Lab Orchestration Is Not What You Think It Is

The modern research lab is a marvel of engineering. Robotic systems move samples with precision, automated workflows coordinate hundreds of instruments, and the research itself progresses around the clock.

Yet orchestration is too often only understood as the coordination of these physical assets and lab workflows, and that definition no longer works. If we are serious about accelerating scientific innovation, we need a way to manage every aspect of the lab.

Scientists still spend too much of their time stitching together disconnected instruments and data. The result is not simply operational inefficiency.

The bigger problem is that scientific intent becomes separated from experimental results, the context is lost as information moves between systems, and researchers become the connective tissue holding the laboratory together. A modern definition of lab orchestration goes far beyond the physical movement of samples.

It connects the physical, digital, and logical dimensions so that experimental intent, data, and decision-making remain linked throughout the design-make-test-analyze lifecycle. This connective layer creates a continuous scientific record that supports reproducibility, technology transfer, and ultimately, trustworthy artificial intelligence (AI).

So, what does an orchestrated laboratory look like when those three domains operate as one?

Orchestration begins where automation ends

Consider the above figure. On the left are the inputs that enable scientific work: people, instruments, materials, data, software, experimental protocols, standard operating procedures, and analytical tools.

Most discussions of lab orchestration focus on this coordination of physical instruments and automated processes, as if providing a new name for the work of scheduling software. Those capabilities remain essential, but they only represent one aspect of the paradigm.

The distinguishing feature of orchestration is the connective layer between the inputs and outcomes. As experiments progress, the context must move alongside the samples.

Too often we have instruments sitting next to each other that do not know anything about the data or the samples that the other one is processing or analyzing. Experimental intent, instrument settings, meta data, and analytical decisions must remain linked as information passes between systems.

This connective layer preserves scientific meaning, allowing every observation to be interpreted within the conditions that produced it. Otherwise, data are generated throughout the laboratory as disparate and disconnected files, much of which gets left behind, rather than coordinated, connected findings.

In the context of AI, we know that agentic AI is only as trustworthy as the data beneath it, and that fragmented, decontextualized, and partial data produces outputs no one can act on. Gartner predicts that in 2026, 80% of agentic AI initiatives in life sciences will fail as organizations struggle to ensure transparency and evidence-based reasoning.¹

The big concern is that as laboratories grow in complexity, these gaps only compound, which turns scaling into a source of operational fragility rather than scientific advantage as it should be. The outcomes shown on the right side of the figure (e.g., greater reproducibility, regulatory compliance, AI-funneled learning, etc.) are therefore not direct products of automation alone.

They only emerge when the entire research ecosystem becomes orchestrated. Note that AI belongs at the end of this sequence where it becomes the natural result of a laboratory whose knowledge has been consistently connected, preserved, and made computable throughout the scientific process.

AI does a great job of trying to make sense out of unstructured data, but it does an even better job of making sense out of structured data.

The human cost of an unconnected lab

It has long been estimated that scientists lose upwards of 50 days per year owing to inefficient processes, and on average 10% to 20% of development work is repeated due to data integrity and accessibility issues.² In a more recent study, 80% of scientists said that the workarounds currently required to get data into meaningful outputs are negatively impacting their work and almost 70% reported compromised decision-making because of this.³

How does the fragmentation and loss of context impact different roles in life sciences organizations?

• The scientist: Writing up a single experiment can take a day or more, as scientists must manually pull together data from digital, physical, and logical sources across the lab. Whereas, with a complete scientific record, AI could do all of that data wrangling in minutes, thereby freeing up the scientist to perform his or her most valuable contribution: creative thinking.
• The digitalization lead: The other challenge is not just context, it is also compatibility. Tools were built by different vendors with no intention of working together, and someone has to bridge those gaps. In today’s labs that patching work is never finished, it is rarely elegant, and it scales badly.
• The R&D director: Because they operate on a fragmented foundation, technology investments perpetually underdeliver. Unfortunately, no single investment can fix that; the foundation must first be addressed.

Three domains define the orchestrated lab

A complete definition of lab orchestration has to operate across three domains at once.

1. The physical domain is where the science happens: instruments generating data, robotic systems executing workflows, scheduling platforms coordinating the movement of samples and the sequencing of tasks. This layer is well-served by dedicated automation and scheduling tools, platforms purpose-built to control equipment, manage queues, and keep physical operations running smoothly.
2. The logical domain is the structure that gives physical work its meaning: experimental protocols, workflow design, business rules, the sequence of decisions that shape how a study is run. This is where scientific intent lives before it becomes action.
3. The digital domain is where data must land to become knowledge: systems that capture experimental outputs alongside their context, connect results to the workflows that produced them, and maintain continuity across the full arc of a research program—across teams, across time, and across the inevitable evolution of tools and methods.

Today's lab is supported by an intricate web of vendor-specific integrations that require continuous maintenance simply to sustain baseline operations. Meanwhile, scientific intent and experimental context remain largely disconnected from both physical workflows and digital systems.

Whereas in a connected lab, data capture is continuous and context remains embedded with results throughout the scientific lifecycle. The connected lab preserves experimental history and a material’s lineage, enabling researchers to understand how outcomes were generated months or years later.

Discovery and development remain connected, allowing AI systems to operate on a complete scientific record rather than fragmented information.

Questions R&D leaders can ask today

• How many vendors and internal integrations does it take to keep your current lab data ecosystem running, and what happens when one of them breaks?
• When a scientist leaves or moves to a new program, how much of the knowledge they accumulated leaves with them?
• How long can your lab operate effectively in the absence of a human operator? Does your lab rely heavily on humans to move data from one system to another, key information into an instrument interface, or evaluate results to determine the next step in the experimental process?
• If you asked your AI initiative to reason across your full scientific record, including the intent behind experiments, the decisions made along the way, and the context that connects them, could it?

The connected lab begins with a decision to stop accepting fragmentation as the cost of doing science and moving toward an orchestrated scientific environment. And it starts with one honest answer to one of those questions.

References

1. Harwood R. Transforming R&D in life sciences: how AI co-scientists are accelerating discovery. Gartner. Published February 5, 2026.
2. Making the most of drug development data. Pharmaceutical Manufacturing. Published December 1, 2005. https://www.pharmamanufacturing.com/production/automation-control/article/11365550/processing-engineering-making-the-most-of-drug-development-data-pharmaceutical-manufacturing
3. Optimizing outsourcing in early small molecule drug discovery. Dotmatics. Published March 21, 2022. https://www.dotmatics.com/whitepapers/cro-outsourcing-early-small-molecule-drug-discovery