To maximise the utility of these technologies, laboratories also require a digital infrastructure that can connect instruments, workflows, applications, and data. The challenge lies in improving research processes without necessitating laboratories to replace existing systems and procedures.

Moving beyond manual laboratory work

Manual processes remain part of laboratory research. Scientists may record observations in notebooks, transfer measurements between systems, prepare experiments, and document results manually. These processes can lead to practical challenges. For instance, handwritten notes may be difficult for colleagues to interpret and share; physical records may be damaged; and manual transcription may introduce errors. Additionally, information can become difficult to trace and track when distributed across various physical documents and systems.

Digitalisation can alleviate this administrative burden by capturing measurements and other experimental information electronically. Data can then be shared more easily, connected to other systems and traced throughout a workflow. The objective is to reduce manual work of scientists that takes time away from experiments, analysis and scientific decision-making. The benefits of modernising laboratories are becoming increasingly evident. A 2025 Deloitte survey of 104 biopharma R&D executives found that 53% reported higher laboratory throughput and 45% fewer human errors following modernisation. Yet, only 34% indicated that their laboratories were genuinely connected, with centralised data, integrated systems and some automation.

Digital does not always refer to being paperless. Imagine this: a leading pharmaceutical company was already using an electronic laboratory notebook, but its pre-formulation teams still relied on paper records and manual data entry. The solution came in the form of a pilot Laboratory Execution System (LES) across 10 workflows, replacing much of that work with guided digital execution and automated instrument data capture while linking existing equipment to the ELN. The lesson is clear: digitising a laboratory is not synonymous with connecting it.

The making of an intelligent laboratory

The definition of an intelligent laboratory depends on the research being performed and the systems supporting it. At its core, an intelligent laboratory employs data and connected technologies to enable scientists to work with greater context and control. A laboratory may utilise balances, sensors, pH meters, analytical devices and other instruments to collect measurements. It may also employ laboratory information management systems, electronic laboratory notebooks, spreadsheets and other applications to record and manage information. When these components operate independently, information can remain siloed across different systems. However, when connected, data can be captured automatically and made available to the workflows and applications that need it. This approach is relevant across healthcare and other research-intensive industries.

Pharmaceutical research, cosmetics development and food and beverage research may involve various products and scientific objectives, but many laboratory activities are comparable across industries. For instance, investigating how materials behave, measuring scientific properties, testing formulations or comparing the results of different experimental conditions are characteristicof research in any field. While the end-products may differ, the need for reliable measurements, structured data and documented workflows remains constant across scientific workplaces.

The engineering challenge: connecting existing systems

Modern laboratories can contain numerous instruments, software systems and processes. Connecting them requires more than adding another application. One technical challenge is whether existing systems provide suitable interfaces for integration. Some instruments and applications offer APIs and other open interfaces, while others have more limited connectivity. This distinction is crucial because a new platform can only exchange information with another system if that system provides a suitable means of communication.

Further, an organisational challenge arises because technology must address the problems that scientists and laboratory teams encounter. Understanding those issues is vital for designing an effective digital workflow. Instead of creating another isolated system, systems that are already part of the laboratory environment must be connected. For instance, Laboperator adopts an approach that works alongside existing laboratory infrastructure, connecting instruments and systems without requiring organisations to replace what is already in place.

A laboratory can continue using its existing instruments and applications while Laboperator provides an additional layer for connecting systems, organising workflows and transferring data between components. This can be implemented in different technical environments. A separate Kubernetes cluster can host Laboperator which can communicate with other systems through network connections and APIs. Those systems can run in other clusters, in various environments or on distinct premises, depending on the laboratory’s infrastructure.

The key requirement is connectivity between the systems, which means laboratories need not replace their existing technology environment to introduce a connected laboratory platform. Individual systems can continue to perform their existing functions while Laboperator facilitates their collaboration.

Automation is more than robotics

Laboratory automation is often associated with robotic arms and automated sample handling. While these technologies are important, automation can also occur at the software and data level. Some examples of automation include automatically capturing measurements from an instrument; transferring information between systems without manual transcription; and standardisingdata and automatically verifying checking experimental information. When these capabilities are combined, laboratories can minimise repetitive tasks and chances of errors while enhancing documentation. A more advanced automated workflow can connect instruments, robotics and software, thereby allowing an experiment to be executed with limited manual intervention.

The shift from automating individual tasks to coordinating entire experiments is already underway. In 2024, researchers introduced ORGANA, a robotic assistant for automated chemistry experimentation that combines large language models, visual perception and robotic systems. In one demonstration, the robotic assistant planned and executed a 19-step electrochemistry experiment in parallel, with chemists remaining in the loop.

In the user study, participants saved an average of 80.3% of their time when using the system. The demonstration illustrates how software can coordinate multiple stages of an experiment, enabling automated instruments and robotic systems to work together as part of a programmable experimental workflow. The value does not stem from adding AI to a laboratory in isolation but from creating the infrastructure that enables AI and other analytical tools to work with the right data and context.

Connected workflows can support reproducibility

Reproducibility necessitates that researchers understand how an experiment was conducted, which conditions were used and what data were generated. When such information is spread across physical notebooks, spreadsheets, instruments and various applications, reconstructing an experiment can become increasingly challenging.

Connected digital workflows can provide a consistent method for recording experimental processes and results. Workflows can be standardised, data can be captured automatically and information can be associated with the relevant experimental steps. Further, digital workflows can facilitate collaboration. Scientists can access shared workflows, review previous experiments and work from the same information rather than relying on individual notes or separate documents. Consequently, the sharing of experimental knowledge within a research organisation can be simplified. For healthcare and medical research, this is particularly important. Research teams need to be able to understand how experimental results were produced and use reliable information when making decisions about subsequent work.

Turning laboratory data into useful information

Laboratories generate vast volumes of data, but merely storing data does not automatically render them useful. Data must be reliable and available in a structure that systems can process and analyse. Manual data entry and repeated transfers between applications can introduce for errors or lead to incomplete information. Automated data capture can mitigate some of these issues. Standardisation can also facilitate easier comparison of information from different instruments and systems. Once data are connected and structured, they can support more than just the documentation of an individual experiment. Connected data can be used to analyse results, identify patterns and inform subsequent research.

As AI becomes more prevalent in pharmaceutical research, the value of this data infrastructure is likely to increase. McKinsey estimates that generative AI could generate $60 billion to $110 billion in annual economic value for the pharmaceutical and medical-product industries,driven in part by applications across drug discovery, development and productivity. This provides an essential foundation for AI. An AI system can only produce useful results when it has access to relevant data in a format it can process. Building that foundation requires laboratories to address data capture, connectivityand standardisation alongside AI development.

Why APIs and open connectivity matter

With evolving research requirements, laboratory technology continues to transform. New instruments, applications and analytical technologies are regularly introduced. Open interfaces and APIs enable these systems to communicate without requiring every component come from the same provider.

This principle is increasingly relevant as laboratories combine equipment, software and automation from different sources. Laboperator exemplifies this approach, providing a connectivity layer through which instruments can be monitored and controlled, while data can flow between devices, workflows, and other laboratory systems. For monitoring, this includes custom dashboards, remote control, threshold-based alerts and live camera feeds, with data that can be copied or exported in formats such as CSV, XLSX and JSON. Its workflow capabilities also support event-driven actions, conditional logic, scripting and RESTful APIs, allowing various pieces of laboratory infrastructure to work together without requiring them to share the same native software or originate from the same manufacturer.

The significance lies in connecting laboratory equipment as well as in enabling different machines to collaborate as part of a single experimental process. This creates a more flexible technology environment. A laboratory can introduce new tools while maintaining connections with systems already in use. For engineering teams, this is a crucial aspect of building laboratory infrastructure that can evolve. The aims is to establish connections between the different components so that instruments, software and workflows can exchange information where required. This approach can also assisthealthcare and medical research organisations in adapting their laboratory infrastructure as new technologies become available.

A new model for the laboratory is emerging

The development of AI, robotics and connected laboratory systems is creating new possibilities for scientific research. Automated laboratories are demonstrating how robotics and AI can be combined to execute experiments with reduced manual intervention. With advancement in these technologies, laboratories may increasingly use software to coordinate instruments, manage workflows and analyse experimental data. Nevertheless, the role of scientists will remain vital. Scientific research requires curiosity, judgement and the ability to determine which questions are worth investigating. Automation can handle repetitive tasks and provide researchers with better access to information, but it does not eliminate the need for human expertise.

