In biopharma R&D, “AI adoption” is often framed as a binary switch: either a lab has it, or it does not. Research* involving 150 life sciences professionals, however, suggests the reality is more incremental and far less orderly. AI integration is not driven by a single software deployment but by a progression of behaviors shaped by the limitations of existing tools. Labs do not simply adopt AI; they move through recognizable stages based on how data, interpretation, and decision-making are handled in practice.
Understanding where a lab sits on this curve is increasingly important for addressing productivity loss and protecting institutional knowledge. And based on the recent research, we see a clear lab AI maturity model emerging.
1: The passive lab (system of record)
In a passive lab, the electronic lab notebook functions primarily as a digital filing cabinet. It is optimized for recording what was done, maintaining audit trails, and meeting regulatory requirements, reflecting the historical role of the electronic lab notebook. This foundation is important, but it stops short of supporting scientific reasoning.
● The symptoms: Scientists spend a disproportionate amount of time acting as intermediaries between systems. More than half report spending excessive time manually importing and exporting data between their ELN and other tools.
● The dependency: Autonomy is low. Only 7%of scientists report being self-sufficient at configuring assays or templates, and just 5% say they can analyze experimental data without external support.
● The bottleneck: Interpretation is routinely displaced into specialist queues. Around two-thirds rely on IT or informatics teams for configuration, and a similar proportion depend on data scientists to interpret results more than a quarter of the time.
● The cost: This model carries a clear efficiency penalty. Sixty-five percent of scientists report repeating experiments or assays because previous results are difficult to locate, reuse, or trust.
2: The shadow lab (adaptive workaround)
The shadow state emerges when scientists outgrow passive tools but cannot wait for formal change. Under pressure to move faster, they layer public generative AI tools alongside the ELN, a pattern increasingly described as shadow AI in the lab.
● The symptoms: AI use becomes nearly universal. Ninety-seven percent of scientists report using some form of AI to support their work, with 77% using public generative AI tools such as ChatGPT, Claude, or Gemini alongside the ELN.
● The risk: Governance weakens as productivity improves locally. Nearly 45%of scientists access these tools through personal accounts they created themselves, moving experimental context outside IT visibility.
● The gap: Despite widespread use, these tools are not designed for scientific work. Only 27% of scientists say current generative AI tools meet their scientific needs very well, while many describe them as poorly suited to lab-specific workflows. Shadow labs are adaptive, but they are inherently unstable. Scientific reasoning increasingly happens outside validated systems, even as the ELN remains the official system of record.
3: The active lab (system of reasoning)
The active lab represents a structural shift. AI is no longer treated as an external aid but embedded directly into the notebook environment through a third generation ELN. This more integrated approach to AI adoption leads to greater streamlining, simplified administration, and more time for science.
● The characteristics: The notebook becomes an active participant in the scientific loop. It supports hypothesis development, highlights patterns across experiments, and connects design, execution, and analysis within a single environment.
● The mandate: Demand for this shift is clear. Ninety-nine percent of scientists agree that ELNs should act as intelligent research partners, and 96%say future systems must help interpret data rather than simply capture it.
● The trust requirement: Transparency is essential. Eighty-one percent of scientists say they would only rely on AI-generated suggestions if the underlying data and reasoning can be reviewed.
Interpreting the maturity curve
The transition from a passive to an active lab is not a routine software upgrade. It reflects a structural change in how scientific work is supported. Passive environments externalize reasoning into spreadsheets and specialist queues. Shadow environments externalize it into public AI tools.
Active labs, on the other hand, bring interpretation and decision-making back into the notebook itself, where scientific context can be retained, reviewed, and reused over time. In these environments, the notebook functions as a system of reasoning rather than a static archive, supported by an integrated AI notebook ecosystem.
For lab, informatics, and technology leaders, the maturity curve offers a practical lens. It clarifies whether scientific reasoning is treated as transient context or durable institutional knowledge and whether digital tools are documenting discovery or actively supporting it.
*About the research: The findings are based on a survey of 150 scientists working in laboratory environments across the United States and Europe. Respondents represented a range of sectors, including biopharma R&D, contract research organizations, clinical diagnostics, and pharmaceutical manufacturing. The research explored how scientists use electronic lab notebooks and AI tools in their day-to-day work, focusing on usability, data analysis capability, experiment reuse, and emerging behaviors such as the use of public generative AI. The survey was conducted in November 2025.