Pharma companies sit on large amounts of unstructured information: field notes, call reports, medical inquiries, meeting summaries, free-text survey answers. Historically, most of it was read once and forgotten, because structuring it by hand doesn’t scale.
Generative AI changes that. It can summarise, classify and extract themes from text at a scale that wasn’t practical before. But in a regulated industry, how you introduce it matters as much as what you build. These are the questions I’d want answered before any GenAI use case goes live.
1. Is there a clear decision at the end?
The most common failure mode for GenAI pilots is a great demo with no owner. Before building, be clear on who will use the output, for which decision, and what they do today without it. If nobody’s workflow changes, the use case isn’t ready.
2. Is the human still in the loop?
For most pharma use cases, GenAI should support judgement, not replace it. Summaries and suggested themes should be reviewed by someone who understands the context, especially where outputs could influence medical, promotional or patient-facing decisions. Design the review step in from the start, not as an afterthought.
3. What data goes in, and is it allowed to?
Free-text fields often contain more than expected: names, health information, details about individuals. Before sending any data to a model, check:
- Does it contain personal or sensitive data, and is processing it permitted?
- Where is the model hosted, and does the data leave approved environments?
- Is there a data minimisation step (removing what isn’t needed)?
Privacy, legal and compliance teams should be partners from day one, not a final gate.
4. How will you know if it’s wrong?
Language models can be confidently wrong. Define how quality will be checked: a sample reviewed by experts, agreement rates with human classification, or clear criteria for what an acceptable summary looks like. Keep checking after launch; quality can drift as inputs change.
5. Is the output explainable to the user?
Users trust outputs they can trace. Where possible, link generated summaries or themes back to the source records so people can verify them. “Here’s the theme, and here are the notes it came from” builds far more confidence than a polished paragraph with no context.
6. Is it compliant with your industry’s rules?
Pharma has specific obligations around promotional content, adverse event reporting and medical information. A system that processes field or customer text must have a clear route for anything that triggers those obligations. This is non-negotiable and should be agreed with the relevant functions before go-live.
7. Can it scale beyond the pilot?
A pilot that works for one team often breaks at the next. Think early about ownership, monitoring, cost, and how the solution fits the existing data platform and governance. Treat it as a product with a backlog, not a one-off experiment.
GenAI won’t fix weak processes or poor data, but applied with clear ownership, human review and solid governance, it can turn information that was previously ignored into insight teams can act on.
The organisations that benefit most won’t be the ones with the most pilots. They’ll be the ones that learn to take the right few into production, responsibly.
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