Therefore, the future laboratory cannot solely depend on an increased availability of robots or replacement of manual processes with software. On the contrary, it will require an environment where scientists have reliable data, connected systems and digital workflows supporting their work. For healthcare, the potential impact extends beyond the laboratory itself. Better-connected research environments can facilitate the development of new medicines, diagnostic technologies and other healthcare solutions by providing researchers improved access to experimental information and more consistent processes.

Building a more connected laboratory

The transition towards intelligent laboratories begins with practical improvements. Instruments must be connected, with systems communicating effectively with each other. Workflows must be documented systematically to ensure repeatability. Data must be captured reliably, and Information must be available in a structure that can support analysis and future applications. These capabilities provide the foundation for more advanced automation and AI.

Laboperator addresses this challenge by connecting with existing laboratory infrastructure rather than requiring organisations to replace it. By integrating instruments, data and workflows, laboratories can reduce manual intervention, enhance access to information and create digital processes that can evolve as the research changes.

The intelligent laboratory is ultimately defined not by a single technology but by how effectively its technologies, data and people work together. For healthcare and medical research, that connection can help create laboratories that are more efficient, more traceable and better prepared for the technologies that will shape the next generation of scientific discovery. The next generation of laboratories may therefore focus less on replacing existing systems and more on making them work together. To see what this can look like in practice, explore how Laboperator connects existing instruments, automates workflows and assists laboratories across healthcare, scientific research, food and beverage, and cosmetics in preparing for what comes next. Editorial note: Originally published by Laboperator on Medium.

The next phase of research: five trends that will shape scientific discovery and labs

Dr.-Ing. Sebastian Uhlig, Solution Engineer — Wed, 23 Sep 2026 10:10:32 GMT

Pharmaceutical research is generating more data, while drug development remains a lengthy and expensive process. IQVIA reports that the median end-to-end clinical development timeline reached 10 years in 2025, the longest point of the past decade. The Congressional Budget Office reinforces this picture, estimating that average R&D costs range from less than US$1 billion to more than US$2 billion per new drug, including failed drugs and capital costs. For pharmaceutical laboratories, the challenge is therefore not only to conduct more research. It is to reduce manual work, make experimental information easier to use and connect the systems involved in scientific decision making.

Five trends are reshaping how that work gets done: voice interaction, artificial intelligence, automation, interoperability, and the growing connection between precision medicine and sustainable research.

1. Moving beyond the screen: How voice is changing the lab

Scientists spend significant time working with software, entering information and completing digital documentation. As laboratory workflows become more digital, these interactions increasingly become part of the work itself. Voice interaction offers an alternative to constant keyboard and screen use. Scientists interact with digital systems through spoken commands, reducing manual software interaction and administrative work.

Augmented reality is also being explored in laboratory environments. Its role is to bring digital information closer to the physical work being performed, rather than requiring scientists to move repeatedly between laboratory activity and separate software interfaces.

The move from paper to digital documentation provides another example. For instance, Laboperator digitises paper documentation. Sustainability is a core part of the Lab Execution System (LES) software offering. By replacing paper-based documentation with digital workflows, the solution reduces paper consumption while supporting more efficient laboratory operations. These changes affect how scientists work with laboratory technology. The interface becomes part of the laboratory workflow rather than a separate administrative layer around the experiment.

2. Turning research history into a source of discovery with AI

Pharmaceutical companies have spent years digitising laboratory processes and accumulating experimental data. Years or decades of research therefore exist within company systems, creating a substantial historical record of experiments and results. Artificial intelligence provides a way to analyse that record. AI systems recognise relationships and patterns across large datasets and search historical information for similar experiments and results.

That changes the value of previous research. Experimental records are not only documentation of completed work. They provide information that researchers can use when evaluating new experiments and deciding what to investigate next.

The infrastructure used for this analysis also matters. Larger pharmaceutical companies are developing and operating internal AI models locally, keeping research data within their own infrastructure. The World Economic Forum highlights usable, representative data, trustworthy AI and interoperability as important enablers of AI adoption in healthcare. The need for usable research data becomes particularly important when considering drugdevelopment outcomes. A review in Nature Reviews Drug Discovery identified a lack of predictive preclinical models as one of the key reasons for high attrition in oncology drug development.

Researchers are also examining AI protein-folding research and quantum computing alongside classical AI. These approaches add new computational methods to pharmaceutical research, while scientists remain responsible for evaluating the evidence produced by those systems.

3. Redefining the scientist’s role in an automated lab

Automation is changing how work is distributed inside the laboratory. Scientists continue to define objectives, plan experiments, understand previous research and determine the next steps. Automated systems perform more repetitive execution, while researchers superviseprocesses, interpret outputs and make decisions. The change resembles developments in software development, where automation handles parts of repetitive execution while people remain responsible for defining requirements and reviewing results.

In pharmaceutical laboratories, this transition is progressive rather than a move directly to fully autonomous research. A fully self-improving system remains further on the horizon. The more immediate change is the scientist’s relationship with laboratory execution. Researchers spend less time performing repetitive actions and more time interpreting results and deciding how those results affect the next stage of the research process.

This shift also increases the importance of digital skills. Scientists need to understand how laboratory technologies, instruments, data systems and software platforms interact because research increasingly depends on those systems workingtogether.

4. Building a connected lab on open standards

Automation and AI depend on data moving between laboratory systems. That is difficult when laboratories use equipment from dozens of manufacturers, each with different data formats and technical ecosystems. Open standards address this problem by providing common methods for communication between equipment and software. OPC UA LADS and SiLA are relevant standards with different purposes. Both facilitate communication between laboratory equipment and software. A balance measurement, for example, can be retrieved automatically by another system rather than transferred manually by a scientist.

Interoperability also affects equipment investment decisions. Laboratory instruments often remain in use for years or decades, so a connected laboratory needs to accommodate equipment purchased at different points in time. Decades-old instruments are already being integrated into modern laboratory systems. Open communication standards therefore support the continued use of existing equipment while allowing laboratories to add newer systems. The issue is also directly connected to data quality. When information moves reliably between instruments and software, researchers have a more consistent basis for analysing experimental results and reusing laboratory data.

5. Bringing precision and sustainability into the research workflow

Precision medicine is increasing the amount and variety of information involved in pharmaceutical research. The Personalized Medicine Coalition reported that personalised medicines accounted for 38% of new drug approvals by the US Food and Drug Administration (FDA) in 2023. As personalised approaches develop, experimental information also needs to be reused across different applications. A chemical reaction associated with one drug, for example, may need to be recorded multiple times when developingpersonalised versions.

That makes structured and accessible research data increasingly important. Information captured during one experiment needs to remain available when researchers return to it for another application. Laboratory sustainability introduces another operational requirement. Laboratories generate waste through consumables and other materials, creating a need to distinguish necessary consumption from avoidable use.

Any change to laboratory processes also takes place within extensive regulations and standards. Requirements reviewed by regulators and independent organisations therefore remain part of decisions about how laboratory workflows arechanged. Animal testing is another area under examination. Animal testing has historically played an important role in medicine and vaccine development, while organ-on-a-chip and lab-on-a-chip technologies are being developed to reduce reliance on some forms of animal testing. Further development remains necessary. Precision medicine and sustainability therefore both increase the importance of how laboratories capture, manage and reuse information while controlling the resources required to conduct research.

Connecting the laboratory workflow

These five trends address different parts of pharmaceutical research, but they meet in the laboratory workflow. Voice interaction changes how scientists interact with digital systems. AI changes how historical research data is analysed. Automation changes how laboratory tasks are divided between people and machines. Interoperability determineswhether instruments and software exchange information reliably. Precision medicine and sustainability increase the requirements for reusable data and efficient laboratory processes.

For pharmaceutical laboratories, the practical question is how these capabilities work together in daily research. Scientists need systems that reduce repetitive data handling, connect equipment and software, preserve experimental informationand support decisions based on research results.

