OPINION
AI in the laboratory: the path to full integration
Andrew Wyat, Chief Growth Officer, Sapio Sciences
Much of the discussion around AI in life sciences assumes a clean, immediate transition from legacy tools to intelligent platforms. In practice, adoption is driven by a series of smaller changes and shaped by day-to-day pressures at the bench. When traditional electronic laboratory notebooks (ELNs) fail to support interpretation or planning, scientists do not stop working. They adapt. Over time, these adaptations form a recognisable maturity curve rather than a simple binary divide between old and new tools. Research from late 2025 involving
150 laboratory professionals across the United States and Europe provides a clear, data-driven view of how intelligence is entering the laboratory. The findings define three distinct stages of maturity: passive, shadow and active.
Stage 1: The passive laboratory 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 creates measurable
drag on discovery. The research shows that 65% of scientists repeat experiments because results are difficult to find or reuse in their current tools. These laboratories 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 laboratory Shadow laboratories emerge when scientists push beyond these constraints without waiting for formal IT change. 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. Almost four in five (77%) of scientists
report using public AI tools for laboratory work, and nearly half do so through personal accounts outside organisational visibility. Shadow laboratories 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 laboratory Active laboratories 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. In an active laboratory, the AI notebook
helps interpret results, expose paterns and connect related experiments in context. It also helps drive workflow. Designs translate into actionable work in the laboratory, and data flows between instruments, analysis and the experimental record. The familiar scientific loop of hypothesise, design, plan, act and analyse becomes a connected, laboratory-in-the-loop process, rather than a series of disconnected steps. Active laboratories do not represent
full automation. They represent tighter coupling between data, analysis and action, with scientists firmly in control.
About Andrew Wyat Andrew Wyat is Chief Growth Officer at Sapio Sciences, responsible for growing the company’s international operations. He has over 30 years of expertise in commercially scaling global software companies. Andrew has worked in a wide range of organisations, from NASDAQ listed companies to privately held businesses, from the communications sector to life sciences industry. Most recently Andrew was the COO of healthcare orchestration firm Lumeon, where he successfully grew the business and entered the US market.
www.sapiosciences.com/
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. 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. Trust remains central to adoption.
Research shows that 81% of scientists will only rely on AI suggestions if they can review the underlying science and evidence. Active laboratories succeed not by automating decisions, but by reducing friction between observation and understanding.
A roadmap for discovery The maturity model is not a diagnostic scorecard. It is a practical roadmap for navigating how AI is already entering the laboratory. For organisations operating in a
Research from 2025 involving 150 laboratory
professionals provides a clear, data-driven view of how intelligence is entering the laboratory. The findings define three distinct stages of maturity: passive, shadow and active
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 laboratories operating in a shadow state, the challenge is realism rather than restriction. Reaching the active stage requires
strengthening the foundations that connect data generation, analysis and execution into a continuous laboratory- in-the-loop workflow. As models become more capable, the laboratories that succeed will be those that treat the notebook as a system of reasoning rather than a passive archive.
PPi April 2026
WWW.PATHOLOGYINPRACTICE.COM 5
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56
