The pace of scientific discovery isn’t limited by ideas—it’s limited by execution. As research grows more complex across verticals, from multiomics to agrigenomics, fragmented systems, manual workflows, and unmanageable data volumes are slowing progress at a moment when speed matters most.
Modern scientific discovery holds incredible promise for real-world impact, but it’s far from a linear journey from hypothesis to result. In order to streamline workflows from end to end, labs need an integrated approach that brings together digital technologies and automation to enable faster, reproducible, and more scalable science.
Cumbersome processes can hinder discovery and scale up
Across pharmaceutical, biotechnology, and clinical research organizations, progress has long been throttled by unnecessary friction in everyday workflows. Take fragmented data, for example. Critical context has lived in data silos—on different instruments, written in notebooks, or inside bespoke databases—and often with inconsistent naming, identifiers, or metadata. This has made it nearly impossible to apply data across experiments, especially when they needed to scale. When scientists can’t quickly determine what changed across runs or why a result diverged, it also erodes confidence in the data. In practice, this lack of standardization makes it difficult to reproduce results or apply machine learning models across datasets, limiting the downstream value of the data itself.
Another bottleneck is disconnected workflows. When advanced technologies are isolated by manual handoffs, scientists are forced to spend the majority of their time doing administrative work, such as setup, sample tracking, documentation, and reconciliation, rather than experimentation and analysis. These inefficiencies can compound as workflows scale, particularly when transitioning from early-stage research to more complex development environments. When development and manufacturing programs inherit ad hoc processes or ambiguous metadata, teams may find themselves reworking foundational elements under tighter timelines.
For example, in bioanalytical labs supporting large molecule characterization, a single assay workflow spans cell culture and sample prep, purification, liquid chromatography-mass spectrometry (LC-MS) analysis, and downstream data processing. At each stage, data and metadata are transferred between systems, often manually, requiring scientists to verify run conditions. While each step may only take a minute, labs can see significant delays in high-throughput environments.
Collaboration across teams, both within organizations and across external partners, can be hindered by the lack of interoperability between systems. In practice, many talented teams are still spending significant effort on manual steps—pipetting, transcribing instrument outputs, reconciling sample IDs, and reformatting data for downstream analysis. As research becomes more collaborative and global and scientific tools become more powerful, these disconnects become increasingly limiting.
The role of digital technologies and automation in scientific workflows
Digital and automation strategies succeed when they address the reality of how science is performed. Science is iterative, data-intensive, and dependent on context. With connected, digital technologies and automation, this work can be amplified to reduce time lost to variability. In this model, digital systems and automation don’t replace scientists, they act as a co-pilot.
In a connected lab, instruments, software, and data systems are designed to create a coordinated environment. Scientists can seamlessly move from sample prep to data that’s immediately useable for analysis. In practice, labs reap the benefits of consistent sample integrity, automated capture of run conditions and metadata, real-time visibility into the process, and audit-ready traceability. All of these things reduce the administrative burden on scientists. Just as importantly, it creates a data foundation that can be reused across experiments rather than recreated each time.
Automation also plays a central role in eliminating busy work. Whether adopting robotics or using advanced tools with automated processes, labs can reduce repetitive tasks and standardize handoffs. This creates the opportunity for closed loop experimentation that leads to improved efficiency and accelerated discovery.
While small delays can add up quickly in higher throughput environments, such as bioanalytical labs or quality control (QC) testing, the value of automation becomes inseparable from compliance. These workflows must be both efficient and traceable. Automation makes way for digital audit trails, controlled access, and robust review-by-exception models that can shorten cycle times while strengthening oversight.
Adding AI to the mix
AI can be a powerful collaborator in scientific workflows. However, its usefulness is constrained by one reality: AI is only as reliable as the data foundation beneath it. This means that labs need connected data, provenance, metadata discipline, and governance—especially when outputs are guiding downstream development decisions in regulated environments.
Where AI can add immediate value is experiment design. Next-generation technologies can use integrated datasets to refine protocols, recommend next steps, and reduce the number of iterations required to reach a confident conclusion. In early applications, this has helped teams narrow experimental conditions and prioritize the most promising pathways without increasing experimental burden. With human-in-loop models, AI can accelerate reasoning and surface patterns and scientist retain ownership, interpretation, and accountability. When this approach bridges the gap between wet and dry lab workflows, labs gain tangible benefits in efficiency.
From installing tools to building ecosystems that enable breakthrough discoveries
In order for labs to see the next step change in productivity, lab managers need to make the move from simply installing new tools to integrating tools into an ecosystem where data, workflows, and scientists can connect without friction. This is why organizations increasingly need a trusted provider who can help them build out both capability and expand mindset. This is what will make digital orchestration realistic at scale.
Most organizations today clearly see the benefits of digital technologies and automation, but struggle with implementation. The first step is establishing process clarity and ownership. Lab leaders should define how work moves throughout the lifecycle and assign clear responsibility at each stage, whether that sits with internal scientists, informatics teams, or external partners. From there, cross-functional teams can map workflows in detail, identify where variability or manual effort introduces risk, and align on what success looks like. With this shared definition, organizations can more effectively determine which processes to automate and which require human oversight.
Equally important is transparency. If new workflows feel opaque or overly complex, adoption will stall. Leaders should approach implementation as a form of scientific change management—testing, iterating, and refining workflows with the same rigor applied to experimental design. Building in quality, compliance, and interoperability from the outset ensures that systems can scale beyond research and development into analytical and QC environments.
It’s clear that digital technologies and automation will be the infrastructure of modern discovery. However, the organizations that lead will be those that pair these capabilities with intentional changes in how science gets done. In any lab, scientists want to spend less time on mechanical or administrative work and more time doing what they do best: asking better questions, designing smarter experiments, and translating complex data into meaningful insights. Prioritizing the technological and human shift in tandem is how science will move faster, smarter, and further.
Albine Roy-Contancin is Senior Director of Strategy, Product Management and Marketing for the Digital Science and Automation Solutions business at Thermo Fisher Scientific. She is responsible for aligning product innovation with market and customer needs, advancing integrated digital and automation solutions that connect instruments, data, and workflows across the laboratory.
With over 20 years of senior management experience, she combines expertise in software with leadership across business, operations and finance to design solutions that empower.