A growing pattern in biopharma labs is that the technology stack is growing faster than its connecting foundational architecture. For example, a new instrument creates large volumes of imaging data that needs processing and storing; a new liquid-handling robot gets bolted on to an existing workflow; an AI-driven tool generates new insights that lab technicians manually transcribe into a separate system. Each individual new technology may be defensible, but the aggregate result is a lab that spends an enormous amount of energy managing the gaps between its own tools. This is compounding the long-standing challenges of siloed lab systems and dark data, and unfortunately for scientists, this means even more time reconciling data and chasing errors, and less time doing high-value scientific work.
The integration debt
It is well documented that when labs integrate laboratory information management systems (LIMSs), electronic medical records (EMRs) and electronic lab notebooks (ELNs) via standardized Application Programming Interfaces (APIs) and structured message formats, the downstream effects are measurable. For instance, manual data entry and paper handling are reduced, workflows get streamlined and there is faster, more reliable reporting. What gets less attention is the inverse: when those integrations are absent, every workflow improvement labs layer on top inherits the fragility of the foundation below.
Integration debt, like technical debt, has a compounding nature. Every new instrument, software or cloud service added to the lab landscape multiplies the number of potential failure points. This often creates the need for data reconciliation and operator interventions to maintain run integrity.
This is the context in which AI-driven decision support either delivers real value or becomes yet another layer of noise. AI tools that flag anomalies and the review-by-exception approach are genuinely powerful, but only if the data they’re operating on is trustworthy, traceable and arriving through reliable channels. “Garbage in, garbage out” is not a new principle, but it is newly consequential when the system informing human decisions is drawing from a compromised data foundation. Enabling the frictionless flow of data from source to insight generation is a cardinal requirement for the modern digital lab.
Cloud convergence becomes infrastructure
The convergence of wet lab automation with cloud and software platforms has moved well past proof-of-concept. Labs can now configure runs remotely, monitor execution status and review intermediate results in real-time. When experiments deviate from expected behavior, scientists can intervene earlier. This human-in-the-loop visibility is a fundamental change in the economics of experimental failure. Catching deviation after a couple of hours rather than two days has direct implications for throughput, reagent costs and timeline for results.
But realizing the benefits of integration requires a deliberate architectural commitment and, often, a strategic technology partner with deployment expertise to support labs from initial configuration to the full build out of an automated digital lab. Cloud-based automation platforms are not drop-in solutions. They require explicit decisions about data provenance, access controls, cross-site governance and precise handoff points between physical automation and digital execution. Before committing to any platform, lab managers should be able to answer if every run, result and decision that system made can be fully traced and trusted. If the answer is no, speed is not an advantage.
Better work, not fewer people
Most labs deploy automation to elevate what scientists do, not to reduce how many of them do it. The real promise of this technology is role transformation, where labs can reduce administrative overhead for scientists and move their time into higher-order work like analysis, experimental design and strategic interpretation. However, this transition requires deliberate investment, starting even before a single tool goes live. Lab managers should treat onboarding as foundational to future success. Scientists need to understand the why and the how, not just the what, and this requires effective training, communication and change management.
Scientists ultimately need more than just operational familiarity with a tool; they need the ability to interrogate it and explain it, and to understand what a reliable AI-generated recommendation looks like, and how the recommendation was derived, so they can recognize errors. That capability comes from building digital fluency as a genuine laboratory competency – as fundamental as pipetting technique or protocol compliance – and giving staff enough practice to trust their own judgment when challenging automated outputs.
What connected work looks like
In practice, a connected lab has a few defining characteristics: data moves seamlessly between instruments, software platforms and cloud environments through governed, auditable pathways; automation platforms are configured to communicate exceptions to lab staff in real-time, keeping workers focused on decisions that require judgment; scientists across sites collaborate on experimental design and review results from a shared data environment, without version control chaos or access gaps. These are achievable today for labs willing to do the hard architectural work that holds them together.
Labs must establish clear integration standards, define who owns data quality at each handoff and build the organizational discipline to maintain those standards as platforms evolve.
For biopharma, connected labs produce data that is more trustworthy, reproducible and valuable across the research and development (R&D) lifecycle. When teams can move faster with greater confidence in their results, the path shortens between scientific discovery and the safer, more efficacious therapies patients are waiting for.
A first step any lab can take today? Audit one integration gap and fix it end-to-end.
Author Bio
Mark Fish is the Vice President & General Manager of Digital Science and Automation Solutions at Thermo Fisher Scientific. Mark’s role focuses on the innovation and execution of Thermo Fisher’s Automated Digital Lab Solutions portfolio, as the company enables customers to reimagine their laboratory processes with leading-edge digital, laboratory and automation capabilities. With over 20 years of laboratory informatics experience, Mark has focused on building long-term, trusting customer relationships and delivering valuable, strategic solutions to address industry challenges. Throughout his career, Mark has held significant positions at Thermo Fisher Scientific, Accenture, Brooks Life Sciences and Brooks Automation. With his extensive experience, Mark has developed a deep understanding in the laboratory informatics and laboratory automation spaces.
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