From System of Record to Co-Scientist: Three Stages of Lab AI Maturity

From Digital Filing Cabinets to Co-Scientists: Mapping the Three Stages of Lab AI Maturity

Published: February 16, 2026

AI adoption in biopharma R&D isn't a switch you flip. It's a progression. Labs move through clear stages defined by how data, interpretation, and decisions actually happen day to day.

Based on research with 150 life sciences professionals, three stages keep showing up: passive, shadow, and active. Knowing where you sit is the fastest way to stop productivity loss and stop your hard-won insights from slipping through the cracks.

Stage 1: The passive lab (system of record)

The ELN acts like a digital filing cabinet. It records work, maintains audit trails, and supports compliance obligations like 21 CFR Part 11. Useful, but it doesn't help you reason about science.

• The symptoms: Scientists shuttle data between tools. Over half spend excessive time importing and exporting between the ELN and other systems.
• The dependency: Autonomy is low. Only 7% of scientists can configure assays or templates themselves, and just 5% can analyze experimental data without outside help.
• The bottleneck: Interpretation queues up with specialists. ~66% rely on IT/informatics for configuration, and a similar share depends on data scientists to interpret results more than a quarter of the time.
• The cost: Sixty-five percent repeat experiments because past results are hard to find, reuse, or trust.

Stage 2: The shadow lab (adaptive workaround)

Scientists outgrow passive tools and bolt on public generative AI to move faster. This "shadow AI" rises alongside the ELN but outside formal governance.

• The symptoms: AI use becomes the norm. 97% of scientists use some form of AI, and 77% use public tools like ChatGPT, Claude, or Gemini alongside the ELN.
• The risk: Governance weakens. Nearly 45% access these tools through personal accounts, pushing experimental context outside IT visibility.
• The gap: General AI isn't built for lab work. Only 27% say current generative tools meet scientific needs very well, and many call them a poor fit for lab workflows.

Shadow labs are adaptive but unstable. Scientific reasoning drifts outside validated systems while the ELN remains the official record.

Stage 3: The active lab (system of reasoning)

Here, AI isn't bolted on. It's embedded in a third-generation ELN that connects design, execution, and analysis. The notebook graduates from passive record to active co-scientist.

• The characteristics: The notebook participates in the loop. It helps form hypotheses, surfaces patterns across experiments, and links steps end to end in one environment.
• The mandate: Demand is overwhelming. 99% of scientists agree ELNs should act as intelligent research partners, and 96% say future systems must help interpret data, not just capture it.
• The trust requirement: Transparency is non-negotiable. 81% would only use AI suggestions if the underlying data and reasoning are reviewable.

Interpreting the maturity curve

Moving from passive to active isn't a routine upgrade. It's a structural shift in how reasoning is supported. Passive labs push interpretation into spreadsheets and specialist queues. Shadow labs push it into public AI.

Active labs pull interpretation back into the notebook, where context stays attached to data and methods. That context can be reviewed, audited, and reused. The result: fewer repeat experiments, faster cycle times, and institutional knowledge that compounds instead of evaporates.

A quick self-assessment for lab and informatics leaders

• Where does scientific interpretation actually happen today: in the ELN, in spreadsheets, or in external AI tools?
• How often do experiments get repeated due to missing, untrusted, or hard-to-locate results?
• Who configures assays and templates: scientists or specialist teams? How long do changes take?
• What share of AI use runs through personal accounts or public tools outside IT visibility?
• Can you trace any AI suggestion to the exact data, assumptions, and methods used?
• Are design, execution, and analysis connected in one place, or split across tools with manual handoffs?
• Do ELN, LIMS, and instruments exchange structured data with provenance and version history?
• Are auditability and GLP expectations reflected in AI workflows? See OECD GLP.

Practical next steps

• Close the loop: connect hypothesis, protocol, run data, QC, and analysis in a single notebook environment.
• Reduce handoffs: templatize common assays so scientists can configure and reuse them without tickets.
• Govern AI use: require account federation, project scoping, data residency controls, and full rationale traceability.
• Unify metadata and lineage: standardize entities, units, and versioning so results are searchable and comparable.
• Pilot with repeat-heavy workflows first: plate-based screens, QC checks, and standardized analytics pipelines.
• Upskill the team on AI-assisted analysis and prompt discipline for scientific contexts. If helpful, explore focused options such as the AI certification for data analysis.

About the research

Findings come from a survey of 150 scientists working in laboratories across the United States and Europe. Respondents spanned biopharma R&D, contract research, clinical diagnostics, and pharmaceutical manufacturing.

The study focused on how scientists use ELNs and AI tools in daily work, including usability, data analysis, experiment reuse, and behaviors such as public generative AI use. The survey was conducted in November 2025.