March 9, 2026
About LabKey
And why they persist even in labs that think they’re organized.
Every lab has workflows. Sample registration, processing, assay requests, analysis, storage, reporting- the steps exist whether they’re documented or not. But having workflows and having managed workflows are two very different things.
The difference shows up quietly at first. A sample takes 15 minutes to locate instead of 15 seconds. An experiment gets repeated because no one could find the original results. An audit request triggers two weeks of manual data assembly. Individually, these feel like minor friction points. In aggregate, they’re a structural drag on everything the lab does.
After working with scientific teams across academia, biotech, pharma, and other research organizations, we’ve identified five workflow gaps that consistently slow labs down. We’ve seen these gaps to be impactful regardless of size, discipline, or how sophisticated the team is.
1. The Thread Is Broken Between Process Steps
In most labs, the workflow technically exists, but it’s distributed across four or five disconnected systems. SOPs live in a shared drive. Sample data sits in a spreadsheet. Assay results land in an ELN or a separate file. Handoff communication happens over slides, email or messaging tools.
No single system holds the thread from sample registration to final result.
This means that when someone needs to answer a basic question like, “What happened to this sample?”, they’re stitching the answer together manually from multiple sources. The information exists, but it’s not connected.
The deeper cost isn’t the time spent searching, it’s the context that gets lost between steps. When a scientist picks up a sample mid-workflow, they should inherit the full history of what’s been done, by whom, and what the results were. In most labs, they inherit a sample ID and a best guess.
2. Traceability Is Retroactive, Not Automatic
Labs rarely notice traceability problems during normal operations. They surface under the pressure of audits, regulatory submissions, partner due diligence, or when leadership asks for a summary to inform decision-making.
That’s when the scramble begins: pulling data from multiple systems, cross-referencing dates and user logs, reconstructing a chain of custody that should have been captured in real time but wasn’t.
The root problem isn’t carelessness. It’s architecture. If the system doesn’t generate traceability as a natural byproduct of doing the work, the audit trail will always be assembled after the fact. And anything assembled retroactively is incomplete by definition.
Here’s a simple test: pick any sample in your lab right now. How long would it take to produce a complete record of every step that sample has been through- who handled it, what was done, and what the results were? If the answer is anything more than a couple of minutes, the traceability infrastructure has a gap.
3. The System Doesn’t Match How Scientists Actually Work
This is the gap that erodes adoption over time.
Most lab systems, whether they’re a LIMS or a homegrown tool, impose a fixed way of working. They have rigid templates, predefined fields, and workflows designed by someone who wasn’t in the lab when the protocols were written.
When a new assay type emerges, or a sample class changes, or a team develops a better protocol, the system can’t adapt without an IT ticket, software vendor support, or a workaround.
Scientists almost always choose the workaround. They open a side spreadsheet. They track the new lab workflow manually. They add a notes column to capture what the system won’t.
The result: the “system of record” is no longer the actual record. The real data lives in shadow files that only their creator fully understands.
This isn’t a training problem or a compliance problem. It’s a design problem. If the system can’t evolve with the science, scientists will route around it — and every workaround creates a new data integrity risk.
4. Knowledge Lives in People, Not in the System
Every lab has at least one person who knows where everything is. They know which freezer holds the backup samples. They know which column in the spreadsheet is actually current. They know the unwritten steps in the protocol that aren’t in the SOP.
This is siloed knowledge, and it’s a single point of failure.
When that person goes on vacation, transitions to a new role, or leaves the organization, the lab doesn’t just lose a team member — it loses the map. New hires take weeks or months to become productive, not because the science is hard, but because the systems don’t encode the knowledge they need.
Structured workflow management eliminates this dependency. When every step is captured, every handoff is documented, and every protocol is digitally enforced, the system itself becomes the institutional knowledge. New team members onboard into the workflow, not around it.
5. The Tools Can’t Scale With the Science
This gap is often invisible until it’s urgent.
A lab starts with 10 people and a manageable number of samples. Spreadsheets work. Manual tracking is annoying but survivable. Then the team grows. The pipeline expands. New assay types get added. A second site comes online.
Suddenly the spreadsheet has 47 tabs, three people are maintaining conflicting versions, and nobody trusts the data anymore.
The problem isn’t that the lab outgrew the spreadsheet — it’s that the lab outgrew the approach. Scaling requires systems that can absorb new sample types, new workflows, new team members, and new reporting requirements without breaking.
Most labs don’t realize this until they’re already in crisis mode, such as when a regulatory deadline is approaching, a partnership is on the line, or the leadership team is asking for data the lab can’t produce.
The Pattern Behind the Gaps
These five gaps aren’t independent. They compound.
A fragmented system (Gap 1) makes traceability impossible to maintain automatically (Gap 2). A rigid system (Gap 3) pushes scientists into workarounds that create shadow data. Undocumented knowledge (Gap 4) fills the gaps that the tools can’t, until the people with that knowledge are no longer available. And the whole structure becomes increasingly fragile as the lab scales (Gap 5).
The common thread: these aren’t process problems. They’re infrastructure problems. No amount of SOPs, training, or spreadsheet discipline will close them permanently. They require a structural change in how the lab captures, connects, and manages data across the full workflow lifecycle.
What to Do About It
If you recognized your lab in two or more of these gaps, the first step isn’t buying a new tool. It’s diagnosing where the structural gaps are creating the most risk- to reproducibility, to compliance, to decision-making speed, or to your ability to scale.
Three questions worth asking your team this week:
- Where do we lose the thread? Identify the specific handoff points where sample or experimental context gets lost between systems or people.
- What would an auditor see? Walk through a sample’s journey as if you were responding to a regulatory request. Note every point where you have to leave one system and open another.
- Where are the shadow files? Find the spreadsheets, side trackers, and personal logs that exist because the official system couldn’t handle something. Each one represents a workflow gap.
These questions won’t fix the problem, but they’ll make the scope visible, a prerequisite for any meaningful improvement.
