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Why CROs buy lab software differently

If you run a CRO, your lab works on someone else's science. Every sample that arrives has an owner, a study code, and a contract behind it. Every result you send back may end up in a sponsor's regulatory submission or in an investor's due-diligence review.

That changes what you need from an ELN and a LIMS. A biotech buying an ELN mostly asks, "Will my scientists use this?" A CRO has to ask that too, plus a few harder questions:

• Can we keep Client A's data away from Client B?

• Can we show a sponsor's auditor exactly who handled their samples, and when?

This guide walks through the questions to answer before you sign. It's written for small and mid-sized CROs and contract development and manufacturing organizations (CDMOs) doing discovery, analytical, bioanalytical and preclinical work. It also covers what changes if you operate in the United States, Germany or France. Where it helps, we show how teams set this up in SciSure, which combines ELN and LIMS capabilities.

Who this guide is for

This guide fits you if your lab:

• Runs discovery or in vitro studies for pharma and biotech sponsors

• Is a preclinical or GLP test facility

• Runs defined analytical methods for clients, where the method is fixed and the client, sample and study change

• Is a bioanalytical lab handling client samples

• Is a CDMO development lab doing process or analytical development before GMP (Good Manufacturing Practice) work begins

It fits less well if your main job is GMP quality control and batch release. Those labs usually need a GMP-focused LIMS with specification management, batch records, and release workflows. For clarity: SciSure ELN & LIMS isn't built as a GMP batch-release system.

1. How will you keep each client's data separate?

Most CRO contracts include confidentiality terms. Many sponsors will ask, during qualification or audit, how you prevent one client's data from being seen by another client or by staff who don't work on their studies.

Client separation usually works on three levels:

• Structure.  
  Each client, program or study gets its own space in the system.

• Permissions.  
  Staff see only the clients and studies they work on, with separate rights to view, edit, sign, and export.

• External access.  
  If a sponsor scientist or quality reviewer needs to see records, their access is limited to their own studies and can be removed when the project ends. This also might require a separate license per client.

With SciSure, a group is the secure collaboration boundary for a lab or team. Access runs through roles with separate View, Add, Update and Delete permissions, plus specific rights for sharing, signing and working with other users' records. When a role doesn't have a permission, the related buttons and menus are hidden.

A practical SciSure pattern for CROs is to structure work by client and study, using groups or projects as the boundary. Custom fields on projects and studies can carry client codes, contract references and study numbers, so every experiment and sample sits under the right client from the start.

What to ask for in a demo

‍Log in as a user assigned to Client A and search for a Client B sample. Then ask what an administrator, an exporter and a sponsor reviewer can each see. If your management team needs reports across all clients, ask how that works without opening up client data to everyone.

2. Can you trace every sample from receipt to report?

For a CRO, the sample is often the most valuable thing in the building. It may be irreplaceable; it belongs to the client, and its history is part of the data you deliver.

You should be able to answer these questions for any sample, quickly:

• When did it arrive, who received it, and in what condition?

• What is the client's ID for it, and what is yours?

• Where is it stored right now, down to the box and position?

• Which aliquots came from it?

• Which experiments used it, and what did they produce?

• Who took it out of storage, and when did it go back?

SciSure ELN & LIMS handles this through configurable sample types. Each sample type is a template with the fields your lab needs, including required fields, dropdowns, dates, links to other samples and auto-numbering. Samples carry an internal ID and can also carry an external barcode, so the client's label and yours can live on the same record.

From there, parent-child relationships record aliquots and derived samples. Storage units model freezers, racks and boxes down to the exact position. Check-in and check-out are available through the free Sample Check-out add-on. Each sample has an audit trail showing who changed what and when.

The link between samples and experiments runs both ways. An experiment lists the samples it used and generated, and each sample record shows the experiments it appeared in. When a sponsor sends a manifest of 400 samples, Sample Batch Import can bring them in from a spreadsheet rather than one by one.

What to ask for in a demo

‍Bring a real (anonymized) sample manifest. Ask the vendor to import it, assign storage positions, create aliquots, use two of them in an experiment, and then show you the full history of one sample.

For a comparison, check out our guide to the best sample management & tracking software.

3. Will your records hold up to a sponsor audit or an inspection?

Sponsor auditors and regulators look for the same basics. Who did the work? When? What changed afterwards, and why? Which version of the SOP was used? Who reviewed and approved it?

