How to choose AI solutions for life sciences

Where does AI pay off first in life sciences, and why?

It pays off first where the work is reading and writing against a source of truth: protocols, study reports, submission modules, labels, batch records, deviations and promotional claims. These documents are long and full of cross-references, and every number in them has to match a table, a dossier or an approved text. A person spends most of the time finding and checking, not deciding. That is the part an AI agent can do quickly and consistently, if it shows where each value came from.

Regulators now say how they expect AI to be used. In January 2026 the EMA and the FDA published ten joint guiding principles for good AI practice in drug development. They cover the whole product life cycle and ask for human-centred design, a risk-based approach, adherence to GxP, a clear context of use and life-cycle management. In January 2025 the FDA issued a draft guidance with a risk-based framework for judging whether an AI model is credible for a defined context of use, when its output supports a regulatory decision. Work that drafts, checks and cites, with a qualified person signing, fits these principles most easily. That is why it is the safest place to start.

Which processes should we start with?

Pick a process that is high in volume, slow today and checked against documents you already control. The output should be a draft or a list of findings that a named person approves. Avoid starting with anything where the AI makes the final call. Good first candidates by function:

• Clinical development: protocol and clinical study report drafting, and daily Trial Master File completeness checks by site.
• Regulatory affairs: CTD Module 2 summaries, cited first drafts of answers to health authority questions, and tracking which agency changes affect which products.
• Labeling: finding every local label a core data sheet change touches, and proofreading artwork against the approved text in every language.
• Manufacturing and quality: deviation and CAPA investigations, batch record review by exception, and risk-based software validation, the approach the FDA set out for medical device makers in its Computer Software Assurance guidance (final, February 2026).
• Medical affairs and commercial: checking promotional claims against their references before MLR review, and cited replies to medical information questions.
• Market access: value dossiers in which every sentence is linked to its source and checked against the label.

What should we demand on accuracy, citations and human sign-off?

Demand a citation on every value. Every number, claim and conclusion should link to the exact passage, table cell or record it came from, so a reviewer checks it in one click instead of re-reading the source. An answer without its source cannot go into a submission, a batch file or a promotional piece.

Demand named human sign-off. The AI drafts and checks, and a qualified person approves anything that leaves the team. The record should show who signed, when and on what evidence. Ask for accuracy measured on your own documents against an answer key your experts wrote, not a vendor benchmark. Ask also how that accuracy is watched after go-live. The EMA and FDA principles of January 2026 ask for risk-based performance assessment and life-cycle management. A vendor that cannot show you both has not built for this industry.

What about security, deployment and our data?

GxP records, unpublished trial data and commercial terms need the same controls as any validated system. Ask for: encryption in transit and at rest, isolation between customers, sign-in through your identity provider, role-based access down to each dataset, and an audit log that cannot be altered and records every human and agent action. Also ask where the data lives and whether it is used to train models.

Botminds runs as multi-tenant SaaS, in a dedicated cloud environment or fully on-premises, with the same controls in each. Data is encrypted with TLS in transit and AES-256 at rest, and each customer is isolated. Your inputs and outputs are not used to train models. The platform holds SOC 2 Type II and ISO 27001, is built for GDPR, and supports SSO and SAML, role-based access and immutable audit logging.

Rules for AI in manufacturing are still being written. From July to October 2025 the European Commission consulted on drafts, prepared by EMA inspectors with PIC/S, of a revised EU GMP Annex 11 (computerised systems), a revised Chapter 4 (documentation) and a new Annex 22 on artificial intelligence. Choose a vendor whose controls already match the direction of those drafts: a defined intended use, testing, human oversight and change control.

How do we run a pilot and measure it?

Run the pilot on one process, with real documents and the people who do the work. Write down the baseline first: hours per document, review cycles, findings missed and time to close. Have your experts build an answer key for a sample, and score the AI's output against it field by field. Count only what the reviewer accepted without changes.

Keep it short and decide in advance what counts as a pass. A good pilot ends with three numbers: accuracy against the answer key, time saved per item after review, and the share of items that went through without rework. If a vendor needs months of custom work before you see the first output, treat that as a finding in itself. Botminds builds a solution from your specification and deploys it once its functional and non-functional quality gates pass, so you measure working software rather than a prototype.

• Baseline before you start: time, cycles, errors.
• An expert-written answer key on a sample of your own documents.
• Field-level accuracy, not an overall impression.
• Reviewer acceptance rate and rework rate.
• A pass mark agreed before the first run.

Should we build, buy a point tool or use a platform?

Building in house gives control, but you then own validation, model changes, citations, audit trails and every new use case yourself. Point tools solve one document type well, but each one brings its own vendor review, its own validation and its own data copy. Ten of them become ten audits.

