Life sciences commercial teams and their compliance colleagues are bracing for a content supply shock. The intense pressure to produce more content, faster, and at higher levels of personalization creates “always on” digital hubs, driving continuous specialized content and a renewed pursuit of utopian omnichannel engagement.
The catalyst of course, stems from generative AI’s ability to dramatically reduce content production costs. Unsurprisingly, pharma and medtech marketing teams now cite content creation as a top use case for generative AI,1 with some organizations allocating up to 20% of commercial budgets to AI.2 In Vodori’s own surveys, a quarter of our customers report use of AI for content creation.3
As adoption grows, the obvious question looms: How’s it all going? And more specifically, what are the downstream effects of the AI supply shock on MLR teams and processes?
AI makes it incredibly easy and fast to develop “good enough” content that previously required a human writer up to 4.7x longer to produce.4 Seemingly bliss for every marketer under pressure.
AI-generated content, while linguistically polished and superficially credible, frequently sounds, well… superficial. Instead of communicating with differentiated, truly creative narratives, the emerging standard feels synthetic, inauthentic.
If every organization uses similar tools in similar ways (insert one anchor piece, generate dozens of derivatives), the messaging converges to a level of indistinguishable monotony. Messaging and headlines become interchangeable. The role of the human writer shifts -- at best from creator to editor, at worst gets removed entirely.
Let’s add in one more uncomfortable truth: We still don’t know what content drives performance. The downstream effect is predictable: more content, less distinctiveness, and an exacerbated challenge to decipher impact or performance.
In addition to the inundation of “good enough” content, AI introduces another wrinkle as it drafts copy that strays from the facts in subtle but material ways.
Despite everyone’s best efforts, AI models continue to hallucinate. A 2026 benchmarking study across 37 large language models found hallucination rates commonly exceeding 15–30%, even among leading systems.5 Nearly half of marketers surveyed encounter AI inaccuracies several times a week, with over 70% of those spending hours fact-checking each week.6 Veering outside of MLR and life sciences commercial content for a moment, the legal world provides a helpful lesson. Consider the high profile US law firm that apologized for misquoting US bankruptcy code and incorrectly citing cases in a filing made on April 9, 2026.7
The toughest part to manage as humans is that AI is simultaneously wrong and convincing. Semantic drift is subtle and there’s real risk in content that overstates, reframes, omits, or blurs what can actually be substantiated. Phrasing like “may cause” versus “might cause,” or “associated with” versus “linked to,” materially changes claims. When inconsistencies are hard to detect and models confidently and eloquently overstate the truth, subtle wording differences are more likely to enter the MLR review process. Best practices demand codification of permitted promotional language and yet, AI excels at introducing outputs that subtly and often imperceptibly test these standards.
AI drives ever-growing content volume, making it trivial to generate nearly endless variations of the same content: ads, social posts, localized version, derivatives, etc. Often, each of these content types enters the same review workflows as the anchor materials. Commercial and compliance teams that cannot effectively triage risk and blend best practices with human accountability will increase compliance risk, impede the organization’s ability to communicate, or both.
We often think of taking a risk-based approach to quality systems, dragging along appropriate levels of governance and oversight. The same needs to happen within the software.
Thus the second key theme: more isn’t better; that we can, doesn’t mean that we should. If the cost to produce drops precipitously, we end up with photocopies of bad originals flooding the review. Here’s a fun new term: Generation Loss. This is the phenomenon where each successive photocopy is degraded from the original and it occurs because each copy introduces, accumulates, and compounds subtle distortions, noise, or reduction in quality, making the copy of a copy less legible over time.8 Sound familiar?
The ensuing review frenzy leads to more circulations and longer review cycles. Sitting here in April 2026, we can all relate to reading content clearly generated by AI. It’s offensive and unfortunately everywhere. Can I get an “It’s not x, it’s y!” Marketers don’t like to be graded by their compliance colleagues on the commercial value of the content. Too often this is the only healthy tension from brand manager to published material. Sometimes the Emperor needs wardrobe advice.
In life sciences, the appeal of AI-generated content makes perfect sense. It promises speed, scale, and efficiency just as teams strive to do more with less. But those gains come with tradeoffs. AI makes it easier to produce more, if not always better, content. It introduces subtle errors that are harder to detect, across a rapidly growing review workload on MLR systems that were not built for this level of volume or nuance.
Like most complex processes and like most technology shifts, the path forward requires a thoughtful amalgamation of fundamental best practices, human expertise, and yes advanced software. Done well, AI + life sciences product promotion is super rewarding: helpful materials, compliantly and ethically positioned, efficiently produced. All orchestrated by engaged colleagues working at their potential to educate the market about vital products that make a difference to patients.
1 https://www.zs.com/insights/generative-ai-in-life-sciences-marketing
3 2025 State of Promotional Review: Benchmarks and Insights for Life Sciences
4 https://ahrefs.com/blog/ai-content-is-5x-cheaper-than-human-content/
5 https://aimultiple.com/ai-hallucination, 6 https://neilpatel.com/blog/ai-hallucination-data-study/
8 https://en.wikipedia.org/wiki/Generation_loss