Where generative AI fits in pharma workflows, and where it doesn’t
Over the past year, I have had more conversations about generative AI than I can count. Teams across pharma are asking where it fits, how it can help them move faster, and where the risks might be hiding. The excitement is real, but so are the doubts.
I spend a lot of time with brilliant people who have deep expertise in medical affairs, market access, and clinical development. What I see is a shared goal to make better decisions sooner, backed by the right data and tools. Generative AI can help with that, but only in the right places. In others, it can slow you down or introduce risk you cannot afford.
Where generative AI shines
Generative AI is at its best when you need help exploring ideas, summarizing large amounts of information, or creating first drafts of content that would otherwise take hours. For example, I have seen medical affairs teams use it to pull together initial outlines for advisory board summaries or to draft plain‑language explanations of complex studies.
It is also powerful for quickly generating variations of existing materials. When your team needs to adjust a slide deck for different audiences or translate key messages into different tones, AI can give you options fast. That early acceleration can be the difference between seizing an opportunity and missing it.
In these cases, AI is not replacing expertise. It is giving your experts a head start, so they can focus on refining and validating rather than starting from a blank page. When used this way, it creates smarter healthcare strategy through innovation and helps teams deliver with confidence.
Where generative AI falls short
The limitations become clear when you move into areas that demand verifiable evidence or precise compliance. I have seen teams experiment with AI to draft regulatory documents or to summarize clinical data without human review. The results can look convincing but contain subtle errors or missing context that only a domain expert would catch.
AI models are trained on vast datasets, but they do not understand the stakes of a clinical decision or the nuances of a payer discussion. They cannot guarantee source accuracy, and they can introduce bias without warning. In workflows where data integrity, attribution, and compliance are non‑negotiable, generative AI must remain in a supporting role, not a deciding one.
This is why we often integrate AI into our consulting and software with guardrails. We make sure outputs are grounded in validated datasets, that traceability is built in, and that experts are always in the loop. Without those steps, you risk creating more problems than you solve.
Finding the right balance
The real opportunity is not to hand everything over to AI but to know exactly where it can help and where human expertise must lead. At VISFO, we look at each workflow and ask three questions. Does this step need creativity or speed more than certainty? Can AI accelerate without introducing risk? Will experts have the time and tools to review and refine?
When the answer is yes, we bring AI in to help. When the answer is no, we keep it out and build processes that put verified data and human judgment at the center. That balance is what gives teams clarity and keeps them moving with confidence.
Final thoughts
Generative AI is changing how pharma teams work, but not every part of a workflow is ready for it. The teams that benefit most are the ones who know where it fits and where it does not.
Used wisely, it speeds up progress, removes friction, and lets your experts focus on what really matters. Used blindly, it can erode trust and add noise. Our goal is to help you get it right, empowering our partners to improve health and well‑being while staying aligned and ahead of the curve. If you want to explore where AI could add value in your own workflows, we would be happy to show you how we approach it.