A platform gives you one set of controls, one audit trail and one validation approach for many processes. A new process becomes a configuration on something already reviewed, not a new procurement. Botminds is a platform of this kind: its life sciences library holds 129 solutions across 16 divisions, from research to finance, and each runs on the same governed engine. Whichever route you choose, ask what it costs to add the fifth and the tenth process, not only the first.

What changes for the team?

People move from writing and searching to reviewing and deciding. A medical writer edits a cited draft instead of building it from the tables. A QA reviewer opens only the batch entries that break a rule. A regulatory lead compares one drafted answer with everything the company has told other agencies. The expertise stays with the people, and the job is to judge, correct and sign.

Plan for this. Update SOPs to say who reviews AI output and how, train reviewers on what to check, and keep the sign-off with the same qualified roles as before. The EMA and FDA principles of January 2026 call for multidisciplinary expertise and clear, plain-language information about what each AI system does. Reviewers can only judge and correct AI output when they can see the source of every value.

What does it cost to wait?

The cost of slow document work is measurable. In a Tufts CSDD study published in 2024, 76% of protocols had at least one amendment, up from 57%. The mean number of amendments per protocol rose to 3.3. An amendment took an average of 260 days from the moment the need was found to the last oversight approval. Earlier Tufts CSDD work, published in 2016 on a smaller sample, put the median direct cost of one substantial amendment at $141,000 for a phase II protocol and $535,000 for a phase III protocol.

The rules are moving too. The principles and Annex 1 of ICH E6(R3) good clinical practice took effect in the EU on 23 July 2025. EU GMP Annex 22 on AI is still a draft. Under Regulation (EU) 2026/1744, in force since 27 July 2026, the EU AI Act's obligations apply to stand-alone high-risk AI systems from 2 December 2027 and to high-risk AI built into regulated products from 2 August 2028. Teams that start now learn to validate and govern AI before those dates arrive. Teams that wait will be learning it under deadline.

Solutions to look at first

• Clinical Protocol AuthoringA medical writing solution for medical writers that drafts trial protocols in your template.
• Clinical Study Report AuthoringA clinical study report solution for medical writers that drafts results from the tables.
• eTMF Quality CheckA Trial Master File solution for clinical operations that files trial documents and lists gaps.
• CTD Module 2 AuthoringA regulatory writing solution for submission teams that drafts clinical and quality summaries.
• Health Authority Query ResponseA regulatory affairs solution for submission teams that drafts answers to regulator questions.
• Regulatory IntelligenceA regulatory watch solution for regulatory affairs that explains what agency changes mean.
• CCDS Label Change ManagementA labelling solution for regulatory affairs that updates local labels after a CCDS change.
• Labeling & Artwork ProofreadingA pharma artwork proofreading solution that checks cartons and leaflets against approved text.
• MLR Review AutomationA promotional review solution for MLR teams that checks claims, references and fair balance.
• Medical Information ResponseA medical affairs solution for MI specialists that drafts cited replies to HCP questions.
• Deviation & CAPA ManagementA GMP investigation solution for QA that gathers evidence and proposes root causes and CAPAs.
• Batch Record Review by ExceptionA GMP batch review solution for QA that shows only the batch entries that break a rule.
• Computer Software AssuranceA GxP validation solution for validation teams that builds risk-based tests and trace matrices.
• Global Value Dossier AuthoringA market access writing solution for HEOR teams that drafts the GVD with every line sourced.

Every life sciences solution →

A checklist for any vendor

1. Can every value in the output be traced to the exact passage, table cell or record it came from?Yes, in one click from the screen where the reviewer works, for every field, not only a document-level reference list.
2. Who signs, and what does the record show?A named, qualified person approves every output that leaves the team, and the record shows who, when and on what evidence, in an audit log that cannot be altered.
3. How was accuracy measured, and on whose documents?Field-level accuracy against an answer key your experts wrote, on your own documents, reported before go-live and watched after it.
4. How does your approach fit GxP validation and a risk-based assurance approach such as the FDA's Computer Software Assurance guidance?A defined intended use, a risk-based test plan, objective evidence and change control for model and configuration changes, and help building the validation package.
5. Where can it run, and where does our data live?A choice of multi-tenant SaaS, a dedicated cloud environment or on-premises, with the same controls in each, and data residency that meets your policy.
6. Are our inputs or outputs used to train any model?No, stated in the contract, with model choice and configuration under your control and recorded.
7. What independent security evidence can you share?A current SOC 2 Type II report, ISO 27001 certification, penetration testing, encryption at rest and in transit, SSO, and role-based access to each dataset.
8. How long from our specification to working software on our documents?A committed date for working software on your own documents that you can measure against your baseline, not months of custom build before the first output.
9. What does the fifth process cost compared with the first?Much less, because it reuses the same controls, audit trail and validation approach, rather than a new tool with a new vendor review.
10. How do you follow changing rules such as ICH E6(R3), EU GMP Annex 22 and the EU AI Act?A named owner, a record of what changed and when, and controls already aligned with the EMA and FDA good AI practice principles.

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