Artificial intelligence has moved well beyond early excitement and is now used throughout modern laboratories. Scientists routinely rely on generative models to synthesize complex results, draft protocols, and think through possible next steps in an experimental series.
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What’s increasingly striking is not that these tools are being used, but where much of that use is happening. In many labs, interpretive work is drifting away from the official experimental record and into public, unmanaged tools.
This pattern is often labeled “shadow AI,” a term that suggests a lapse in discipline or a breakdown in oversight. That framing misses the mark. In practice, unmanaged AI use reflects less a failure of governance and more a mismatch in system design. Scientific interpretation is often easier to carry out outside formal platforms than within them, even when those platforms are meant to anchor day-to-day lab work.
Documentation is not the same as interpretation
Most laboratories have made real progress on documentation. Electronic lab notebooks are generally effective at capturing the mechanics of an experiment: methods, raw outputs, and the metadata required for compliance. Yet the most consequential part of scientific work often begins only after results are recorded.
Interpretation requires comparison across runs, reconciliation of anomalies, and rapid judgment about what to do next. These are time-sensitive, cognitively demanding tasks. When performing them inside governed tools feels slow or cumbersome, researchers adapt.
In practice, that adaptation increasingly involves public generative AI tools. They are fast, conversational, and well-suited to synthesis.
Research into lab workflows reflects this shift. AI use is now nearly universal, with 97 percent of lab professionals reporting some form of AI to support their work. More notably, 77 percent say they use public generative AI tools alongside their electronic lab notebooks. Nearly half report doing so through personal accounts rather than company-managed access.
This is not fringe behavior. It is a pragmatic response by experienced scientists who encounter friction in formal systems and prioritize speed when decisions need to be made.
What is lost when reasoning leaves the record
The implications of this shift go beyond security or compliance. The more lasting concern is what happens to scientific reasoning over time.
When interpretation happens in external tools, and only the final conclusions are entered into the notebook, much of the thinking behind those conclusions never makes it back into the record. Discarded hypotheses, “what-if” scenarios, and the context behind key decisions stay outside the system. What gets saved is the answer, not the reasoning that led there.
Over time, that starts to thin out the scientific record. The next researcher can see what was concluded, but not how the team got there. Confidence in reuse begins to slip. Experiments get repeated, not because replication is scientifically necessary, but because earlier work can’t be quickly evaluated or trusted in context.
Data has been found to back this up. Sixty-five percent of lab professionals say they’ve repeated experiments or assays because previous results were hard to find or reuse with confidence. The issue isn’t a lack of data. It’s missing context and fragmented reasoning.
The limits of general-purpose AI in scientific workflows
Public generative AI tools can be genuinely helpful while still poorly aligned with scientific workflows. Their limitations are arguably more structural than technical.
General-purpose models lack embedded experimental context. They do not know which dataset version is authoritative, how a specific instrument has behaved over time, or which methods have been formally validated within a given organization. They also do not automatically preserve provenance or decision rationale.
This mismatch helps explain a paradox in current adoption. Despite widespread use, only 27 percent of scientists say existing AI tools meet their scientific needs very well. This means that these tools are only being used because they reduce friction, not because they are designed for governed scientific reasoning.
Designing workflows that keep interpretation inside the lab notebook
Institutional responses to unmanaged AI use often focus on restriction. New policies and outright bans may reduce visible usage, but they rarely address the underlying demand. When high-value interpretive work isn’t well supported inside governed systems, it doesn’t stop happening. It simply moves somewhere else.
A more sustainable approach is to redesign workflows so interpretation happens where experiments are documented and reviewed. This is the logic behind third-generation, science-aware lab notebooks. Rather than treating AI as an external add-on, these platforms embed AI-assisted reasoning directly into the notebook environment, alongside experimental context and audit trails.
Sapio Sciences’ ELaiN reflects this direction. By integrating science-aware AI within the lab notebook and connecting it to a broader ecosystem of analytical and domain-specific tools, the goal is to support interpretation without fragmenting the record. Reasoning is captured alongside the experimental data, making it easier to review, validate, and reuse over time.
When the notebook becomes the easiest place to think through scientific results, the incentive to rely on unmanaged workarounds diminishes. The objective is to ensure that when AI is used to summarize, compare, or plan, logic remains part of the institutional memory.
Unmanaged AI use is best understood as a symptom. It reveals where current tools have failed to keep pace with how science is actually practiced. Addressing it requires less emphasis on enforcement and more attention to workflow design that respects how scientists reason, decide, and move forward.
About Sapio Sciences
Sapio Sciences' mission is to improve lives by accelerating discovery, and because science is complex, Sapio makes technology simple. Sapio is a global business offering an all-in-one science-aware (TM) lab informatics platform combining cloud-based LIMS, ELN, and SDMS.
Sapio serves some of the largest global and specialist brands, including biopharma, CRO/CDMOs and clinical diagnostic labs across NGS genomic sequencing, bioanalysis, bioprocessing, stability, clinical, histopathology, drug research, and in vivo studies. Customers love Sapio's platform because it is robust, scalable, and with no-code configuration, can quickly adapt to meet unique needs.
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