In SciSure, experiments are built from dated, timestamped sections, and each section keeps its own version history. Completed experiments can be signed and locked. If your group policy requires witness signing, the experiment goes to an assigned witness, who either approves and permanently locks it or declines it with a comment. Signed records stay readable but can't be edited, so amendments go into comments or linked copies, and the original stays intact.

Protocols and SOPs have their own versioning, signing, and publishing. When a scientist inserts a protocol into an experiment as a procedure section, the experiment records that specific version. One practical tip: insert the procedure rather than linking to it, because a protocol link shows the latest active version, not the version in use when the work was done.

Group policies can require digital signatures, witness signatures, password rules, and two-step verification. That lets a CRO apply stricter settings to GLP studies without forcing the same process on exploratory work.

One point every vendor should be honest about: software supports compliance, but it doesn't make a lab compliant. Your CRO still needs system validation, SOPs, training, quality assurance oversight, record retention, and change control. Our GxP compliance guide covers where that responsibility sits.

What to ask for in a demo

‍Ask the vendor to sign an experiment, have a witness decline it, correct it and sign again, and then show you the audit trail for the whole sequence. Ask what validation documentation they provide and what your team will need to produce itself.

4. Can you connect the science to what it costs?

A CRO's margin depends on knowing what a study really consumed: samples run, repeats, reagents, instrument time and analyst hours. In many CROs, that information lives in spreadsheets outside the lab system. Over time, quotes drift away from what the work actually costs, and nobody notices until a fixed-price project goes over budget.

When you evaluate an ELN and LIMS, look at how scientific records and cost-related information can sit together:

• Can you tag projects, studies and samples with client codes, purchase order numbers or cost-center codes?

• Can consumables be linked to the samples and studies that used them?

• Can instrument bookings be tied to a study?

• Can your finance or ERP (enterprise resource planning) system pull this data out, so nobody retypes it?

With SciSure ELN & LIMS, custom fields on projects, studies and samples can carry client and commercial references. Equipment records include booking calendars. Data can leave the system through Excel exports and the API, and automations can send webhook messages to other systems when records change.

5. Where will the data live, and who can reach it?

Client contracts sometimes specify where data may be stored and who may access it. Your IT team will have its own rules. And if you process personal data for a sponsor, data protection law adds more.

Hosting

‍SciSure ELN & LIMS is available as cloud, private cloud or on-premises. Public cloud customers choose an EU or US region. Private cloud offers dedicated resources and custom network restrictions, and on-premises keeps data on your own servers.

Identity and access

SciSure supports single sign-on (SSO) through SAML and identity providers such as Microsoft Entra ID, Okta and OneLogin, and two-step verification can be enforced at group or system level. Password policies, automatic logout, and account lockout after failed logins are configurable. For more details, check out our security documentation in our Trust Center.

Data protection

Under Article 28 of the GDPR, a processor can't engage with another processor without the controller's written authorization. If your CRO processes personal data on behalf of a sponsor, your ELN or LIMS vendor may count as a sub-processor. Check this with your data protection officer before you sign.

For a longer list of security questions, check out our guide on how to choose a data-secure ELN.

6. How will you report back to clients?

Most CROs deliver more than a final report; sponsors ask for raw data, sample lists, chain-of-custody records, and sometimes signed copies of experiment records. Check how easily you can:

• Export a full experiment record, with signatures and timestamps, as a PDF

• Export sample lists with only the columns a client needs

• Send data to a client's system through an API rather than email attachments

SciSure supports printing and exporting experiments, Excel export from advanced sample search, a Sample Export Column Selector add-on, and API access for system-to-system transfers.

7. What happens when a client's program moves into GxP work?

Your clients' needs change as their programs move forward. A discovery project becomes a GLP tox study. A process development project moves to GMP manufacturing. Decide early which work your ELN and LIMS will cover and where you'll hand off to other systems.

SciSure supports GLP- and Part 11-aligned workflows where configured and validated, and it's a good fit for discovery, analytical development and GLP study documentation. It isn't a GMP batch-record or QC release system. If part of your business runs under GMP, plan for how the two systems will exchange data.

HistologiX: From paper logs to searchable sample records

HistologiX, a UK clinical research organisation specializing in tissue-based analysis, used paper logs to track sample receipt, location and dispatch. As its client work grew, finding information meant searching those logs in person. The team adopted SciSure for Research to bring sample and stock information into a searchable system.