Turning these principles into practice requires a more automated approach to laboratory operations. Laboperator connects laboratory equipment, workflows and data within a digital laboratory environment, helping organisations build more integrated and scalable research operations. Explore Laboperator to see how automated laboratory infrastructure can support more efficient pharmaceutical research: https://laboperator.com/contact

AI compliance by design: how pharma and research labs are moving to review by exception

Jeroen de Haas, CPO — Tue, 18 Aug 2026 08:44:37 GMT

Compliance is moving from paperwork to proof of execution. Pharmaceutical compliance frameworks rely on documented evidence. Laboratories record scientific activities, review generated records, and provide regulators with evidence that approved processes have been followed. Documentation serves as a primary mechanism for demonstrating process adherence and maintaining quality control. That model is increasingly being challenged. At the same time, pharmaceutical companies are investing heavily in digitalisation and artificial intelligence. The two developments are more closely connected than they may appear: the systems that make compliance more reliable can also create the structured data and consistent processes that AI requires.

As scientific processes become more complex, regulators and industry leaders are moving towards a different idea: compliance should be demonstrated not only by what organisations record, but by how work is carried out. This approach, known as “review by exception”, shifts the focus from checking every activity retrospectively to identifying and addressing deviations from the expected process. The aim is for systems to make the correct action the natural outcome, support compliance by design and minimise routine manual review. Instead, Quality Assurance (QA) can focus its attention on exceptions that require investigation or intervention. The future of compliance is therefore less about reconstructing events after they happen and more about designing systems in which compliant behaviour is the natural outcome.

This is a difficult transition. Regulation has always evolved carefully, and rightly so. Patient safety cannot be compromised by rapid change. But the slow evolution of standards and regulatory expectations can create an unintended burden: organisations often build new digital capabilities on top of old processes, creating a mixture of modern technology and outdated workflows.

Deloitte’s 2025 Global Life Sciences Outlook highlights the scale of this transformation. Their research surveyed 150 C-suite executives across pharmaceutical, biotechnology and medical technology companies in North America, Europe, and Asia. It found that 68% of executives expected revenue growth, and 57% expected margin expansion. Around 60% identified digital transformation or generative artificial intelligence as one of the most significant trends shaping their businesses, while nearly 60% planned to increase investment in generative AI across the value chain.

Deloitte estimates that AI investments could generate up to 11% of revenue value across functional areas over the next five years. The challenge is whether organisations are building the foundations needed to achieve it. Artificial intelligence receives much of the attention, but AI does not create value from incomplete processes. It depends on structured data, reliable workflows and consistentexecution. Many organisations are investing in advanced technologies while still relying on fragmented systems, manual transfers and inconsistent laboratory practices.

In regulated laboratory environments, those foundations begin with compliance and data integrity; in non-GxP environments, the emphasis can be more heavily weighted towards flexibility and experimentation. The question is therefore not simply whether companies are adopting AI. It is whether their laboratories are ready for it.

Digital standardisation is becoming a strategic capability

Digital standardisation is becoming less a question of technical efficiency than of how organisations create a common foundation across laboratories, systems and sites. For pharmaceutical companies operating across multiple sites, countries and scientific disciplines, standardisation affects far more than operational efficiency. It influencesprocurement decisions, system integration, maintenance strategies and the ability to scale innovation. A laboratory that uses standardised instruments, connected systems and consistent workflows can adopt new technologies more effectively. A laboratory built around isolatedsolutions creates long-term complexity.

The balance is different across laboratory environments. In GxP settings, standardisation and controlled execution are central to demonstrating compliance. In non-GxP research environments, scientists may require greater flexibility to adapt methods and workflows. Digitalisation therefore should not mean imposing identical controls everywhere, butestablishing the appropriate level of standardisation for each environment.

This is increasingly important as organisations evaluate new vendors and technologies. The question is no longer simply whether a system performs a specific task. It is whether it can connect with the wider digital ecosystem.

Compliance by design: making data integrity part of the process

Regulatory requirements place data integrity at the centre of laboratory processes, with organisations expected to maintain accurate, reliable, and traceable records. The FDA’s guidance on Data Integrity and Compliance with Drug CGMP reinforces the importance of ALCOA+ principles: data must be attributable, legible, contemporaneous, original and accurate, alongside being complete, consistent, enduring and available. The implication is significant: data integrity cannot rely solely on final review. It is maintained through controls embedded within routine laboratory operations.

Digital laboratory systems capture information that records who performed an action, what was changed, when the change occurred, why it was made, and where the information originated. Data integrity frameworks use traceability to document the history of data changes. When a scientist changes a value, the system records the reason for the change, the original value, and the updated value. This is not simply a regulatory requirement. It is the foundation of trust. In GxP environments, that trust is essential to demonstrating compliance. In digital laboratories more broadly, the same traceability can also make data more useful for analysis and automation. Organisations continue to implement this model. Electronic signatures, audit trails, and connected systems are in use, but their implementation varies across laboratory environments and may not always provide a complete compliance framework.

The industry is moving towards a future where systems themselves provide evidence of correct execution, rather than requiring organisations to reconstruct evidence afterwards. Standards such as LADS (Laboratory and Analytical Device Standard), based on OPC UA, and SiLA 2, developed by the SiLA Consortium, are helping to address this challenge. By providing common ways for instruments and software to communicate, they reduce the reliance on bespoke connections between systems. For laboratories, digitalisation is therefore not simply about connecting equipment; it is about creating the infrastructure needed to make quality and compliance part of everyday operations; and to make the resulting data more usable.

The challenge of global consistency

Pharmaceutical organisations may operate laboratories across multiple locations and jurisdictions. Consistent scientific processes across these environments depend on aligned procedures, systems, and data practices. Local requirements, established practices and regional preferences can make harmonisation difficult. A process that works well in one location may not immediately translate to another. The solution is not to remove local expertise. Instead, organisations need to identify which elements must be standardised and which areas require flexibility. The strongest models combine global standards with local execution knowledge, rather than treating standardisation as uniformity.

Another challenge is communication. Laboratories may use different terminology and working methods across locations. Differences between scientific and corporate language, together with variations in documentation practices, can influence how information is interpreted. As pharmaceutical companies become more international, standardisation becomes a tool not only for efficiency, but for reducing misunderstanding.

Compliance as a driver of performance

Compliance and scientific work address different aspects of laboratory operations. Scientists need processes that support reliable results and, particularly in non-GxP research, allow experimentation. Compliance functions need processes that provide control, traceability and evidence of adherence to approved procedures. But the future of compliance should be different. When designed correctly, compliance creates structured information that can be reused, analysed and improved. It provides a foundation for operational excellence rather thanacting as a restriction. The question is not whether compliance creates additional work. The question is whether organisations design processes in a way that removes unnecessary work.

A well-designed digital workflow should make the compliant path the easiest path.

ISPE’s Pharma 4.0 maturity model reflects this idea. The framework describes a progression from basic digitalisation towards fully integrated intelligent operations. It argues that organisations cannot achieve advanced automation, artificial intelligence, or autonomous operations without first establishing standardised processes, digital workflows, reliable data, and connected systems. This raises an uncomfortable question for many organisations: are they attempting to implement AI before they have solved the fundamentals?

AI needs a foundation of standardised data

Artificial intelligence is attracting enormous attention across life sciences. The promise is substantial: faster research, improved decision-making and more efficient operations, But AI is only as valuable as the data it receives. A single data point without context has limited meaning. A measurement of five grams may appear precise, but without knowing the instrument, conditions, operator, method and purpose, it has limited meaning. AI requires context.

Organisations face challenges when historical data is created through inconsistent processes, varying terminology, and disconnected systems. The information may exist, but its structure can limit analysis and interpretation. Companies that want to benefit from AI must begin by improving how data is created today. Building the right foundations now is far easier than attempting to reverse-engineer decades of inconsistent information later.

The next generation of regulatory expectations

Digitally mature laboratories are expected to operate within regulatory frameworks that continue to build on existing principles rather than introduce entirely new expectations. What will change is how those expectations are achieved. Digital maturity will increasingly give organisations new ways to demonstrate compliance through connected systems rather than relying primarily on extensive manual documentation. Instead of requesting printed records and reconstructed evidence, they will expect access to reliable digital processes that provide transparency by design.

The bigger question will be the role of artificial intelligence itself. How are AI systems validated when they continuously evolve? How do organisations demonstrate that AI-driven decisions remain reliable and explainable? How will regulators evaluate technologies that do not always produce identical outputs?

