Welcome back for the final instalment of the ELN series! In Part 1 we covered what an ELN is, in Part 2 we explored who uses them, what types exist, and why, and in Part 3 we tackled the (potentially!) messy reality of implementing one.
Now for the big finish: where are they heading?
The ELN you implement today won't look anything like the ELN you'll be using in five years, and the organisations that understand that will have a significant advantage over those who don't.
The global ELN market was valued at approximately $750 - 805 million in 2025/2026 [1] [2]. Depending on which analyst you ask, it's projected to reach somewhere between $1.1 billion and $1.5 billion by 2035, growing at a Compound Annual Growth Rate (CAGR) of roughly 5 - 7% [1] [2] [3].
And there’s more. The cloud-based ELN segment is growing significantly faster, with one forecast projecting a 12.6% CAGR from 2026 to 2036 [4]. That tells you where the market’s head is at; cloud-native SaaS-first platforms.
What’s the reason? It's being driven by life sciences organisations transitioning towards AI-enabled, interoperable, and cloud-native research environments [3]. The ELN is now infrastructure.
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Did you know? 💡 The old-school method was literally stapling! Before ELNs, scientists would print out instrument data and physically staple it into their paper notebooks. ELNs replaced this by allowing direct incorporation of instrument data into digital records [5]. That's a lot of choice, and a lot of vendors fighting for your attention. The market is consolidating around platforms that do more than just "notebook," which brings us to... |
ELNs have gone through distinct generational shifts, and understanding the current state of that evolution helps explain where ELNs might be going. One useful framework breaks it down like this [6] [7]:
Generation
1st Gen (2000)
What It Did
Paper-on-glass. Capture, document, comply. Essentially a PDF of your paper notebook.
Generation
2nd Gen (2005)
What It Did
Structured + unstructured data. Scientists can now template their processes.
Generation
2.5 Gen (2015)
What It Did
The integration era - ELN + LIMS + SDMS convergence. Platforms started talking to each other.
Generation
3rd Gen (2025)
What It Did
The AI Lab Notebook (AILN). A co-scientist that can advise on experiment design, creation, and analysis.
The third generation has brought the biggest shakeup yet; agentic AI. These systems can understand scientific context, help plan and design experiments, analyse results, and ensure everything is documented, repeatable, and compliant [7] [8].
The ELNs of today are already using AI to save time: auto-generating experiment summaries from structured data, suggesting protocol templates, and enabling natural language search across your entire notebook history [9]. The ELN of tomorrow might just help you figure out what to do next.
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Did you know? 💡 The term "AILN" (AI Lab Notebook) is starting to gain traction as a distinct category. One vendor describes their 3rd-gen ELN as "a co-scientist that accelerates discovery, automates the grind, and frees scientists to focus on breakthroughs" [10]. |
If AI is the engine, then FAIR data is the petrol. You wouldn’t put diesel in an unleaded engine (especially at these prices), so you shouldn’t feed your AI non-FAIR data either.
FAIR stands for Findable, Accessible, Interoperable, and Reusable.
Why does this matter for ELNs? Because your notebook data is only as valuable as its reusability. If your experiment records are locked in proprietary formats, buried in unstructured text, or missing critical metadata, stacking AI on top of it won’t help you at all.
The good news is that ELNs are uniquely positioned to embed FAIR principles directly into everyday scientific workflows [12]. When a scientist fills in a structured template, links samples to experiments, and tags their work with standardised metadata, they're making their data FAIR without even thinking about it.
And this isn't just a nice academic ideal. Open science mandates from the National Institutes of Health (NIH) [13] and the EU are increasingly requiring FAIR-compliant data management. The organisations that build FAIR into their ELN workflows now will have a massive head start when these mandates tighten further.
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Did you know? 💡 The FAIR data principles were first formally published in 2016 [11], which is a full decade before the current AI wave! They were designed with machine-actionability in mind, and the researchers who wrote them were quite literally building the foundation for the AI-powered lab of the future (before most people had heard of ChatGPT)! |
So ELNs are perfect right? Well, there are still real challenges that the ELN world needs to solve:
Adoption resistance remains a human challenge
A 2026 survey found that 87% of researchers still use spreadsheets and 85% still use paper notebooks [14]. As covered in previous articles ELNs have to challenge a culture, and a way of working.
The bench-to-desk gap
Scientists must remove their gloves and physically leave the bench area to input data. Research has found that pharma scientists delay documentation by up to three weeks and waste an average of 30 minutes a day [15] transcribing paper notes into their ELN!
Cost and ease-of-use
These remain the top barriers to adoption, particularly for academic labs with limited budgets [16]. User-friendliness was rated as the most important requirement by 90% of researchers surveyed [14].
Integration complexity
Connecting ELNs to instruments, LIMS, SDMS, and other lab systems is still harder than it should be. Successful integration demands disciplined execution across technical, organisational, and regulatory dimensions [17].