HistologiX now uses barcodes and scanners when checking samples in and out, locating them and recording disposal. Project managers and client account managers can also check sample and reagent status without asking a scientist to search the lab’s records. That visibility helps the team plan work and spend less time on sample administration.
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"Adopting SciSure has improved HistologiX’s operational efficiency, enabling us to streamline material receipt, stock management, and sample logistics.

The reduced administrative workload has allowed our scientists to focus on what they do best – applying our expertise in tissue-based analysis to advance our client’s cutting-edge drug discovery."

- Laura Hubbard, Operations Director
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Regional considerations: United States (GLP, Part 11 and contract labs)

In the US, 21 CFR Part 58 sets GLP rules for nonclinical laboratory studies that support applications for FDA research or marketing permits. Two parts matter directly for CROs:

• Not everything is GLP.  
  The definition of a nonclinical laboratory study excludes basic exploratory studies that test whether a compound has potential utility. Much discovery work falls outside GLP, so your system should make it clear which studies are GLP and which aren't.

• Sponsors must tell you.  
  Under section 58.10, when a sponsor uses a contract lab for part of a GLP study, the sponsor must notify the lab that the work is part of a GLP study. Your ELN and LIMS should make it easy to flag that status on the study and apply the right controls.

For electronic records, 21 CFR Part 11 section 11.10 lists controls for closed systems. These include limiting system access to authorized people, secure computer-generated audit trails with date and time stamps, and authority checks on who can sign or change a record.

What this means for your evaluation

‍Ask whether you can apply stricter settings to GLP studies only. With SciSure, group policies can require signatures, witness signatures, and two-step verification for specific groups, so a GLP group can run under tighter rules than a discovery group.

Regional considerations: Germany (GLP oversight, GDPR and cloud assurance)

In Germany, GLP principles are anchored in the Chemicals Act (Chemikaliengesetz, ChemG), sections 19a to 19d. According to the German Federal Institute for Risk Assessment (BfR), state (Länder) authorities inspect test facilities and check individual studies, while the GLP-Bundesstelle at the BfR coordinates GLP questions nationally and internationally.

For computer systems, German inspectors work from the OECD GLP documents. OECD Document No. 17 sets out a risk-based life-cycle approach to validating computerized systems, with a focus on data integrity. Its supplement on cloud computing, published by the BfR, says test facility management must control cloud suppliers and their subcontractors, and that written agreements should state whether parts of the service are subcontracted.

Two other points often come up in German procurement:

• GDPR (DSGVO).  
  The processor rules described above apply in full.

• BSI C5.  
  The Federal Office for Information Security (BSI) publishes the Cloud Computing Compliance Criteria Catalogue (C5), a set of minimum-security requirements for cloud services. The current version, C5:2026, was published in April 2026. Larger German buyers may ask whether a cloud vendor holds a C5 attestation or can map its controls to C5.

What this means for your evaluation

‍Ask for the vendor's written terms on subcontractors and hosting, and how its controls support your OECD No. 17 validation. SciSure offers an EU hosting region, private cloud and on-premises options, and a German-language interface.

Regional considerations: France (BPL oversight, health data hosting and language)

In France, GLP is called BPL (bonnes pratiques de laboratoire), and oversight is split by product type. According to COFRAC:

• ANSM (the national medicines and health products agency) inspects GLP for human medicines, cosmetics and tattoo products.

• ANSES, through its veterinary medicines agency (ANMV), covers veterinary medicines.

• COFRAC (the French accreditation committee) covers pesticides, food and feed additives and industrial chemicals, and makes GLP conformity decisions on behalf of the state.

COFRAC took over this role from the former interministerial chemicals group (GIPC) under Decree No. 2021-662. The ANSM says it inspects test facilities regularly, in principle every two years.

If your CRO handles personal health data, check one more rule. In France, a provider that hosts personal health data on behalf of someone else may need HDS (Hébergeur de Données de Santé, or health data host) certification. Version 2 of the HDS framework had to be in place for all existing certificates by 16 May 2026, and it tightens the rules on transferring data outside the EU. Most preclinical and analytical CRO work doesn't involve this kind of data. Bioanalytical labs handling clinical samples should check with their data protection officer whether any system that stores that data needs an HDS-certified host.