These questions remain difficult. But the direction is becoming clearer. The laboratory of the future will not be defined by how much documentation it creates. It will be defined by how effectively its digital systemscreate trust. Digital standardisation is therefore not simply an efficiency project. It is becoming the foundation for compliance, productivity and scientific innovation. Learn more: https://laboperator.com/contact

The hidden complexity of modern laboratories

Jeroen de Haas, CPO — Mon, 03 Aug 2026 10:08:07 GMT

When workarounds become infrastructure: Laboratories are judged by what they produce: successful experiments, reliable data, and regulatory compliance. Less attention is given to how those outcomes are achieved. Yet beneath the surface of many apparently well-run laboratories lies an operating model shaped by years of incremental compromise.

A spreadsheet is created because two systems cannot exchange data. A scientist keeps a personal record of protocol changes because the official version is difficult to access. Instrument results are copied manually into another application because integration has been postponed. Each decision is rational at the point it is made. None appears significant in isolation.

Together, these workarounds become part of the laboratory’s infrastructure.

The result is an organisation that continues to perform, but whose performance depends on the experience, diligence and memory of its people rather than the resilience of its systems. That distinction matters as laboratories generate larger volumes of data, face greater regulatory scrutiny, and seek to improve productivity without proportionately expanding their workforce.

The wider life sciences industry faces pressure to improve the efficiency and value of research operations. The Springer Nature Digital Transformation of R&D report highlights that the average cost of developing a new drug has reached $2.8bn, while only 12% of drugs entering clinical trials are ultimately approved. These pressures are driving organisations to reconsider how research processes are designed, how data are managed, and how technology can support efficient and reproducible workflows.

The gradual accumulation of complexity

Operational complexity does not usually result from a single transformation project. It develops as laboratories expand, adopt new instruments, introduce additional software, and respond to changing scientific and regulatory requirements.

Organisations build on existing digital environments rather than redesigning them from first principles. New technologies are added to existing systems, creating layers of infrastructure that reflect years of practical decision-making rather than a coherent long-term architecture.

This evolutionary approach is understandable. Laboratories cannot suspend research while systems are rebuilt, nor can they replace every application whenever new technology becomes available. The consequence is that many laboratories operate in environments where information moves less efficiently than science itself.

Scientists compensate for these limitations. They know which spreadsheet contains the latest calculations, which instrument requires manual intervention, and which colleague understands a process that was never formally documented. The laboratory continues to function because individuals bridge gaps between disconnected systems. This adaptability can be mistaken for operational strength. It can conceal structural weakness.

The costs that remain largely invisible

Unlike the purchase of a new instrument or software licence, the costs associated with fragmented workflows do not appear as a single budget line. They are distributed across routine activities performed across the organisation.

Manual transcription is an example. Studies of scientific and clinical environments report data-entry error rates of between one and four per cent. While some mistakes are identified before causing harm, others lead to repeated experiments, delayed investigations or unnecessary deviation reports. The time required to resolve these issues can exceed the time required to create them.

Documentation presents another challenge. The Federal Demonstration Partnership (FDP) Faculty Burden Survey, published in Research Management Review in 2009, remains an important reference because it is one of the largest quantitative studies examining the administrative burden experienced by researchers. The survey found that 42% of the time spent by principal investigators on federally funded research projects was devoted to administrative activities rather than research. Based on responses from 6,081 faculty members across 73 research institutions, the study showed that administrative burden results from the cumulative effect of multiple individual requirements rather than a single process. The finding highlights a long-standing challenge for research organisations: ensuring that essential documentation and compliance activities are supported by efficient systems rather than excessive manual effort.

These hidden costs extend beyond productivity. Every manual step introduces another opportunity for inconsistency, delay, or uncertainty. Individually, such inefficiencies may appear manageable. Collectively, they influence the pace, quality, and reproducibility of scientific work.

Designing compliance into workflows

Regulated laboratories are designed to produce consistent outcomes. Consistency becomes difficult to maintain when critical processes depend on individual behaviour.

A revision to a standard operating procedure illustrates how compliance can depend on system design. The updated document is distributed by email and uploaded to a shared repository. Printed copies and locally stored files can remain in use after the revised version is issued. During an audit, establishing which procedure was followed for a particular experiment can require extensive investigation.

The issue is not negligence. It reflects systems that require people to remember information that technology could manage automatically. The same principle applies across regulated workflows. Compliance is strongest when correct behaviour is embedded within daily processes rather than dependent on individual vigilance. Systems should reduce opportunities for error, not rely on staff avoiding them.

Measuring execution, not simply outcomes

Laboratories devote considerable attention to measuring scientific results. Less attention is given to measuring how those results are produced.

If experimental data are scattered across multiple applications, spreadsheets, and paper records, identifying inefficiencies becomes difficult. Managers may know that productivity needs to improve but lack the operational visibility required to understand where delays occur or why deviations arise.

Without reliable execution data, process improvement becomes anecdotal. Decisions are guided by experience and intuition rather than objective evidence. The challenge is becoming more significant as scientific workflows grow more complex. Springer Nature’s Digital Transformation of R&D report identifies increasing research complexity as a driver of digital transformation in life sciences, highlighting the need to improve efficiency, reproducibility, collaboration and the management of scientific information.

This has implications beyond operational efficiency. Automation and artificial intelligence require structured, consistent, and accessible data. Disconnected workflows reduce productivity and limit the value of digital initiatives.

The economic impact of poor data infrastructure is significant. The Springer Nature Digital Transformation of R&D report highlights that, according to estimates from the European Commission and PwC, the lack of FAIR (Findable, Accessible, Interoperable and Reusable) research data could cost the European economy up to €26bn annually. The figure illustrates that effective data management is not simply a technical consideration; it is an operational and economic priority for research organisations.

Digital maturity therefore depends not only on acquiring new technologies but also on understanding how work is performed across the laboratory.

Designing systems that support science

Laboratory modernisation centres on new software and equipment, but the challenge is integrating existing capabilities into coherent operational systems.

Traditional laboratory software captures important information, but often after work has been completed. An Electronic Laboratory Notebook (ELN) records experimental outcomes. A Laboratory Information Management System (LIMS) manages samples and associated data. Both play important roles, yet neither necessarily guides scientists through laboratory procedures as work is performed. Laboratories are recognising the value of execution platforms that support scientists in real time, ensuring that protocols, data capture and compliance are integrated into workflows rather than treated as activities completed afterwards.

A laboratory may possess sophisticated analytical instruments, a LIMS, an ELN and numerous specialist applications. If these technologies operate independently, scientists continue to spend time transferring information rather than interpreting it.

Effective digital infrastructure reduces these interruptions. Data are captured once and made available wherever required. Scientists follow current procedures without searching for documentation. Routine administrative activities become part of the workflow rather than separate tasks completed afterwards, while audit trails and execution records are created automatically as work progresses.

The objective is not to replace scientists with software. It is to remove repetitive administrative tasks that consume their time, allowing them to focus on scientific thinking, experimentation, and discovery.

Building resilience rather than simply efficiency

Laboratories do not become vulnerable through a single flawed decision. They accumulate risk through practical compromises that appear reasonable individually but weaken systems collectively.

Strengthening infrastructure does not require replacing every existing system. The priority is to connect workflows, reduce manual processes, and improve the movement of information between existing applications.

The challenge for laboratory leaders is not simply selecting new technology but ensuring that the underlying operating model can support more complex scientific work. Organisations with connected workflows, reliable data management, and robust processes are better placed to meet future demands. Their advantage lies not only in efficiency, but in resilience.

Scientific excellence depends not only on researchers and technology, but on systems that allow both to perform consistently. The strongest laboratories are those where reliable processes enable scientists to focus on discovery rather than compensate for operational limitations. If your laboratory is ready to reduce operational complexity and build more connected workflows, contact Laboperator to discuss the next step. https://laboperator.com/contact

The missing layer in laboratory transformation

Tim Aulike, Head of Sales & Partnerships — Mon, 13 Jul 2026 16:29:13 GMT

However, one part of the laboratory has often remained surprisingly manual.

The question for many laboratories is no longer whether to digitise. It is how to connect every part of the laboratory into one efficient and reliable digital environment.

That is where a Lab Execution System (LES) plays a critical role.

