Is Your Data AI-Ready? A Practical Checklist for Life Sciences Orgs

If you’re a life sciences org that has been treating AI readiness as a future problem, the future is now.

The decisions that determine whether your organization’s data can be used with AI in the future are made in the present, in master service agreements, software terms, consent forms, terms of service, and the like. You need to have the necessary permissions and provisions in place before you start pointing AI at your data.

At California Life Sciences, we work with companies at every stage, from two-person startups in the FAST California cohort to established members with decades of accumulated research data. The same pattern shows up across all of them: the barrier to entry for AI is not so much the technology as the availability of usable data for it to learn and make inferences from.

This is a practical checklist across six dimensions. Three concern whether you have the right to use your data, and three concern whether a machine can make sense of it. We’ll start with the rights questions, because that is where we see many life sciences organizations hit their first roadblock.

1. Owned: do you have the rights to use it?

Early-stage life sciences companies don’t typically have a wealth of “owned” data. Instead, data comes from contract research organizations (CROs), contract development and manufacturing organizations (CDMOs), academic collaborators, core facilities, and vendor software. These relationships carry terms about who owns what, and govern how each party is expected to act. These terms will often address publication rights, confidentiality, and increasingly, how (or if) data can be used with AI, including whether it can be shared with large language models (LLMs) like Claude, Gemini, and ChatGPT.

Three questions matter most:

• Who owns data your team may generate on a partner’s platform?
• Can the service provider use your data to train or improve their own products or models?
• Who owns what comes out the other side?

The last question is key, because in some AI partnerships the platform company retains rights to the model while the sponsor receives only the right to use the results.

The exposure runs in both directions. Early-stage companies send proprietary data to CROs and CDMOs by design, and those partners increasingly run AI systems of their own. Your data-handling terms with service providers are part of your AI position whether or not you ever build a model yourself.

2. Consented: does your consent language cover this use?

If your work touches human subjects, patient records, or biospecimens, consent is the constraint that no amount of engineering can fix later.

Much of the informed consent language in circulation was written for a different research model and a different time: a defined study, a named team, a specific disease area, and de-identification as the primary protection. AI work is different. It combines datasets across modalities, retains data indefinitely for retraining and validation, and produces applications the original protocol never contemplated, including target discovery, drug repurposing, and commercial licensing.

Broad consent and secondary-use provisions exist precisely to create room for future research, but they have limits, and those limits are set by what the participant was actually told. De-identification helps, though it is worth being clear that re-identification risk rises as datasets get richer and more linkable. A scoping review of patient consent for secondary use of health data in AI models found inadequate consent processes and unauthorized data sharing among the leading barriers to this work, alongside privacy and security concerns.

This is a question for your institutional review board (IRB) and your counsel, not one to resolve internally. The best move is to ask it early, while consent language is still being drafted, rather than discovering the answer too late.

3. Secured: can you use it without losing control of it?

Security in this context is not only about keeping data in. It is about knowing what leaves, and under what terms.

The everyday version of this problem is an employee pasting an assay result, a protocol, or a draft regulatory document into a public chatbot or LLM. Depending on the service and the account tier, that content may be retained and used for training. The same issue appears in enterprise form inside software contracts: a clause permitting the vendor to use customer inputs to improve its services can mean your proprietary data is used to train someone else’s model.

The fix is not prohibition but rather classification. Decide which categories of data can go to a third-party service and which cannot. It needs to be documented and understood so the team will choose tools whose terms match the classification. At a small startup, this job probably befalls the founder or founders.

4. Traceable: can you prove where it came from?

Even if it’s accurate, a number that can’t be attributed isn’t useful. Doubly so in regulated environments.

Traceability means knowing, for any given data point, which sample it came from, which instrument produced it, who touched it, and what was done to it along the way. In regulated work this is a formal requirement. 21 CFR Part 11 sets expectations for electronic records and audit trails, and data integrity practice extends the same logic across the lifecycle. Outside regulated work the requirement is less formal but no less real, because a model trained on data of unknown origin produces conclusions of unknown reliability.

The regulatory direction of travel is clear. The FDA’s draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, sets out a risk-based framework for establishing the credibility of an AI model for a specific context of use. Data provenance sits at its center. If AI-derived evidence is going into a submission, the agency expects you to be able to show where the training data came from and why it supports the conclusion.

Researchers are converging on the same point. The National Institutes of Health Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group has proposed seven criteria for AI-ready biomedical data, and provenance is one of them:

• FAIRness: the data is findable, accessible, interoperable, and reusable
• Provenance: its sources and transformations are documented and traceable
• Characterization: it carries descriptive metadata, data dictionaries, and an account of potential bias
• Ethics: it was acquired under sound governance, licensing, and consent
• Pre-model explainability: its documentation is readable by both humans and machines
• Sustainability: it is archived somewhere that will still exist and still be usable later
• Computability: it meets recognized standards and can move between systems

It is worth noting how much of that list comes down to governance rather than engineering.

5. Findable: can anyone actually get to it?

This is where we get into “data hygiene” and ensuring data is clean, reliable, and organized.

Notes on a lab computer or laptop that don’t talk to the LIMS, instruments that write to their own storage, a shared folder where organization is ad-hoc, a body of results living in spreadsheets on individual drives. It’s easy to lose oversight of data when everything seems to be happening at once.

The FAIR Guiding Principles, published in Scientific Data in 2016, remain the clearest external framework here.

FAIR principles

1. Findable: data and its metadata carry a persistent identifier and are indexed somewhere searchable
2. Accessible: once found, it can be retrieved through a standard protocol, with authentication where that is warranted
3. Interoperable: it uses shared vocabularies and formats, so it can be combined with other datasets
4. Reusable: it is described richly enough, and licensed clearly enough, that someone else knows what they may do with it

Data discoverability comes first on the list because data that can’t be found can’t be used.

You do not need to consolidate everything in the early stages, though it’s never too late to start. But the team needs to know where data lives in order to use it.

6. Structured: can a machine read it?

Data that a person can interpret but that a machine cannot is a common, and expensive, problem in the life sciences.

There are three main culprits:

• Proprietary instrument formats that require vendor software to open and can turn an analysis task into a licensing question
• Paper records, including the scanned PDFs of paper that are still the reality at many benches
• Inconsistent internal conventions, where the same compound carries four identifiers, units shift between experiments, and “result” means different things in different contexts

The best practice here is to capture metadata at the point of collection rather than reconstructing it later. Reconstruction is slow, error-prone, and can be impossible if the person who ran the experiment moves on. Controlled vocabularies and standard ontologies help, and adopting an existing one is nearly always better than inventing your own.

Where to begin for life sciences AI data readiness

Start with the contractual items because they can compound. A vendor agreement signed today can govern data for years.

Establish clear data handling and categorization practices that are outlined in SOPs. If data is scattered, organizing it and creating the shared repositories to ensure it stays organized is probably an OKR-level priority. These are clear opportunities to improve AI data readiness in the near term. Recovering rights that have been signed away, or obtaining consents that were never obtained, is a hairier issue.

Data readiness has become a diligence question in its own right. Partners ask where data came from and what rights are attached to it. Investors and acquirers examine data quality and provenance as part of assessing what a company is actually worth. Answering those questions well is now part of being a credible counterparty, and AI data readiness in life sciences is much easier to build than to retrofit.

If the vocabulary here is unfamiliar, our AI in life sciences glossary covers the terms in plain English, and our explainer on what AlphaFold actually changed shows what becomes possible when high-quality data meets a well-built model.