What this means for your evaluation

‍Confirm which authority oversees your studies, and whether any of your data would trigger HDS requirements. SciSure offers a French-language interface and an EU hosting region.

A quick checklist for your vendor evaluation

Where SciSure fits for CROs

SciSure ELN & LIMS is a good fit for CROs and CDMO development labs that need structured experiment records, client-level separation, detailed sample tracking and audit-ready documentation for discovery, analytical and GLP work. It works well when your sample types and client needs change often, because sample types, fields and templates are configurable rather than fixed.

SciSure, however, is not the right tool for GMP batch release or QC specification management. If that's the center of your business, you'll want a GMP-focused LIMS, and we'd rather help you figure that out early.

Frequently asked questions

What's the difference between an ELN and a LIMS for a CRO?  

An ELN documents the scientific work: what was planned, performed, observed and approved. A LIMS manages samples, storage, inventory and their history. CROs usually need both, and they work best when experiment records link directly to the samples they used.

Does a CRO need a GLP-validated ELN?

Only for work that falls under GLP. No software is "GLP certified" on its own. Your CRO validates the system for its intended use, following guidance such as OECD Document No. 17 and, in the US, 21 CFR Part 11. The vendor's controls and documentation make that validation easier or harder.

Can one ELN keep different clients' data separate?

‍Yes, if the system has strong permission controls and you set it up by client from the start. Test it during evaluation by logging in as a user from one client team and trying to reach another client's data.

Can GLP data be stored in the cloud?‍

Yes. The OECD's supplement on GLP and cloud computing accepts cloud solutions, as long as the test facility controls the provider, has written agreements in place and can show that archived records stay intact, controlled and readable.

Does SciSure replace a GMP QC LIMS?  

No. SciSure supports research, analytical development, and GLP-aligned workflows. It isn't built for GMP batch records or QC release.

If this sounds like the kind of support you need, get in touch with us. We'll walk you through how SciSure is built to fit your lab workflows, not the other way round.

Keep reading

• The 5 Best Electronic Lab Notebooks (ELN) in 2026 Ranked by Ease-of-Use & ROI: Based on Real User Reviews
• ELN Software Advantages: Who Gets The Most Out of an ELN?
• The Best Time To Implement an ELN? Before You Even Enter The Lab
• 6 Electronic Lab Notebook (ELN) Adoption Barriers & How To Help Your Research Team Overcome Them
• 7 Critical Features To Look For In A Sample Management Platform
• The Hidden Cost of Manual Lab Data Entry & Poor Sample Management

When we first published this guide in 2023, most labs were asking whether AI belonged in research at all. In 2026, the question is how to use it well. Researchers already use general-purpose assistants every day, and labs now need those tools to work with their own experiments, samples and protocols without losing control of the data.

This guide walks through ten practical steps to implement AI in a research lab, from choosing the right problem to validating results and connecting AI tools to your lab records. Along the way, we show where a structured electronic lab notebook (ELN) and laboratory information management system (LIMS) such as SciSure fit in, and where your team's own judgment still matters most.

Moving beyond buzzwords: A few definitions
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• Artificial intelligence (AI)
  ‍Software that performs tasks that normally need human judgment, such as recognizing patterns, making predictions or understanding language.
  ‍
• Machine learning (ML)
  ‍A branch of AI where models learn patterns from data rather than following hand-written rules. Used in labs for classification, prediction and anomaly detection.
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• Deep learning
  ‍A branch of ML that uses multi-layered neural networks. It powers most modern image analysis, protein structure prediction and language models.
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• Neural network
  ‍A model made of layers of connected nodes, loosely inspired by the brain, that learns complex relationships in data.
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• Big data
  ‍Data sets too large or varied to manage with spreadsheets and manual methods, such as sequencing runs, high-content imaging or years of experiment records.
  ‍
• Large language model (LLM) (new)
  ‍A deep learning model trained on large amounts of text that can answer questions, summarize and draft. ChatGPT, Claude and Copilot are built on LLMs.
  ‍
• AI agent (new)
  ‍An AI system that can take steps toward a goal, such as searching records, calling tools and combining results, rather than only answering one prompt.
  ‍
• Model Context Protocol (MCP) (new)
  ‍An open standard for connecting AI applications to external systems, such as databases and business software. It lets an assistant like Claude or ChatGPT retrieve information from a connected system in a consistent, controlled way.