Data is not the challenge

Alongside these systems sits another essential part of the laboratory: the instruments that generate scientific data.

From balances and chromatography systems to spectrometers and PCR instruments, laboratories rely on equipment from multiple manufacturers. Each instrument often uses its own communicationprotocol and data format, making it difficult to exchange information automatically.

How the modern laboratory comes together

A laboratory is made up of several connected layers, each with a different purpose.

The information layer manages samples, experiments, documentation and reporting through systems such as LIMS and ELNs. These applications manage samples, experiments, documentation and reporting. The instrument layer includes the laboratory equipment that generates analytical and experimental data.

Between these sits the operational layer, where scientists carry out laboratory procedures, prepare samples, operate instruments and generate results.

This operational layer is where a Lab Execution System fits.

More than SOPs

This removes the need to manually record results before entering them into anothersystem.

The outcome is a more consistent laboratory process, improved data quality and complete traceability from start to finish.

Instead of asking scientists to adapt to disconnected systems, an LES brings systems together around the scientist.

Why connectivity matters more than ever

Digital transformation is delivering measurable benefits across laboratories, but only when systems are connected.

As laboratories continue to digitise, the ability of instruments and software to communicate has become just as important as the technologies themselves.

Yet connecting laboratory instruments and software remains a persistent challenge. Equipment is often supplied by different vendors, each using proprietary interfaces and data formats. Without commonstandards, integrating new devices can be slow, costly and difficult to scale.

These advances are about more than introducing new software. They depend on creating connected workflows in which data moves seamlessly between instruments, scientists and digital systems. As laboratories embrace automation and AI, these foundations will become increasingly important for improving efficiency, strengthening data integrity and enabling more flexible, scalable laboratory operations.

Without connectivity, laboratories continue to rely on manual processes that slow productivity and increase operational risk.

LES, LIMS and ELN each have a different purpose

One of the most common misconceptions is that a Lab Execution System replaces a LIMS or an ELN.

In reality, these systems work together.

A LIMS focuses on managing samples, tracking their progress through the laboratory and storing analytical results.

An ELN focuses on documenting experiments, recording scientific observations and supporting collaboration.

Each system solves a different challenge, but together, these systems create a connected laboratory where information flows seamlessly between people, instruments and software.

Building a connected laboratory without replacing existing systems

The first step is understanding how existing systems communicate and identifying any gaps in connectivity.

A modern Lab Execution System can integrate existing laboratory instruments with LIMS, ELNs and other digital platforms, allowing laboratories to modernise while protecting previous technologyinvestments.

This approach reduces disruption, supports future growth and creates a scalable foundation for automation, artificial intelligence and advanced data analytics.

Modernisation is not about replacing what already works. It is about connecting it.

Turning strategy into execution

The laboratories that will benefit most from digital transformation are not necessarily those with the newest technology, but those whose systems work together.

A Lab Execution System provides the missing operational layer at the bench, connecting scientists, laboratory processes, and instruments to enable more efficient and data driven workflows. As laboratories continue to invest in automation, artificial intelligence and digital transformation, this connected approach will become essential for improving efficiency, ensuring data integrity and supporting regulatory compliance.

Whether you are taking the first steps towards laboratory digital transformation or looking to optimise your existing laboratory infrastructure, our specialists are here to help. Get in touch with the Laboperator team to discover how we can help transform your laboratory into a connected, future ready digital environment: https://laboperator.com/contact

What a yacht meme reveals about laboratories

Jeroen de Haas, CPO — Tue, 05 May 2026 11:00:00 GMT

Digital transformation has given laboratories unprecedented technological capability. Despite this progress, many organisations continue to underestimate the investment required to turn disconnected systems into connected science.

A recent LinkedIn meme makes a serious point through an absurd comparison. A luxury yacht represents the Marketing Plan, a household iron the Marketing Budget and a cruise ship the Expected ROI. The mismatch is immediately recognisable. Few organisations struggle to articulate ambition; many prove less willing to invest proportionately in achieving it. The same pattern can be observed in laboratories.

Reimagined through the lens of scientific research, the metaphor is equally compelling. The yacht becomes a fully connected laboratory, where instruments exchange data seamlessly, softwareplatforms operate as a unified ecosystem and artificial intelligence augments scientific decision-making. The iron represents a more familiar reality: isolated technology investments, fragmentedsoftware and limited funding for the integration required to connect them. The cruise ship remains unchanged, symbolising expectations of faster discovery, greater productivity and AI-enabledresearch.

The comparison is humorous. Its implications are not. It exposes a structural imbalance that continues to shape laboratory digital transformation.

Vision has never been the problem

Laboratories today are not short of ambition. Across research, pharmaceutical development and quality control, organisations have embraced a vision of increasingly connected and intelligent scientific environments. Digital workflows, interoperable data, laboratory automation and AI-assisted experimentation have become widely accepted strategic objectives rather than distantaspirations.

The technologies required to support that vision already exist. Electronic Laboratory Notebooks (ELNs), Laboratory Information Management Systems (LIMS), cloud-based platforms, connectedlaboratory instruments and emerging AI assistants have all reached a level of maturity that makes the connected laboratory technically achievable.

Yet digital capability should not be confused with digital cohesion.

According to Deloitte's Future-Proofing Pharma R&D Labs, fragmented data environments, ageing infrastructure and limited interoperability remain among the principal barriers to research productivity. The report argues that future competitiveness will depend less on acquiring additional technologies than on creating integrated digital ecosystems that enable collaboration, automation and AI-ready research. The challenge, therefore, is no longer technological. It is organisational.

The hidden cost of fragmented investment

The second panel of the meme, the household iron, captures a familiar pattern. Investment often focuses on acquiring individual technologies rather than connecting them into coherent workflows.

Laboratories purchase sophisticated analytical instruments while postponing integration projects. New software is introduced alongside existing systems, creating parallel rather than connectedenvironments. Change management, workflow redesign and interoperability are frequently treated as secondary considerations, despite being fundamental to successful digital transformation.

The consequence is what might be described as pseudo-digitalisation. Laboratories possess digital tools, yet many scientific processes remain surprisingly manual. Data are captured but not easily shared. Information is stored but seldom flows seamlessly between applications. Scientists continue to spend valuable time transferring, verifying and reconciling data instead of interpretingit.

Buying technology is relatively straightforward. Building an environment in which technologies communicate reliably, consistently and securely is considerably more demanding. That distinction often determines whether digital transformation delivers meaningful operational change or simply digitises existing inefficiencies.

Why AI raises the stakes

The final panel of the meme depicts an impressive destination: autonomous laboratories, real-time analytics, intelligent decision support and dramatically faster scientific discovery. These expectations are not unrealistic.

Artificial intelligence has genuine potential to improve reproducibility, accelerate research and reduce administrative burden. However, AI is fundamentally dependent on the quality, accessibilityand consistency of laboratory data. Disconnected systems inevitably produce fragmented intelligence.

McKinsey's The State of AI finds that organisations deriving the greatest value from artificial intelligence are distinguished not simply by their use of AI, but by the quality of their underlying data architecture, governance and operating models. In other words, organisational readiness matters at least as much as technological capability. For laboratories, the implication is straightforward. Artificial intelligence cannot compensate for disconnected workflows. It simply reveals them more quickly.

The gap lies between strategy and execution

The yacht meme ultimately illustrates a broader organisational challenge: misalignment between strategic ambition, investment priorities and expected outcomes.

Many laboratories continue to pursue transformational objectives while allocating incremental resources to the infrastructure needed to achieve them. Integration remains a project rather than a strategic capability. Workflow orchestration is often overlooked in favour of software procurement. Data standards are treated as technical considerations rather than business assets.

Yet it is precisely these less visible investments that determine whether digital technologies function as isolated tools or as part of an integrated scientific ecosystem.

The question laboratories should increasingly ask is not, Which platform should we implement next?

It is, How does information move from experiment to scientific insight?

That shift in perspective fundamentally changes the nature of digital transformation.

The shift from tools to ecosystems

The laboratories making the greatest progress are moving beyond individual technology decisions towards platform thinking. Rather than optimising isolated applications, they are designingconnected workflows in which instruments, software and scientists operate as part of a single digital environment.

