Artificial intelligence is becoming increasingly common across the life sciences industry, but turning adoption into measurable business value remains a challenge. For pharmaceutical and biotechnology companies, the difficulty extends beyond implementing models: AI-generated scientific content must meet the same standards of accuracy, regulatory compliance and medical rigor as material produced by human experts.
Deloitte’s 2026 Life Sciences Outlook found that only 22% of surveyed life sciences executives had successfully scaled AI, while just 9% reported significant returns from their initiatives. Although organizations are experimenting with the technology, integrating it into established workflows remains difficult.
The challenge is particularly visible in scientific communication, where pharmaceutical companies produce clinical study reports, medical publications, product labeling and materials for healthcare professionals. Each document must pass through review processes designed to ensure that scientific claims are accurate, consistent and supported by evidence.
These issues were discussed at Articulate 2026, an executive summit hosted by Prezent Vivo on October 1 at the Museum of the American Revolution in Philadelphia. The event brought together biopharma leaders to examine how AI is changing scientific communication, medical affairs and the broader healthcare industry.
AI’s role in scientific communication across the life sciences
One of the summit’s sessions, “From Quill to AI: The New Craft,” examined where artificial intelligence can improve scientific communication and where human expertise remains essential. Moderated by Rebecca Goldstein of Abeona Therapeutics, the panel featured Julie Guery, Manish Tewani and Vivek Mukhatayar.
Guery, global head of regulatory affairs labeling at Sanofi, brought a regulatory perspective to the discussion. Her work involves labeling across therapeutic areas including immunology, rare diseases, oncology, vaccines and general medicine, covering products from early development through their full lifecycle.
Product labeling illustrates why scientific communication presents a particular challenge for AI systems. Pharmaceutical companies must ensure that information remains consistent across markets, while each claim must withstand regulatory scrutiny. Generating a document quickly does not eliminate the need to verify its content or secure the required approvals.
AI can nevertheless help reduce the time spent on repetitive activities. These include locating information in clinical study reports, reconciling document versions across markets and adapting scientific findings for different audiences, such as physicians, patients and payers.
For teams managing multiple products and regulatory jurisdictions, automating parts of this work could reduce administrative burdens and allow specialists to concentrate on tasks that require deeper scientific interpretation.
However, the panel’s central point was that efficiency cannot be the only measure of success. AI-generated content can sound authoritative even when a claim is unsupported or a qualification has been omitted. In a regulated environment, scientific and medical professionals remain responsible for determining whether the information is accurate and appropriate for its intended audience.
That distinction becomes especially important when communicating clinical findings. The placement of a caveat, the interpretation of a result and the emphasis given to a particular outcome can affect how healthcare professionals understand the evidence. These decisions require judgment that extends beyond producing fluent text.
Prezent Vivo CEO Rajat Mishra addressed the broader direction of the technology during the summit, predicting that “by 2030, AI will be the baseline.” If AI becomes a standard component of business operations, differentiation may depend less on access to the tools themselves and more on how organizations integrate them into their processes.
For life sciences companies, that means establishing review procedures, preserving accountability and ensuring that AI-generated material remains grounded in validated scientific evidence.
The discussion at Articulate 2026 highlighted a distinction that is becoming increasingly relevant across enterprise AI adoption: automating production is not the same as automating responsibility. In scientific communication, the technology can accelerate the work, but human expertise remains central to ensuring that the resulting information is reliable, compliant and useful to the people who depend on it.
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