Where AI stands in research labs in 2026

AI has moved from experiment to everyday tool, but trust and governance have not kept up. In 2024, the Nobel Prize in Chemistry went partly to the AlphaFold2 team, whose model predicted structures for virtually all of the roughly 200 million proteins researchers have identified. Most labs, though, use AI in far more ordinary ways.

Recent researcher surveys show the gap between use and confidence:

• Use has surged.
  In Wiley's ExplanAItions 2025 study of 2,430 researchers, AI use for any part of their work rose from 57% in 2024 to 84% in 2025. Most (70%) rely on freely available, general-purpose tools.
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• Concerns are rising too.
  Worry about inaccuracies and hallucinations rose from 51% to 64%, and privacy and security concerns from 47% to 58%. Only 41% said their organization offers adequate support.
  ‍
• Trust is the brake in industry R&D.
  In Elsevier's Corporate Researcher of the Future report, two-thirds of corporate researchers had used AI for work, but only 27% called AI tools trustworthy. Respondents said transparent citations (70%) and confidential handling of inputs (63%) would increase their use.

The technical risks named in the original post still apply. Models can inherit bias from their training data, and their predictions can be hard to interpret. The difference now is that the biggest practical questions sit around data quality, access control and validation. The ten steps below are built around those questions.

How to implement AI in your research lab: 10 steps

Whether you are building a custom model or connecting a commercial assistant to your lab records, the same rule applies: AI is only as reliable as the data, validation and governance around it. Bring in bioinformatics and data science colleagues early, and involve IT and quality teams before any tool touches lab data.

Step 1: Identify the problem or question

Start with a specific bottleneck, not with "we need AI". A clear question tells you what kind of tool you need and how you will judge whether it works. Common starting points in research labs:

Just keep in mind that partner add-ons are developed by third parties and may need separate licensing.

Step 2: Research available tools before building your own

For most lab questions, an existing tool is faster and cheaper than a custom model. Compare options on accuracy evidence, ease of use, support and documentation. Then ask the questions researchers worry about most:

• Where does your data go, and is it used to train the vendor's models?
• Does the tool cite its sources, so results can be checked?
• Does it respect your existing user permissions?
• Does it work with your hosting model and your lab software?

Build a custom model only when no existing tool fits your question or your data.

Step 3: Make sure your data is ready

Check your data's quality, volume, structure and possible biases. AI tools struggle with notes scattered across paper notebooks, spreadsheets and shared drives. The FAIR principles (Findable, Accessible, Interoperable, Reusable) are a useful benchmark because they focus on data that machines, as well as people, can find and reuse.

In practice, that means consistent names, required metadata fields, sample lineage and links between experiments and the samples they used or generated. With SciSure, teams can structure experiment documentation with templates and protocols, and define sample types with required fields and parent-child relationships with sample management. That structure is what makes lab records useful to AI later.

Step 4: Define the context of use and plan validation

Write down exactly what the AI output will be used for and what happens if it is wrong. A tool that suggests papers to read carries less risk than one that informs a go/no-go decision on a compound.

The FDA's draft guidance on AI to support regulatory decision-making sets out a risk-based approach: define the question, define the context of use, assess model risk, then plan how to show the output is credible. The guidance covers AI used to support regulatory decisions, not early discovery, and is still in draft. Even so, the same logic is a sound way to plan validation for any lab AI tool.

Include outliers and edge cases in your test plan, since that is where models tend to fail.

Step 5: Train or configure your AI tool

If you're building a model, train it on your prepared data and tune its parameters to minimize error on data it has not seen. If you are using an existing tool, configuration matters just as much. Set up prompts, templates and settings that match your lab's standards. For example, SciSure's AI Protocol Generator lets teams adjust the tone, structure and creativity of generated SOP drafts.

Step 6: Test and validate the results

Test on data the model was not trained on, and compare outputs with results you already know. For predictive models, track accuracy, precision and recall, and watch for overfitting. For language-based assistants, ask questions you can answer yourself, check every citation and confirm that answers match the underlying records.

Keep a qualified person in the loop. AI-generated protocols, summaries and suggestions are drafts until someone with the right expertise reviews them.

Step 7: Integrate AI into your lab workflow with governed access

AI is most useful when it can work with your own lab records, and most risky when it does so without controls. Before connecting any assistant, decide which data it can reach, which actions it can take and how access is authenticated.

