Targeting Efficiency and Accuracy with AI in Life Science Marketing

A common concern I’ve heard from life science marketing leaders about adopting AI is whether the accuracy of the content it creates can be trusted and whether it will be as well accepted as that produced by a human alone.

Why the symbiotes win

Life science marketing demands precision and creativity. Content must be factually sound, scientifically accurate, and aligned with regulatory requirements. Despite these constraints, content must still capture attention and emotionally move audiences. 

Recent studies have shown that humans working in partnership with AI (human-AI symbiotes) can create medical copy 70% faster than unaided medical writers and achieve identical success rates in clearing medical, legal, and regulatory (MLR) reviews. AI-only approaches had a mere 20% success rate in clearing MLR reviews.

Interestingly, the human-AI symbiote’s advantage is consistent across multiple scenarios - while AI can accelerate analysis and output generation, human professionals remain essential for guiding direction and output quality.
In spite these benefits, I’ve frequently heard peers expressing concerns about:

• Factual errors – AI can generate text that sounds correct but contains subtle inaccuracies, particularly in technical subjects like lab techniques and clinical data.
• Lack of contextual understanding – AI struggles with scientific nuance and the latest research, sometimes misinterpreting data.
• AI hallucinations – AI models have been known to fabricate plausible but false information, posing a serious risk in regulated industries.
• Brand voice and specificity – AI-generated content may feel generic, failing to capture a brand’s unique positioning and messaging.

These challenges can all be resolved, but also underscore the need for human oversight.

As one marketer I spoke with recently told me: “AI is great at catching human errors, but ironically only humans seem to be able to catch mistakes made by AI.” 

Best Practices for AI-Augmented Content

To navigate these challenges, life science marketers need to adopt a strategic approach: AI serves as an aid, but human expertise (for now at least) needs to remain the final arbiter. Here are key best practices:

• Keep a human in the loop – Always have scientists, medical writers, or subject matter experts review AI-generated content for accuracy and compliance.
• Use AI for low-risk tasks – AI effectively generates content outlines, alternative phrasings, or headline variations, but final execution decision should remain human-led.
• Fact-checking and validation – AI-generated drafts should be cross-referenced with peer-reviewed research, product manuals, and regulatory guidelines.
• Fine-tune – Some companies train AI on their own proprietary datasets to improve relevance and accuracy, though this requires careful validation and security/privacy assessment.

AI offers immense potential in life science marketing, but its accuracy and quality limitations require careful management. By combining AI’s efficiency with human expertise, marketers can create content that is both scalable and scientifically rigorous.

The key is balance - leveraging AI for productivity while ensuring final outputs are precise, credible, and brand-aligned.