After years in diagnostic microbiology and three years building software tools inside a working research laboratory, one thing has become clear: the weakest link in most laboratories is not the science. It is the infrastructure the science depends on.
Most laboratory managers already know this, even if they rarely say it out loud. Talented scientists cannot produce reliable, reproducible, inspection-ready research when the systems around them are not fit for purpose.
Before studying informatics, I worked as a diagnostic microbiologist in Nigeria, learning what it takes to keep a high-volume laboratory running under sustained pressure. When COVID-19 arrived, I watched that system break. Not the science. The infrastructure. Ordering workflows stalled. Compliance documentation piled up. Processes not designed for resilience showed their limits. That experience brought me to study biomedical informatics at the University at Buffalo. Three years later, I am more convinced than ever: the bottleneck is not scientific capability. It is the operational infrastructure holding that capability up.
The gap that does not get enough attention
The infrastructure problem shows up in predictable places. Supply chains managed on spreadsheets with no forecasting and no audit trail worth the name. Compliance documentation reviewed in bursts before inspections rather than continuously. Laboratory information management systems and electronic laboratory notebooks sitting in separate silos, connected by improvised workarounds rather than intentional data flows.
None of this reflects poor judgment. It reflects an industry that has invested heavily in the science and far less in the systems that support it. Federal Demonstration Partnership surveys found that principal investigators spend 42 percent of their research time on administrative and compliance tasks—more than double the rate from two decades prior. A 2015 study across 13 US universities found that regulatory compliance consumed 11 to 25 percent of research expenditures.
That is not a staffing problem. It is a systems problem.
How to tell if your laboratory has a systems problem
The signs of a systems gap rarely announce themselves – they disguise themselves as people problems. Answer four questions before drawing conclusions about individual performance:
1. Are the same mistakes happening repeatedly across different people? If an error recurs with multiple staff members, the process is the problem, not the person. A well-designed system makes the correct action the easier one.
2. Are your most capable people spending time on tasks that do not require their expertise? Skilled scientists manually reconciling inventory or reformatting data is a workflow problem, and compounds over time.
3. Does your laboratory operate differently the week before an inspection than the week after? If compliance activity spikes around audits and flattens between them, your infrastructure is reactive rather than continuous—exactly what inspectors are trained to find.
4. How long does it take to trace something when it goes wrong? If answering a question about a reagent lot or a protocol deviation requires significant manual effort, the system lacks adequate audit trail infrastructure. Retrieval should take minutes.
If any of these scenarios sound familiar, the issue is likely not your team. The infrastructure is making skilled people look less effective than they are.
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Why existing tools have not solved this
Most automation tools were designed for general enterprise contexts, adapted for laboratory use, and that adaptation is often incomplete. Audit trails, validation documentation, role-based access controls, and inspection-ready governance must be incorporated from the beginning. They are architectural requirements, not features.
When evaluating any tool, the real question is whether it can produce a complete, traceable record of every transaction a federal inspector could review without advanced notice. Most tools cannot answer that satisfactorily, so the spreadsheet stays.
What compliance-first design actually looks like
The systems that work best share a single principle: compliance is not a feature to add later. It is the foundation on which everything else is built.
Capabilities like predictive reordering, spending forecasts, and anomaly detection only become useful when they sit within a framework that includes role-based authentication, receipt confirmation, and inspection-ready documentation. Continuous document analysis—scoring records against FDA 21 CFR Part 11 requirements and flagging gaps as they emerge—means the goal is not to prepare for an audit. It is always to be ready for one.
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A framework for evaluating any new system
Three principles should be in place before any technology is adopted.
The first is role clarity before deployment. Every workflow should map each action to a specific role: who initiates, who reviews, who approves? When roles are ambiguous, people either reach into decisions that are not theirs or hold back on problems that are. Both create risk.
The second is a designated systems thinker—someone responsible for tracing a process end-to-end, identifying where a workflow breaks down before an inspector does, and asking where data could move without leaving a record. This need not be full-time, but must belong to someone with time and authority to act.
The third is pairing people to specific parts of the system. When everyone is theoretically responsible for compliance, no one actually is.
With those principles in place, evaluation questions become concrete: Does the system assign alerts to specific roles or generate notifications nobody acts on? Does the audit trail show who held responsibility, not just what happened? If the person who understands this system most deeply left tomorrow, would it still function? A tool that cannot satisfy those questions was not built for a real laboratory.
The path forward
The laboratories that benefit most from intelligent automation will not be those with the most sophisticated tools, but those that adopt tools designed for the regulatory reality they already operate in.
The systems problem is real and solvable. COVID-19 demonstrated what happens when infrastructure is treated as an afterthought. To make intelligent tools work in scientific environments, we must start with the systems the science runs on.
Note on AI use: In accordance with Lab Manager’s contributor guidelines, AI tools were used to assist with outline development and structural organization. All writing, analysis, professional judgments, and conclusions are the author’s own.