With SciSure MCP, labs can connect compatible AI environments, such as Claude, ChatGPT or a locally hosted model, to SciSure Research. Access is read-only, every request is authenticated, and the AI only sees what the signed-in user is already allowed to see. Scientists can ask questions like "Do we have an existing protocol for this assay?" without exporting records. You can likewise record AI-assisted analysis in your ELN, including which tool and version you used, so results stay traceable.

SciSure MCP is available on Private Cloud and On-Premises installations. Setup guides are on the SciSure developer hub.

Step 8: Monitor performance over time

Model performance can drift as your data, methods and priorities change. Commercial AI tools also change when vendors update their underlying models. Set a review schedule, track key metrics and keep a simple log of issues, so you can spot when a tool stops meeting your needs.

Step 9: Update, retrain and revalidate

When performance drops or your needs change, retrain with new data, adjust parameters or switch tools. Treat each significant change like a method change: revalidate before relying on the results again, and document what changed and why.

Step 10: Build governance and compliance in from the start

Protect personal and patient data under regulations such as GDPR and HIPAA, and check whether AI touches records covered by 21 CFR Part 11 or GxP requirements. Set a clear lab policy on which data may go into which AI tools. For example, SciSure's AI Protocol Generator documentation advises against entering sensitive or confidential information, because its AI engine runs outside the SciSure environment.

For broader frameworks, the FDA and EMA published ten shared Guiding Principles of Good AI Practice in Drug Development in January 2026, covering a risk-based approach, data governance and life cycle management. The voluntary NIST AI Risk Management Framework and its Generative AI Profile offer practical guidance for any organization.

Software can support compliant AI use through permissions, audit trails and controlled access, but your organization still owns validation, SOPs, training and oversight.

Where SciSure fits in your AI plans

SciSure supports three layers of AI readiness in research labs:
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• A structured data foundation.
  SciSure ELN and LIMS capture experiments, protocols and samples as linked, searchable records with audit trails, so AI tools work from reliable data.
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• Governed access for the AI tools you choose.
  ‍SciSure MCP connects compatible assistants to permitted SciSure Research data, read-only and within each user's existing permissions.
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• AI tools inside the workflow.
  The SciSure Marketplace offers AI add-ons for protocol drafting, image analysis and literature evidence, alongside APIs and integrations for custom work.

IT teams reviewing AI connections can find hosting, security and integration details on the IT Professionals page.

AI is now part of everyday research work, but the labs that get real value from it start with the basics: a clear question, structured data, honest validation and governed access. Whether you build a custom model or connect a commercial assistant, those foundations decide whether you can trust the results.

FAQs

How do you implement AI in a research lab?

Start with one specific question, check whether an existing tool can answer it, and confirm your data is structured enough to use. Validate the tool against known results, connect it to lab records with controlled access, then monitor and revalidate over time.

What does a lab need before using AI?

A lab needs well-structured, consistently labeled data, a clear use case, a validation plan and a policy on which data may go into which tools. Involve IT and quality teams early, especially when AI touches regulated or confidential records.

How do you integrate AI with laboratory systems?

Most integrations use APIs or, increasingly, Model Context Protocol (MCP), an open standard for connecting AI applications to external systems. Whatever the method, the AI should authenticate as the user and only reach data that person is allowed to see.

Can AI assistants like ChatGPT or Claude access our ELN data safely?

They can when the connection is governed. SciSure MCP gives compatible AI environments read-only access to permitted SciSure Research information, applies each user's existing permissions and does not modify records. It is available on Private Cloud and On-Premises installations.

Is AI use in research labs regulated?

There is no single AI rule for research labs, but existing data protection and record-keeping rules still apply. For drug development, the FDA has draft guidance on AI used to support regulatory decisions, and the FDA and EMA published ten shared good AI practice principles in January 2026.

If this sounds like the kind of support you need, get in touch with us. We'll walk you through how SciSure's AI tools are built to fit your lab workflows, not the other way round.

Keep reading

• ‍Smart AI, Real Value: Innovation Without Compromise
• ‍Using ChatGPT Wisely with Your Lab Work
• ‍The Role of Human-Centric AI in Biotech Laboratories
• The 5 Best Electronic Lab Notebooks (ELN) in 2026 Ranked by Ease-of-Use & ROI: Based on Real User Reviews
• Best Sample Management and Tracking Software For Labs: 5 Platforms for Research Groups and Enterprise Organizations

So, you've finally secured investment for your biotech start-up. Congratulations!