This means investing in interoperability, application programming interfaces (APIs), common data standards, orchestration layers and workflow automation alongside laboratory software itself.

Equally important is the experience of the scientist. A connected laboratory should reduce administrative effort rather than increase it. Data capture should occur naturally within the workflow. Repetitive documentation should be automated wherever possible. Technology should support scientific work, not become another task to manage.

The Society for Laboratory Automation and Screening (SLAS) has consistently identified interoperability, workflow orchestration and standardised data exchange as critical enablers of next-generation laboratory automation. As laboratories become increasingly instrument-intensive and data-driven, connectivity is rapidly becoming as valuable as analytical capability itself.

Where ambition meets reality

The enduring appeal of the yacht meme lies in its simplicity. Organisations rarely fail because they lack ambition. Too often, they underestimate the investment required to turn ambition into operational reality. Laboratories are no exception.

Most already possess the expertise, technologies and scientific capability needed to build more connected ways of working. The remaining challenge is less visible, but ultimately moreconsequential: connecting technologies, redesigning workflows and investing in the digital infrastructure that allows information to move seamlessly from experiment to insight.

Those organisations that align strategic vision with investment in connectivity will be best positioned to realise the promise of laboratory automation and artificial intelligence. Those that continue to invest primarily in individual tools may find themselves wondering why the expected returns remain stubbornly out of reach.

The yacht meme may be humorous. Its lesson for modern laboratories is anything but. Building a connected laboratory is ultimately less about acquiring more technology than enabling existing technologies to work together. For organisations looking to turn digital ambition into operational reality, Laboperator helps design and deliver connected laboratory environments that support long-term scientific and operational success. https://laboperator.com/contact

When laboratory technology joins the team

Jeroen de Haas, CPO — Tue, 07 Apr 2026 12:10:10 GMT

Maybe you already saw our little friend at analytica in Munich (March 24-27). On our booth bench, surrounded by devices, it sat quietly—small, playful, almost toy-like at first glance. With its articulated legs, a friendly digital face, glowing WiFi signals, and connectivity ports on the front, it naturally drew attention.

But this little device is more than just charming design.

It represents something much bigger—a shift in how we think about laboratory technology, where connectivity, automation, and approachability come together to redefine the lab experience.

From Tools to Teammates

For decades, laboratories have been powered by highly sophisticated instruments — but supported by surprisingly fragmented digital systems.

Scientists still deal with:

Manual documentation
Context switching between ELN, LIMS, and instruments
Data copied from one system to another
Compliance requirements layered on top of workflows
Administrative overhead that steals time from science

We digitized notebooks.

We implemented systems.

We connected databases.

But we didn’t truly redesign the experience.

The result?

Digital tools that feel like obligations rather than collaborators.

The Symbolism Behind the Robot

That small, smiling lab robot captures what modern lab infrastructure should feel like.

The WiFi signals symbolize connectivity — instruments, systems, and workflows speaking to each other.
The Bluetooth icon represents seamless integration.
The ports on the front hint at interoperability.
The articulated legs reflect mobility across processes — from experiment planning to execution to documentation.
The friendly face stands for human-centered design.

This isn’t about building “another lab system.”

It’s about designing a workflow companion.

The Real Problem in Labs Today

The biggest challenge in modern laboratories isn’t a lack of digital systems.

It’s friction.

Friction between:

Instruments and documentation
Planning and execution
Data capture and compliance
Scientists and software

Every time a scientist stops an experiment to enter data manually, friction occurs.

Every time information must be retyped, friction occurs.

Every time compliance requirements interrupt focus, friction occurs.

And friction slows innovation.

The Next Evolution: Conversational, Connected, Contextual

The future of lab operations lies at the intersection of:

Workflow automation
Real-time data capture
Intelligent assistance
Native connectivity
AI-driven contextual support

Imagine infrastructure that:

Captures data at the moment it is created
Guides workflows step by step
Understands experimental context
Proactively surfaces relevant information
Ensures compliance automatically

Not a static ELN page.

Not a passive database.

But an active participant in the experiment.

What It Means for Scientific Organizations

When lab systems become collaborative rather than administrative, the impact is tangible:

Reduced manual errors
Increased reproducibility
Higher data integrity
Faster turnaround times
Better regulatory readiness
More time for scientists to focus on science

The organizations that embrace this shift will not just improve efficiency — they will accelerate discovery.

Designing Technology That Feels Human

There’s a deeper lesson in that smiling robotic assistant.

Enterprise software has trained us to expect complexity, friction, and steep learning curves.

But what if lab technology felt:

Approachable
Friendly
Intuitive
Supportive
Invisible when needed, powerful when required

Scientists work at the edge of human knowledge.

Their tools should empower them — not slow them down.

The Future Lab Assistant

The future lab assistant might not literally have eight legs and a smiling face.

But it should feel like one.

It should move with the experiment.

It should connect the ecosystem.

It should understand context.

It should remove administrative burden.

It should help scientists stay in flow.

That is the mindset we try to build with a modern workflow platforms like Laboperator — transforming static documentation systems into connected, guided, intelligent workflows.

The future of lab digitalization isn’t about adding more software.

It’s about building systems that behave like teammates.

And when that happens, science moves faster.

The execution gap in regulated laboratories

tim.aulike@laboperator.com (Tim Aulike) — Wed, 18 Mar 2026 11:30:19 GMT

Most GxP labs today operate ELNs, LIMS, and QMS. Documentation is digital, audit trails exist, and compliance frameworks are well established.

Yet during audits and daily lab work, one uncomfortable truth repeatedly surfaces:

Most GxP labs are digitally documented — but not digitally executed.

Between written procedures and generated results, a critical gap remains.
This is the Execution Gap.

Executive Summary

Regulated laboratories have invested heavily in digital systems such as ELN, LIMS, and QMS. Documentation is digital, audit trails exist, and compliance frameworks are well established.

However, the most critical part of laboratory work still happens outside these systems: workflow execution at the bench.

This gap between documented procedures and real-world execution creates hidden compliance risks, inefficiencies, and data integrity challenges.

In this article, we explore:

Why ELN and LIMS cannot ensure compliant workflow execution
Where manual processes introduce risk in GxP environments
What true digital workflow execution looks like
Why a dedicated execution layer is becoming essential for audit readiness, scalability, and AI-driven laboratories

Closing the execution gap is the next step in the digital transformation of regulated labs.

What is Changing in the Lab

Regulated laboratories are undergoing rapid transformation.
Data volumes are growing, regulatory expectations are increasing, and organizations are expected to deliver results faster while maintaining full compliance.

Several trends are reshaping daily lab work:

Digital lab ecosystems – ELN, LIMS and QMS are now widely adopted, replacing many paper-based processes.
Connected instruments – Labs operate more devices than ever, generating increasing amounts of data.
Stronger focus on data integrity – Regulators are placing greater scrutiny on traceability and audit readiness.
Global collaboration – Workflows must be standardized and scalable across sites.
AI and advanced analytics – New technologies require structured, contextual, and trustworthy data.

While documentation and data storage have become digital, workflow execution at the bench has not kept pace.

This shift is bringing the execution gap into focus.

The Real Problem: Digital Documentation Without Digital Execution

Despite major investments in digital lab systems, a critical part of laboratory work remains largely manual.

Most GxP labs today operate electronic systems for:

SOP management
Documentation and reporting
Sample and batch tracking
Quality and compliance processes

From a systems perspective, the lab appears highly digital.

But when scientists perform their daily work at the bench, the reality often looks very different.

They still rely on:

PDF or printed SOPs
Manual step execution
Local instrument software
Excel templates and calculators
Copy-paste data transfer between systems

This creates a disconnect between how work is documented and how work is actually performed.

Procedures are digital.
Results are digital.
But the execution in between is not.

This is the root cause of the Execution Gap in regulated laboratories.

Key Concepts

Before diving deeper, it is helpful to clarify three core concepts that shape digital laboratory environments.

01Digital Documentation

Systems such as ELN, LIMS, and QMS focus on storing, managing, and reviewing information.
They capture:

Experimental results
Sample and batch information
SOPs and quality documentation

These systems ensure traceability of what was documented.

02Workflow Execution

Workflow execution describes the actual performance of laboratory procedures at the bench.
This includes:

Step-by-step guidance for scientists
Interaction with laboratory instruments
Real-time capture of data and metadata

Execution focuses on how work is performed.

