Optimize Before You Automate: Why Process Excellence in the Laboratory Must Come First
The allure of laboratory automation is undeniable: robotics handling experiments, AI designing protocols, sensors, video and audio capture technologies self-documenting in real time, and integrated systems that record all data automatically. But organizations eager and bold enough to embrace this vision often make a crucial mistake — they automate first and optimize later.
This approach leads to compounded inefficiencies on a larger scale. In a Lab of the Future discussion late last year, one R&D leader said something that struck a chord: “You don’t automate a bad process — all you do is amplify inefficiency.” His statement supports a simple practice we apply to all lab modernization projects. Before talking platforms, we step back and ask three practical questions: Are the core processes standardized and documented? Is the data captured with enough context (FAIR principles + metadata) to be usable by analytics and AI? And are we clear on which steps truly benefit from automation — versus which ones should stay manual or be redesigned first? Failing to answer any of these questions results in automation that simply embeds existing problems into new software.
Here we break down where automation typically goes sideways in R&D environments — and the three foundational moves that prevent “fast chaos” and enable real scale.
Why Automation Can Amplify Problems
Automation is an accelerator — it makes processes faster, more consistent and scalable. If the underlying process is solid, automation can add significant value, saving time and resources while improving data quality. But when the process is flawed, automation turns inefficiencies into systematic obstacles.
Many laboratories today operate like artisan workshops, with each scientist bringing their own experience and preferences. For example, three researchers might design the same flow cytometry experiment in three different ways. This variability isn't necessarily bad for discovery, as creativity thrives on diverse perspectives, but it becomes a hindrance when trying to automate or use AI at scale. Robots will carry out inconsistent protocols perfectly, leading to inconsistent results. AI trained on fragmented data may generate recommendations that reflect confusion rather than clarity. In other words, automation can turn slow chaos into fast chaos.
Laying the Foundation: Processes, Data & Standards
Successful automation depends on a strong foundation in three areas:
- Standardize, Optimize & Document Laboratory Processes
The aim isn’t to limit scientific creativity, but to set common baselines for reproducible work. This allows scientists to focus their creativity where it matters most. Standardizing how data is recorded, how controls are run and how routine assays are performed doesn’t restrict insight — it helps avoid reinventing the wheel. The counterintuitive truth: standardization is an enabler of innovation, not a limiting factor. For example, clinical data standards like the Clinical Data Interchange Standards Consortium (CDISC) made multi-site trials feasible. Far from constraining progress, these standards created stable platforms, enabling rapid advancement.
The same applies to laboratory modernization. Standardizing flow cytometry templates, control execution and metadata capture doesn't limit insight — it frees scientists from reinventing the routine to focus on the novel. - FAIRify Data for AI Use
Process optimization isn’t complete without making data clear, consistent and fully in context. In many labs, data is scattered and described differently — “cell viability” in one place, “percent viable cells” in another or “passage 3 HeLa cells” versus “P3 HeLa.” Without shared standards and comprehensive metadata, it’s hard for both people and AI to compare results or automate tasks reliably.
Applying the FAIR principles — Findable, Accessible, Interoperable, Reusable — helps organize data so it’s easier to interpret, share and automate. This step reduces confusion and ensures that process improvements and automation are built on a solid, understandable foundation. When data is FAIR, it’s much simpler to automate routine reporting, track quality metrics or identify process bottlenecks. Consistent metadata, standard vocabularies and clear data dictionaries ensure that everyone — humans and machines alike — derives the same meaning. - Choose What to Semi-Automate
Manufacturing often succeeds with full automation because processes are primarily standardized and predictable. Discovery research is different — designs change, and volumes vary. It’s best to automate routine, high-volume tasks where human judgment isn’t essential, such as sample preparation for established assays, routine quality control and data transfer. Human expertise is still crucial for experimental design, interpretation and complex problem-solving.
A useful rule of thumb: automate what is frequent, consistent and low-judgment — and keep humans in the loop where variability and interpretation matter. This balance reduces friction without forcing discovery into a factory model.
Building Excellence Before Scaling Up
Many organizations are prioritizing both innovation and efficiency in their laboratory digitization efforts. In a 2025 Lab of the Future survey from the Pistoia Alliance, accelerating innovation (53%) and improving the efficiency of R&D (47%) were in the top three benefits to automating the lab as cited by respondents. But the same body of research highlights that the biggest blockers to automation are rarely the tools themselves — they’re data silos, inconsistent metadata and low-quality datasets. Technology alone can’t drive innovation and efficiencies: real progress comes from well-designed processes, standardized data and thoughtful automation strategies.
In order to fully realize the value of automation investments, it’s worthwhile to focus on optimization. Map out workflows, remove unnecessary steps and document appropriately. Document processes so they can serve as reliable guides for automation. Agree on standard protocols. Apply FAIR data principles with clear frameworks for metadata, ontologies and dictionaries.
The technology is ready. The question isn’t whether automation and AI can transform laboratories — it’s whether your processes and data are ready to be transformed. Process excellence and data FAIRification aren’t delays; they are the foundation required to realize the value of automation.
Learn how EPAM’s industry-seasoned cloud, data and AI practitioners can support you on your laboratory modernization journey.
