The move from seeing artificial intelligence as an interesting add-on to treating it as a core lab capability rarely happens through a sweeping, top-down strategy. More often, it happens in small, practical steps that are driven by immediate needs at the bench rather than a fully coordinated master plan.
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Because of that, the results vary. In many cases, AI adoption is shaped just as much by the shortcomings of existing tools as by intentional design. Some labs are seeing clear, measurable improvements. Others have more activity and more tools in play, but not much lasting change to show for it.
If we want to understand why outcomes differ so widely, it helps to step back from individual features and look at the bigger picture. A lab AI maturity model can be useful here as a way to think about how documentation, interpretation, and decision-making are beginning to connect again in a more systematic way.
The gap between recording data and using it
At the center of this shift is a growing disconnect between recording experimental data and actually being able to use it in a meaningful way.
Over the past decade, laboratories have invested heavily in electronic lab notebooks. Those investments have largely paid off in documentation and compliance. In recent research, 62 percent of lab professionals say their ELN supports efficient day-to-day work.
Interpretation, however, remains a weak point. Eighty-one percent report that their ELN captures data but does not help them interpret it. Prior work may exist, yet still be difficult to find, compare, or trust in context. The result is a paradox. Labs can be compliant and data-rich, while still struggling to leverage their own experimental history.
This friction has real consequences. Sixty-five percent of scientists report repeating experiments or assays because previous results were too hard to locate or reuse with confidence. That repetition is not a failure of scientific rigor. It is a workflow problem.
The passive lab: Documentation without momentum
This pattern defines the first stage of the maturity model, often described as the passive lab.
In a passive environment, the ELN functions primarily as a system of record. It reliably captures data, but plays a limited role in active scientific thinking. Interpretation and analysis frequently happen elsewhere.
Autonomy is constrained. Only 7 percent of researchers report being able to configure assays or templates without specialist support. Routine changes can introduce delays, and interpretation becomes fragmented across spreadsheets, specialist tools, and informal processes that sit outside the governed notebook.
Scientific work continues, but context does not accumulate in a durable way.
The middle ground most labs now occupy
As pressure for speed increases, many passive labs drift into a transitional state.
Scientists begin supplementing the ELN with public generative AI tools to summarize results, explore explanations, and plan experiments. The productivity gains are immediate. The structural costs are not.
Research shows that 77 percent of lab professionals now use public AI tools alongside their ELN. For many, this is not driven by policy decisions, but by necessity. Governed tools do not yet support the interpretive work scientists need to do, so they look elsewhere.
In this middle stage, the pace of work accelerates while the scientific record thins. Reasoning shifts into unmanaged environments. The “why” behind decisions lives in personal chat histories that the organization cannot review, audit, or reuse.
The active lab: Bringing reasoning back into the record
The most mature stage of the model is the active lab.
Here, intelligence moves back inside the governed environment. A third-generation ELN supports interpretation at the point of documentation, retaining reasoning alongside results so it can be reviewed, reused, and trusted.
The defining characteristic of an active lab is not simply the presence of AI. It is the ability to take governed action within the workflow. Interpretation, comparison, and planning occur close to the experimental record rather than being reconstructed after the fact.
Progression to this state is incremental. Early gains come from retrieval and comparison. Scientists can pull relevant prior work, summarize outcomes across runs, and explore interpretations with clear links back to source data. As trust and governance mature, more orchestrated actions can be introduced, always within defined guardrails and with explicit human oversight.
It is also worth remembering that scientific culture is inherently evidence-driven, and that discipline carries over to AI. Eighty-one percent of researchers say they would only trust AI-generated suggestions if they can review the underlying data and logic. Transparency is not a secondary feature. It is a prerequisite rooted in scientific norms.
Redefining the role of the lab notebook
Taken together, these shifts change what the lab notebook actually represents.
Instead of serving as a static repository, it starts to function as an integration hub where experimental data, analytical tools, AI support, and human judgment come together. Connectivity across the broader ecosystem becomes critical. Interpretation rarely happens in isolation, and context is easily lost when systems don’t communicate with one another.
Sapio Sciences’ ELaiN reflects this third-generation notebook model. It embeds science-aware AI directly into governed workflows, helping ensure that interpretation stays anchored to underlying evidence. The objective is to make sure that speed, trust, and reuse develop in parallel.
As AI becomes more embedded in laboratory operations, the question is no longer whether labs will use it. The more pressing issue is how intentionally they decide where it belongs and how it fits into daily work.
When interpretation is as straightforward to capture as documentation, informal workarounds become less attractive and institutional knowledge can finally keep pace with the science itself.
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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