AI Brings the Next Generation of the Electronic Lab Notebook
Industry Insight
Published: December 10, 2025
Most labs use an electronic lab notebook (ELN) to capture work, keep records audit-ready and make past experiments searchable. That alone beats paper. But the real opportunity is obvious: what if the ELN could help you plan, check and optimize work as you go-like a colleague who knows your SOPs and your data?
That's the promise behind AI-integrated ELNs such as Sapio Sciences' ELaiN. In conversations at Lab of the Future Congress Europe 2025, Rob Brown, PhD, head of the scientific office at Sapio Sciences, outlined how an AI "co-scientist" shifts the ELN from passive record-keeper to active partner.
Why ELNs Still Matter
Classic ELNs solved documentation. They made results findable and trustworthy. The bigger win is reuse: before you set up a new run, you can quickly see what's already been tried, what worked and what failed-cutting duplicate work and costs.
Modern ELNs also enable collaboration across teams and partners. Whether you're side-by-side at the bench or working with a CRO, shared context in one system keeps work aligned and moving.
What "Third-Generation" AI-ELNs Add
Natural language, no steep learning curve. Scientists shouldn't have to memorize complex workflows for every tool. With an AI layer, you write what you want to do, and the system figures out how to execute it in the ELN.
From passive to proactive. If the AI understands the scientific intent-not just the words-it can suggest synthesis routes, flag risks, propose sequence optimizations or generate analysis templates that reflect your SOPs. Think of it as a chemist or bioinformatician looking over your shoulder 24/7.
Less context switching. Instead of stopping mid-entry to chase a toxicity check or ask for a method, you ask the ELN. Answers appear in seconds, and you keep your train of thought.
How It Feels in Practice
You're documenting a plan to make a set of molecules. Midway through, you wonder about a liability. Rather than leaving the ELN, you ask, get a yes/no, see the rationale and continue. The flow stays intact.
For early-career scientists used to smart assistants, this feels familiar. Instead of complex forms, they ask clear questions, review the system's work and move on. Less training, faster impact.
Barriers to Adoption-and Practical Answers
Security. Many teams worry their questions will be sent to public models. With the right setup, prompts and data stay contained, with the same governance you expect from enterprise lab software.
Consistency. Variability is a fair concern. ELaiN's approach ties the AI's language interface to deterministic APIs, so identical inputs produce identical outputs. The system also shows how it reached a result: which algorithms were called, what sources were used and why the answer was chosen.
Validation. The AI is an assistant. Scientists remain accountable for the experimental plan, analysis and conclusions. The benefit is time: reviewing and tweaking a generated template is faster than building one from a 20-page SOP.
- Safeguards to look for:
- Private deployment options (on-prem or VPC) and access controls that align with your policies.
- Full audit trails linking prompts, data used, algorithms called and outputs generated.
- Deterministic back-end operations for searches, mappings and calculations.
- Human-in-the-loop review before anything becomes part of the record.
What to Pilot First
- SOP-to-template generation: Convert long methods into pre-populated, equation-ready templates; review and approve.
- In-ELN Q&A: Method selection, reagent checks, basic risk flags, sequence suggestions and reagent stoichiometry sanity checks.
- Data visualization: Auto-generate standard plots, tables and heatmaps with units and calculations embedded.
- Search and reuse: Ask for prior internal runs "like this one" and compare conditions, yields and deviations.
Implementation Checklist
- Define 3-5 high-impact use cases tied to SOPs and routine work.
- Connect the ELN to the data it needs: approved SOPs, reference libraries, instrument outputs.
- Set governance: who reviews generated templates, who approves changes and what gets auto-logged.
- Validate with golden datasets; document expected inputs/outputs and edge cases.
- Train teams to "ask like a colleague": specific goals, constraints and success criteria.
- Measure impact: time-to-plan, rework rates, duplicate experiments avoided and onboarding time for new scientists.
Addressing the AI Skills Gap
Putting an assistant inside the ELN gives teams a practical, low-friction way to build AI fluency. The guidance is simple: ask questions the way you would in a lab meeting, then review the system's work like you would a junior scientist's draft.
For broader upskilling beyond the ELN, you can explore role-specific AI learning paths that focus on hands-on productivity and data tasks. See curated options by job function at Complete AI Training.
What Good Looks Like
- Faster experiment planning and fewer context switches.
- Templates that reflect SOP intent and include built-in checks and calculations.
- Clear audit trails for every AI-assisted action.
- Lower duplicate work across teams and partners.
- Shorter ramp time for new scientists.
Context and Sources
ELNs remain the most widely adopted digital tool in R&D, according to industry surveys such as the Pistoia Alliance's Lab of the Future work. For background on adoption trends, see the Pistoia Alliance overview here.
Expert Perspective
Rob Brown, PhD, head of the scientific office at Sapio Sciences, emphasizes two shifts. First, lowering the learning barrier with natural language so scientists spend time on science, not on software. Second, turning past data into an active partner that suggests next steps with context, provenance and reproducibility.
The result is practical: better-designed experiments, fewer do-overs and analysis you can trust-without breaking your workflow.
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