Your laboratory is becoming operational, equipment is arriving, and you're still excited about everything ahead. Meanwhile, investors are already asking about milestones, even though half the equipment is still sitting in boxes.

Among the many decisions you need to make, there's another question: Do we actually need Electronic Lab Notebook (ELN) software and a sample management system?

Of course, better reproducibility and data organisation can save time and money. Many ELN providers also offer special programmes for start-ups. But perhaps it's too early? Why invest time in a system when you barely have any samples and your experimental workflows aren't even established yet?
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Spoiler: I think the earlier, the better. Ideally, before your first experiment.

I've experienced this from both sides. Before working with laboratories on ELN implementation, I was a scientist in a biotech start-up myself. Back then, I didn't know much about ELNs, but we had one from the very beginning.

Looking back, I'm very glad we did. Here are three reasons why.

1. Start-up life is busy enough without searching for everything

Working in a biotech start-up means doing a bit of everything. One day you're planning experiments, another day you're writing code, pitching for a company, ordering consumables, fixing equipment or preparing results for investors.

And, unlike in a well-established laboratory, there's often nobody whose sole responsibility is keeping everything organized.

During my first year at the start-up, I was splitting my time between computer simulations and laboratory experiments. Sometimes I would spend two weeks mostly working on simulations. When I returned to the lab, I could barely remember where everything was.

• Which freezer contained that sample?
• Which box?
• Which position in that box?
• And what was the last experiment I had started but hadn't finished?

Having a searchable system made a huge difference. Instead of interrupting a colleague who was just as busy as I was, I could find the sample location or check the experiment myself.

It sounds like a small thing. But when your team is small and everyone's time is limited, these small things add up surprisingly quickly.

2. It's much easier to organize a lab before it becomes disorganized

Implementing an ELN isn't just about installing software. Every laboratory has its own protocols, experimental workflows and ways of organizing samples.

Even if you're starting from scratch, you probably bring protocols from different academic laboratories. And, as we discovered, different scientists often have different ways of performing essentially the same experiment.

We had to bring our protocols together, discuss which approaches worked best and establish an experimental structure that made sense for our team.

We also had the opportunity to decide how our freezers, boxes and sample records should be organized before we actually filled them.
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We were already working in the ELN before running our first experiment.

Was it some extra work at the beginning? Certainly. But it saved us a lot of trouble over the following two to three years.

Everything had its place, both physically and digitally. New reagents could be registered as they arrived. New experiments followed an agreed structure. And when new colleagues joined, they didn't have to learn an entire system of unwritten rules.

There's another advantage to starting early: you don't need to migrate hundreds of existing samples or experiments. You simply add things as you go.

Now that I work on the other side, helping laboratories adopt ELNs, I see how much more complicated implementation can become when years of existing data and habits need to be accommodated.

3. Even a small laboratory generates data surprisingly quickly

It's tempting to think that a team of three scientists doesn't really need a dedicated system. But three scientists working full-time can generate quite a lot of experiments and samples.

In our start-up, we had two years to deliver MVPs for four different projects. For the first few months, there were only three of us working in the laboratory.

Even after the first rounds of experiments, we already had dozens of samples. Eventually, that number grew into the hundreds.

Imagine trying to introduce a sample management system at that point. You would first need to identify everything, reconstruct missing information, agree on naming conventions and enter all those records.

And you'd have to do all of this while continuing the experiments needed to meet your milestones.

Today, I sometimes work with laboratories that already have hundreds or even thousands of samples when they decide to implement a system. It always reminds me of my previous job and makes me appreciate how much effort we avoided by starting early.

The question isn't necessarily how many samples you have today. It's how many you'll have in six months, and whether you'll still be able to find and understand them.

So, is it ever too early for an ELN?

I don't think so.

Of course, a three-person start-up doesn't need the same complex infrastructure as a large pharmaceutical company. And spending months designing a perfect system before doing any science would rather defeat the purpose.

But having a simple, agreed way to document experiments, manage protocols and track samples from day one is a different matter.

It helps you stay organised when everyone is busy, makes onboarding easier and avoids the painful task of reconstructing everything later.
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The best time to organize your laboratory is when there's still very little to organize.

And if you haven't even unpacked your first piece of equipment, that might actually be the perfect moment to start.