Data migration
What happens to years (or decades) of paper records? The answer is usually not much, which is a problem if you’re trying to build comprehensive and searchable data histories.
These aren't reasons to avoid ELNs, just reasons to choose the right one, implement it properly (see Part 3!), and work with people who understand the challenges.
Where does all of this lead? Here's what the newest ELNs are currently offering:
1. Mobile-first and voice-enabled ELNs
So long, previously mentioned bench-to-desk gap! ELNs now work on tablets at the bench, accept voice input while your hands are gloved, and sync with the desktop experience.
2. AI-driven experiment design and protocol optimisation
Beyond documentation, ELNs are actively suggesting experimental approaches based on your lab's historical data, published literature, and real-time results. The ELN becomes less of a record and more of a research partner.
3. Deeper integration with knowledge graphs and scientific data clouds
ELNs can connect to centralised, FAIR-compliant knowledge graphs that make your organisation's scientific data searchable, linkable, and reusable [18]. You could ask your ELN if anyone in your organisation has performed an assay with specified parameters, and receive an accurate answer immediately.
4. The ELN as the central nervous system of the lab
Not just a notebook or data store, the ELN is now the hub through which instruments, LIMS, SDMS, inventory systems, and AI tools all connect. Lab informatics systems are already becoming part of the operating architecture of modern laboratories, [19] with the ELN naturally at the core.
5. Regulatory frameworks evolving alongside AI
As AI-generated records become more common, regulators are also updating their guidance. FDA 21 CFR Part 11 for example was written for a world of electronic signatures and audit trails. Now, AI-generated experiment summaries have encouraged regulators to re-assess what's valid, and ELNs built with compliance in their DNA will adapt most smoothly.
Now that you've spent all this time reading how beneficial ELNs can be for your lab, let me be the first to tell you... there is another way, and it's called a data fabric.
So, what is a data fabric? Julia already covered that in part 4 of her LIMS series, but I'll reiterate here briefly. Imagine all of the applications you use to do your job daily; your LIMS, your ELN, LES, SDMS, ERP... when these are all separated, you lose a lot of time moving between systems. Even if it's in your muscle memory, it loses you time that would almost certainly be better spent elsewhere.
So how does the data fabric solution help? Imagine all of those previously mentioned applications sitting on one layer. All of their information accessible in one place, with one single login - now everything you have permissions to access, can be accessed from one single place.
But is this all achievable without tons of developer work? This is where low-code interfaces come in - they exist to bridge this gap by providing a highly-customisable user interface, manipulable with little-to-no code knowledge, providing you with guard railed workflows that evolve with your sites, rather than independently of them.
Now, rather than having an ELN which does most of what you need it to, imagine a customisable interface, controlled by you, that not only provides all the data you need from your ELN, but from every other application you use as well. You won't have an ELN anymore, but you'll have all of it's capabilities, and then some!
Part 2: The Who, What and Why of ELNs
Part 3: How to Implement an ELN
Part 4: Where Are They Heading?
We've covered a lot of ground across these four articles, and if there's one takeaway from this entire series, it's this: don't wait for the perfect ELN – it doesn't exist! What exists is a growing market of tools that, when chosen wisely, can transform how your lab captures, shares, and builds on scientific knowledge!
The question isn't whether you'll switch. It's whether you'll make it on your own terms, whether you'll be forced into it by a mandate, an audit finding, or because I asked you very nicely. So whether you're a 10-person academic group, or a 500-person pharma R&D site, with these articles hopefully you know somewhere out there there’s an ELN that’ll work for you.
As always, if you need help with any stage of your ELN integration, want to hear our own take for a more digital solution, or if you simply want to talk to someone about issues you're experiencing in your lab, please click here to book a meeting with us to discuss!
Thank you for reading the whole series, hopefully it's been useful!
See you, space cowboy...
- Nathan and the ADIGILEAP team.
[1] https://www.towardshealthcare.com/insights/electronic-lab-notebook-market-sizing
[2] https://www.fortunebusinessinsights.com/electronic-lab-notebook-market-116517
[3] https://www.expertmarketresearch.com/reports/electronic-lab-notebook-market
[4] https://pr.report/bSP2TcO0
[5] https://www.nature.com/articles/nmeth.1631
[7] https://www.sapiosciences.com/news/sapio-sciences-introduces-worlds-first-3rd-generation-eln/
[9] https://genemod.net/blog/electronic-lab-notebook-software-in-2026-what-is-changed
[10] https://www.sapiosciences.com/products/electronic-lab-notebook/
[11] https://www.nature.com/articles/sdata201618.pdf
[12] https://ncl.dmponline.dcc.ac.uk/plans/193023/export.pdf
[14] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355558
[16] https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-017-0221-3.pdf
[17] https://www.excelra.com/blogs/eln-lims-strategy-success/
[18] https://www.technologynetworks.com/genomics/blog/from-elns-to-ai-laboratory-notebooks-409221
[19] https://www.intelmarketresearch.com/blog/1262/ai-enters-the-lab-informatics