03The Execution Gap

The execution gap is the space between documented procedures and documented results.

When workflows are executed manually and documented afterward, risks arise:

Transcription errors
Missing metadata
Inconsistent execution
Complex audit preparation

Closing this gap requires bringing workflow execution into the digital ecosystem.

Introducing the Execution Layer

A modern lab operating model adds a dedicated execution layer between planning and documentation.

This layer connects:

Scientists
Instruments
Workflows
Enterprise lab (office) systems, e.g. ELN or LIMS

It ensures that workflows are:

Guided during execution
Connected to instruments
Captured in real time
Fully traceable by design

With this layer in place, the lab architecture becomes complete — linking SOPs, execution, and results into a continuous digital process.

How It Works: Step by Step Workflow Execution in a GxP Environment

A compliant execution model connects SOPs, instruments, scientists, and enterprise systems into one controlled flow. Here is what that looks like step by step:

Workflow is defined and approved

The procedure is created and approved in the QMS (e.g., SOP, method, or work instruction).
A validated workflow template is then made available for execution — including required steps, data fields, and checks.

Work is planned and assigned

Samples, batches, and work orders are managed in LIMS (or a planning system).
The workflow is linked to:

the correct sample IDs / batch context
required materials and instruments
roles and permissions (who can execute, review, approve)

Scientist starts execution at the bench

The user launches the workflow in the execution layer (LES).
Key controls are enforced automatically:

correct user authentication and role-based access
training status / authorization checks (if required)
version control (ensuring the approved workflow version is used)

Guided steps reduce variability

The workflow guides the scientist step-by-step.
Each step can require:

confirmations and checks
calibration and expiration checks
required fields
thresholds / pass-fail criteria
conditional branching (e.g., “if out-of-range → retest / deviation flow”)

This minimizes interpretation and ensures consistent execution.

Instruments are connected and data is captured automatically

Connected bench-top instruments stream results directly into the workflow record.
This typically includes:

measurement values
timestamps
instrument ID and configuration
relevant metadata

This eliminates transcription and strengthens data integrity.

Built-in audit trail is created during execution

Every action is logged automatically:

who did what
when it happened
what data was generated
which instrument was used
which workflow version was executed

Audit trails are generated as a by-product of work, not reconstructed afterward.

Exceptions follow controlled paths

If something goes wrong (out-of-spec values, missing data, instrument issues), the workflow can enforce:

repeat steps
escalation and approvals
deviation / investigation triggers
mandatory comments and attachments

This makes exceptions manageable and compliant — without relying on memory or side notes.

Review and approval becomes faster and more robust

Because data and context are already structured, review becomes significantly easier:

completeness checks are automatic
reviewers can see the full execution context
approvals are traceable and attributable

Results are handed over to ELN/LIMS and reporting systems

Final results and required metadata are transferred to:

ELN (documentation and scientific narrative)
LIMS (sample lifecycle, batch record, status updates)
analytics/data platforms (dashboards, trending, AI)

This closes the loop from SOP → execution → results — with full traceability.

Compliance, Risk, and Business Value

Closing the execution gap is not only a technology improvement — it directly impacts compliance, operational risk, and laboratory performance.

Stronger Data Integrity by Design

In manual environments, compliance is often achieved through training, documentation, and retrospective checks.
With digital workflow execution, compliance becomes part of the process itself.

Key improvements include:

Automatic capture of timestamps, users, and instrument data
Elimination of manual transcription and copy-paste steps
Enforced use of approved workflow versions
Consistent execution across users and sites

Instead of reconstructing the story of an experiment during an audit, the full execution history already exists.

Reduced Compliance Risk

Many common GxP findings originate from the same root causes:

Incomplete records
Missing metadata
Inconsistent execution
Uncontrolled spreadsheets
Unclear audit trails

A digital execution layer addresses these risks at their source.

It ensures that:

Required fields cannot be skipped
Deviations follow controlled processes
Data is captured contemporaneously
Audit trails are generated automatically

This shifts compliance from reactive to proactive.

Faster and Less Stressful Audits

Audit preparation often requires weeks of effort:

Collecting documentation
Reconstructing workflows
Explaining manual processes
Verifying data consistency across systems

With digital execution, much of this work disappears.

Auditors can see:

how workflows are executed
which controls are enforced
how data flows between systems
how traceability is ensured

Audit readiness becomes part of daily operations rather than a periodic project.

Efficiency Gains for Lab Teams

Scientists should focus on science — not data transcription and paperwork.

Digital workflow execution reduces:

manual data transfer
repetitive documentation work
time spent searching for missing information
rework caused by human error

This allows laboratory teams to:

increase throughput
improve consistency
focus on high-value activities

A Scalable Foundation for the Future

As labs grow, collaborate globally, and adopt AI-driven analytics, the need for structured and trustworthy data becomes critical.

A digital execution layer provides:

standardized workflows across sites
consistent data generation
reliable context and meaning for analytics and AI
a scalable foundation for future automation

Compliance, efficiency, and innovation no longer compete — they reinforce each other.

Conclusion: From Digital Documentation to Digital Execution

Regulated laboratories have made enormous progress in digitalization.
Documentation, quality processes, and sample management are now largely digital and well established.

But the most critical step — the execution of laboratory workflows — still often happens outside the digital ecosystem.

This execution gap introduces compliance risks, limits scalability, and consumes valuable scientific time with manual tasks.

The next phase of laboratory digital transformation is clear:
bringing workflow execution into the digital architecture.

By connecting SOPs, scientists, instruments, and enterprise systems in one continuous flow, laboratories can move from retrospective documentation toward real-time, audit-ready execution.

The result is a lab that is:

more compliant
more efficient
more scalable
better prepared for AI and advanced analytics

The future of GxP laboratories is not just digital documentation.
It is digital execution.

Close the Execution Gap in Your Lab

A Lab Execution System provides the missing execution layer that connects workflows, instruments, and enterprise lab systems into one controlled environment.

If you want to explore how audit-ready workflow execution can look in your organization, our team is happy to help.

Talk to an expert and discover how to close the execution gap in your laboratory.

Why the Laboratory Execution System (LES) Is Replacing the Electronic Lab Notebook (ELN)

jeroen.dehaas@laboperator.com (Jeroen, Chief Product Officer) — Mon, 16 Feb 2026 15:10:08 GMT

For decades, the Electronic Lab Notebook (ELN) has been the digital standard for laboratory documentation. Scientists document experiments, attach data files, and archive results in searchable databases. The shift from paper notebooks to ELNs was celebrated as a major step forward—and it was.

But as laboratories evolve toward automation, real-time analytics, and AI-driven workflows, a fundamental question is emerging:

Is documentation enough?

The answer is increasingly clear: No.

Modern laboratories need more than a digital record keeper—they need an execution engine. This is why forward-thinking labs are shifting from ELNs to Laboratory Execution Systems (LES).

From Passive Documentation to Active Orchestration

The difference between an ELN and an LES is not incremental—it’s architectural.

An ELN is retrospective. It captures what happened after the fact. A scientist runs an experiment, collects data from instruments, and then documents it in the ELN. The system is a repository—a structured way to store, search, and retrieve experimental records.

An LES is prospective. It guides, enforces, and orchestrates what happens in real-time. The system doesn’t just record the experiment—it executes it. It connects directly to instruments, enforces SOPs, captures metadata automatically, and triggers the next step in the workflow.

This shift mirrors what happened in manufacturing decades ago: the evolution from paper logbooks to Manufacturing Execution Systems (MES). MES didn’t just document production—it controlled it.

Laboratories are now following the same trajectory.

The Five Limitations of ELN-Centric Workflows

As labs push toward higher automation, stricter compliance, and AI readiness, the limitations of ELN-centric workflows become impossible to ignore.

1. Manual Data Transfer Creates Bottlenecks and Errors

In most ELN workflows, data still moves manually.

A scientist weighs a sample on a balance. The balance displays a reading. The scientist writes it down—or exports a CSV file to a USB stick—and later uploads it to the ELN, manually entering the sample ID, the method, and any relevant notes.

This process is slow, error-prone, and strips away critical context. Was the balance calibrated? What was the temperature in the lab? Who performed the measurement? These metadata points are often lost—or entered inconsistently.

