Pharma’s AI Reality Check: Five Priorities for Marketing and Communications
Fierce Pharma Week 2026 was filled with industry, vendor and agency representatives all eager to learn and share knowledge about pharma marketing, medical communications, omnichannel, PR and of course... AI.
I don’t think I attended one session where the word “AI” wasn’t uttered – no matter the topic! But much like “social media” was on the lips of attendees of Digital Pharma East years ago, AI has become pervasive, no matter the discipline. Two years ago, it was, “What is AI, and how do I use it?” Last year was, “OMG, AI makes mistakes.” And this year has settled into, “Let’s use AI but use it responsibly.”
The hype has given way to tougher questions related to improving outcomes, governance, learning and determining the purpose of AI use.
These are 5 topics/trends that stood out.
1. Close the Framing Gap
My colleague Joseph Daley and I presented on this topic.
There is a way that you wish to position your brand to others, and yet there is a new bard in the world – AI – that is now positioning your brand for you.
You need to ensure that you are monitoring your brand/product/corporate story that’s being told by AI and correcting for it when that story is being told wrong.
2. Personalization with Purpose
While marketing personalization remains a priority, speakers questioned when it becomes excessive or hyper-personalized. UCB’s Kimberly Moran, PhD, MBA challenged whether this hyper-personalization actually creates meaningful impact or just annoys a marketing-weary customer. The point is, personalization should support a customer’s desire or decision and not merely be done to demonstrate the technical capability.
3. AI Acceleration Can Create “Fake Velocity”
Alex Halliday of AirOps described everyone’s mad dash into AI adoption masquerading as progress by describing it as “fake velocity.” Companies are showcasing AI projects that are absent validation and risk mitigation yet viewing it as acceleration. CMI Media Group’s Molly Farrell added a warning along those lines: while a human makes mistakes at a fixed pace, AI can potentially produce thousands of errors in minutes.
4. Human Leadership Matters More Than Human Presence
The term “human in the loop” has been making the rounds with the advent of AI agents. We often refer to it as, “AI in the loop,” since we firmly believe AI should just be viewed as a tool in the hands of human expertise and oversight. Novartis’ Tatiana Sorokina referred to it as having a “human in the lead.” Another reference I like. Routine decisions with a small number of outcomes may require less intervention, while more interpretive work, such as analyzing clinical data, will demand higher-order judgment by people. I like how Doceree’s Graham Wilkinson put it – if you can’t accurately describe a process yourself, don’t think you can train an agent to do it.
5. We Must Measure Outcomes, Not Outputs
Chris Patil from BioAge Labs suggested that you should consider if an AI helps an organization assist, accelerate, amplify, or augment. If it does none of those things, it may simply be “AI theater.” Annemarie Carpico from Jazz Pharmaceuticals put it well when she noted that we have to measure AI outcomes and not AI outputs. And Graham reinforced this point in the need to shift from asking “Are we using AI?” to “Is it effective?”
In short, AI is a tremendous force multiplier, but we don’t need AI for AI’s sake. We need AI implementation alongside purposeful systems, accountable teams, revealing friction, and communicators’ skills in translating this technological possibility into genuine value.
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Contributors
Matt Snodgrass
AI Innovation Lead
Syneos Health Communications
This blog was originally posted to Matt's LinkedIn profile here.
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