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.
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.
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.
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.
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.
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.
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