For years, the conversation around AI in pharma was focused on potential. Could it accelerate R&D? Could it reduce manual work in pharmacovigilance? Could it help medical affairs teams handle growing volumes of requests?
Today the question has changed. AI is already here. The real challenge for life sciences leaders is whether they can scale it without creating compliance risks that undermine trust, brand reputation, or even the right to operate.
At Newpage, we work with pharmaceutical and biotech teams that are eager to use AI but also aware that regulated industries cannot afford to “move fast and break things.” Compliance is not a box to check at the end. It is the foundation that determines whether your AI programs are safe, auditable, and sustainable.
Here are a few realities that leaders sometimes overlook:
Regulators are paying close attention. The FDA and EMA may not have finished writing the rulebooks for GenAI, but expectations are already clear. Documentation, human oversight, and explainability are required, not optional.
GxP still applies. Whether AI is used in medical information, clinical operations, or pharmacovigilance, good documentation practices and audit trails are still the law of the land. AI does not get a free pass.
Validation is about limits, not perfection. Regulators do not expect your model to be flawless. They expect it to operate within clearly defined boundaries, with risks documented and tested.
Drift happens. AI models change over time. Without regular monitoring, retraining records, and version control, what was once accurate can quickly become unreliable.
Human oversight is non-negotiable. AI can assist, but in life sciences it cannot fully replace expert review. Every critical decision point requires a person in the loop.
The message is simple: compliance is not slowing you down, it is what makes scale possible. Without trust from regulators, healthcare professionals, and patients, even the most sophisticated AI tools will fail to deliver value.
In our latest blog, we share a practical framework for implementing AI in life sciences that is both innovative and compliant. We cover how to monitor drift, validate workflows, maintain explainability, and partner with vendors who understand regulatory expectations.
👉 Read the full article here: What Every Pharma Leader Needs to Know About AI in Life Sciences and Compliance
The companies that succeed with AI in 2025 will not be the ones that move the fastest. They will be the ones that balance speed with structure and innovation with governance. That balance is what builds lasting impact in pharma.
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