2. No Real-Time Process Control

An ELN cannot trigger the next step in a workflow.

If a pH reading is out of range, the ELN cannot alert the operator or halt the process. If a sample needs to move from prep to analysis, the ELN cannot schedule the instrument or notify the next user.

Workflow logic exists in the scientist’s head—not in the system. This works for small, exploratory labs.
It breaks down at scale.

3. Limited Integration with Automation

Robotic systems, liquid handlers, and automated instruments often operate independently of the ELN.

Data reconciliation happens later—if at all. Multi-instrument workflows require human coordination. A scientist manually checks when the preparatory step is complete, then starts the analytical instrument, then uploads results separately.

The promise of lab automation is undermined by software that cannot keep up.

4. Weak Metadata Capture

Metadata is the connective tissue of the modern laboratory. Without it, data cannot be trusted, reused, or analyzed by AI.

ELNs were designed for human-readable notes—not machine-readable context. Environmental conditions, device states, calibration status, operator identity, and method parameters are often entered manually or omitted entirely.

The result: incomplete records, compliance gaps, and data that cannot power advanced analytics.

5. Difficult to Scale Across Global Operations

When labs rely on ELNs as their primary execution tool, each site develops its own usage patterns. Standardization requires extensive training, governance, and auditing.

Process variability across locations becomes the norm. Comparing results from different sites becomes difficult.
Regulatory audits expose inconsistencies.

For global organizations, this fragmentation is a strategic liability.

What an LES Brings to the Modern Laboratory

A Laboratory Execution System transforms how work gets done. It doesn’t replace the need for documentation—but it redefines where and how that documentation happens.

✓ Direct Device Connectivity

An LES connects directly to instruments—balances, liquid handlers, chromatographs, spectrometers, incubators—using standards like OPC UA LADS, SiLA, or proprietary drivers.

Data flows automatically from the device to the system. No USB sticks. No manual entry. No lost context.

The moment a measurement is taken, it’s captured—along with calibration status, device ID, timestamp, operator, and environmental parameters.

✓ Workflow Orchestration

An LES turns SOPs into executable workflows.

Method parameters are enforced at execution time. The system guides the user step-by-step. Conditional logic enables adaptive protocols: if pH < 6.5, trigger alert and halt; if weight > threshold, proceed to next step.

Multi-device workflows are coordinated automatically. The LES schedules instruments, manages queues, and ensures the right sample reaches the right device at the right time.

✓ Rich Metadata Capture by Design

Metadata is not an afterthought—it’s built into the fabric of the system.

Every action generates a structured record: who, what, when, where, why, and how. User attribution, timestamps, device states, calibration records, environmental logs—all captured automatically.

This metadata makes the data compliant, reproducible, and AI-ready.

✓ Real-Time Compliance and Quality Control

An LES enforces compliance at the point of execution—not during an audit six months later.

Critical process parameters are validated in real-time. Deviations trigger alerts. Non-compliant operations are prevented before they happen.

The system generates audit trails automatically, meeting ALCOA+, 21 CFR Part 11, and EU Annex 11 requirements without manual effort.

✓ System Integration Architecture

An LES is not an island. It’s designed to integrate.

APIs connect the LES to LIMS, ERP, QMS, and MES systems. Data flows bi-directionally. A sample registered in LIMS triggers a workflow in the LES. Results from the LES update quality records in the QMS.

Cloud-native or hybrid architectures enable distributed teams to work with the same standardized data, from anywhere.

✓ Analytics and AI Enablement

Structured, contextualized data is the foundation of AI.

An LES produces data that machine learning models can consume directly. Predictive maintenance becomes possible. Process optimization algorithms can run continuously. Digital twins can simulate experiments before they’re performed.

The lab becomes a learning system—one that improves itself over time.

When You Still Need an ELN (And When You Don’t)

The rise of the LES does not mean the death of the ELN. Understanding when each system makes sense is critical.

ELN Still Makes Sense For:

Exploratory research with unstructured, evolving workflows
Academic labs where protocol variability is high and automation is low
Documentation-heavy environments with minimal device integration needs
Early-stage R&D where flexibility matters more than standardization

LES Becomes Essential For:

GxP-regulated environments (pharma, biotech, food, medical devices)
High-throughput and automated workflows
Multi-site operations requiring standardization
AI/ML readiness initiatives
Process development and scale-up environments
Labs preparing for digital transformation

“

As a CPO, I see many labs trying to force-fit ELNs into execution roles they were never designed for. The result is workarounds, frustration, and missed opportunities for automation. An LES isn’t just a “better ELN”—it’s a fundamentally different category of software built for orchestration, not just documentation. The labs that understand this distinction are the ones pulling ahead.

The Hybrid Reality

Many labs run both: an ELN for unstructured research notes, and an LES for standardized execution.

Some modern LES platforms include ELN-like documentation capabilities, blurring the lines further.

The trend is clear: consolidation onto fewer, more integrated platforms that prioritize execution over passive documentation.

Real-World Impact: What Changes When You Deploy an LES

The difference is not theoretical. It’s measurable.

Before LES:

Scientist exports CSV from balance → uploads to ELN → manually enters sample ID, method, and notes
Transcription errors occur 5-10% of the time
Metadata (calibration status, environmental conditions) is missing or incomplete
Time per sample: 10-15 minutes
Compliance audit finds gaps in traceability

After LES:

Balance auto-transmits weight + calibration status + timestamp to LES
Sample ID pre-loaded from LIMS integration
Workflow validates reading against method parameters automatically
Complete audit trail generated without human intervention
Time per sample: <1 minute, zero manual entry

Quantifiable Benefits:

60-80% reduction in data entry time
95%+ reduction in transcription errors
100% metadata completeness
50% faster protocol execution cycles
70% reduction in compliance audit preparation time

These are not aspirational numbers. They’re being realized today in labs that have made the transition.

The Path Forward: Making the Transition

Moving from an ELN-centric model to an LES-driven lab requires planning, but the roadmap is well-established.

Step 1: Assess Your Current State

Identify high-value, high-frequency workflows where automation and standardization would have the greatest impact. Map device integration requirements. Evaluate compliance gaps and data quality issues.

Step 2: Start with a Pilot

Choose one automated workflow or critical process. Prove value before full-scale deployment. Learn what works, iterate quickly, and build organizational confidence.

Step 3: Build Your Integration Layer

Connect critical devices first: balances, liquid handlers, analytical instruments. Establish metadata standards. Ensure connectivity to LIMS, QMS, and other enterprise systems.

Step 4: Scale Systematically

Expand to additional workflows and devices. Standardize processes across sites. Train users on an execution-first mindset—where the system guides the work, not just documents it.

Step 5: Leverage the Data

Enable real-time analytics dashboards. Launch predictive models for maintenance and optimization. Pursue digital twin capabilities to simulate and improve processes.

Key Success Factors:

Executive sponsorship and change management
Cross-functional collaboration (IT, QA, R&D, Operations)
Vendor selection: prioritize open architecture, non-coding platforms, proven compliance, and strong support

The Future: Where Lab Software Is Headed

The boundaries between LES, LIMS, and MES are blurring.

Unified lab informatics platforms are emerging, like Laboperator, —cloud-first, AI-native, and designed for interoperability. These systems don’t just connect devices; they orchestrate entire lab ecosystems.

What this enables:

Self-optimizing laboratories that learn from every experiment
Autonomous experiment execution guided by AI
Real-time supply chain integration that adjusts protocols based on material availability
Predictive quality release that reduces cycle times
Fully digital, paperless operations from sample intake to final report

The question isn’t whether your lab will move toward execution-centric systems—it’s when.

The labs making this transition now are building competitive advantages in speed, quality, and innovation that will compound over years.

Conclusion

The Electronic Lab Notebook served laboratories well in the era of manual, documentation-first science. But as labs embrace automation, AI, and digital transformation, the limitations of passive documentation become impossible to ignore.

The Laboratory Execution System represents the next evolution: a platform that doesn’t just record what happened, but actively orchestrates it—with precision, compliance, and intelligence.

A number without context is just a number.

An experiment without orchestration is just a procedure.

A lab without execution software is just a room full of disconnected devices.

For laboratories ready to move from documenting science to executing it at scale, the shift from ELN to LES isn’t just an upgrade.

It’s a transformation.