Processing a lab sample. Image by Tim Sandle
Research involving 150 laboratory professionals shows that most labs currently operate in a so-termed ‘passive state’, using electronic laboratory notebooks (ELNs) as digital filing cabinets, or a shadow state, relying on ad hoc public AI.
Andrew Wyatt, Chief Growth Officer, Sapio Sciences has told Digital Jopurnal how the transition to an active lab occurs through the AI Lab Notebook (AILN), which embeds governed, science-aware intelligence directly into the notebook to connect data and decision-making.
Much of the discussion around AI in life sciences assumes a clean, immediate transition from legacy tools to intelligent platforms. In practice, says Wyatt, adoption is driven by a series of smaller changes and shaped by day-to-day pressures at the bench. When traditional electronic lab notebooks (ELNs) fail to support interpretation or planning, scientists do not stop working. They adapt. Over time, these adaptations form a recognizable maturity curve rather than a simple binary divide between old and new tools.
Research from late 2025 involving 150 lab professionals across the United States and Europe provides a clear, data-driven view of how intelligence is entering the lab. The findings define three distinct stages of maturity: passive, shadow and active.
Stage 1: The passive lab
According to Wyatt: “In the passive stage, the ELN functions primarily as a digital filing cabinet. Experiments are documented and compliance is supported, but the software rarely influences what happens next. Interpretation, planning and reuse of results occur elsewhere, often through manual spreadsheets or heavy reliance on specialist informatics teams.”
This passivity, Watt observes: “Creates measurable drag on discovery. The research shows that 65 percent of scientists repeat experiments because results are difficult to find or reuse in their current tools. These labs are not failing due to a lack of talent. They are constrained by tools designed to capture past activity rather than actively support scientific reasoning.”
Stage 2: The shadow lab
Shadow labs emerge, Wyatt details, when scientists push beyond these constraints without waiting for formal IT change. He states: “Public generative AI tools are layered around the ELN to assist with drafting, interpretation and experimental planning. While local productivity may improve initially, governance and data integrity can weaken.”
Furthermore: “Seventy-seven percent of scientists report using public AI tools for lab work, and nearly half do so through personal accounts outside organizational visibility. Shadow labs are an adaptive response to unmet demand, but they are inherently unstable. They move sensitive scientific reasoning into unvalidated environments where intellectual property may sit outside the governed system of record.”
Stage 3: The active lab
As for the next stage: “Active labs take a fundamentally different approach by embedding intelligence directly into the notebook environment. This transition is anchored by the AI Lab Notebook (AILN), which acts as a governed co-scientist rather than an ad hoc side channel.”
Here Wyatt finds: “In an active lab, the AI lab notebook helps interpret results, expose patterns and connect related experiments in context. It also helps drive workflow. Designs translate into actionable work in the lab, and data flows between instruments, analysis and the experimental record. The familiar scientific loop of hypothesize, design, plan, act and analyse becomes a connected, lab-in-the-loop process rather than a series of disconnected steps.”
Active labs do not represent full automation. They represent tighter coupling between data, analysis and action, with scientists firmly in control.
Agency over conversation
The defining feature of an AI Lab Notebook is agency rather than conversation. Instead of generating text in isolation, the AI operates within the software environment itself, with governed access to instruments, data, analytics and workflows.
As to what this means, Wyatt explains: “Scientists can ask the notebook to analyse results, compare experiments or prepare next steps, and the system can act on those requests within approved processes. This allows the notebook to support the scientific loop end-to-end, without removing human judgment or obscuring evidence.”
The are points to consider, however: “Trust remains central to adoption. Research shows that 81 percent of scientists will only rely on AI suggestions if they can review the underlying science and evidence. Active labs succeed not by automating decisions, but by reducing friction between observation and understanding.”
A roadmap for discovery
Wyatt further cautions: “The maturity model is not a diagnostic scorecard. It is a practical roadmap for navigating how AI is already entering the lab.”
Outlining his roadmap, Wyatt steers: “For organizations operating in a passive state, the priority is to improve data findability, reuse and interpretation where records already exist. Turning static data into accessible knowledge reduces delays and lays the groundwork for more advanced capabilities. For labs operating in a shadow state, the challenge is realism rather than restriction.”
He concludes by noting: “Reaching the active stage requires strengthening the foundations that connect data generation, analysis and execution into a continuous lab-in-the-loop workflow. As models become more capable, the labs that succeed will be those that treat the notebook as a system of reasoning rather than a passive archive.”