Using generative AI in research takes a practical approach
Generative AI can speed up some research tasks, but it also introduces risks that need to be managed in regulated environments.
Where it helps
Summarization
The clearest win. A model can condense a hundred page regulatory document or a stack of papers into a briefing a scientist can read in minutes, and then answer follow up questions about the source.
Drafting
Protocols, reports, documentation — first drafts that a person then edits rather than writing from blank.
Code generation for analysis
Writing the R or Python needed for a statistical test or a plot, saving time for scientists who are not primarily programmers.
Structured extraction
Turning free text into tables — a task models do well and humans find tedious.
Where it does not
Models can produce convincing text even when the information is wrong. In a research context, a fabricated citation, an invented dosage, or a misremembered mechanism is not a minor error.
Generative AI used in scientific work needs to be grounded in reliable information — retrieval from verified sources, human review before anything is relied on, and honest limits on what the model is asked to do unsupervised.
Data exposure is the other hard constraint. Proprietary research, patient data, and unpublished results cannot be pasted into a public model. Private deployment inside the organization's own infrastructure is the norm for anything sensitive.
Getting it into a regulated environment
Regulated processes need traceability. If a model contributed to a document that becomes part of a regulatory submission or a GxP record, the organization needs to be able to show what it did, on what data, and who reviewed the output.
The difficult part is usually the surrounding engineering and process, not choosing the model.
SequoiaAT applies generative AI to biological data analysis and research acceleration, deployed through a RAG framework designed so raw data never reaches the AI models directly.
The same approach works for research workflows: use reliable source material, keep sensitive data private, and have people review the output.