Life sciences content strategy is being reshaped by a combination of faster-moving therapies, expanded digital channels, and rising expectations for speed and precision. What once operated on predictable, months-long cycles now requires more frequent updates, tighter coordination, and greater visibility across teams.
At the same time, content itself has changed. Modular assets, rapid refreshes, and channel-specific execution have replaced large, static deliverables—raising the bar for alignment, review, and measurement without reducing regulatory scrutiny.
In this Q&A, Jon Fisher, Senior Manager of Product Management at Vodori, shares his perspective on what’s driving these shifts, where teams commonly get stuck, and how organizations are adapting their content strategies to operate more effectively in today’s life sciences environment.
How has life sciences content strategy evolved in recent years?
Significantly. There are a couple of reasons for the evolution in my opinion. One, since the focus on digital channels was put into full throttle due to COVID. This meant content needed to align and be tailored to different channels, many of those channels needing a constant flow of content to keep them fresh. In addition to that, some of the newer therapies on the market today evolve much faster than legacy therapies. New data and labels are being received much more frequently, leading to refreshed messaging every few months, sometimes even more frequent. Whereas, not that long ago, it was every 6 months.
Ultimately, both factors have led to the need for faster content execution. A tactic that may have taken a few months to create, go through reviews, and get approved, now may have to be done in less than one month for it to be relevant. Additionally, content is being created in smaller chunks versus large multiple slide/page documents. While this may mean the content can be created quicker, it does potentially introduce the need for more discussion and alignment to ensure that in that smaller chunk, the message is properly balanced and includes the necessary safety information.
Ultimately all of these necessitates that the tool being used to facilitate the review and approval of the content doesn’t become a blocker. It needs to be easy to use for all the teams included in the process. From initial upload, to substantiating claims, facilitating the review, and finding historical context.
What are common mistakes in life sciences content strategy, and how do organizations avoid them?
One of the common things that I saw when I was on the agency side and continue to hear from customers is the “rinse and repeat” mindset. A lot of money goes into the development of content, but often the strategy does not include evaluation of the performance of the tactic or content strategy. Sometimes, this is due to there just not being time to do so, but in a lot of cases, it’s due to the lack of connectivity between ecosystems so the data to do the analysis just isn’t there or it costs a lot of money to make it happen. This is something I think as an industry we need to get better at, what messages are resonating, what content channels make the most sense, and what is driving meaningful conversation and change. Having the data to support this type of analysis and conversation will allow for a go/no-go decision on specific tactics when it comes time to refresh content, leading towards reducing the amount of content and teams working more efficiently to get the right content, in the right channel(s) to their customers faster.
What role do you see emerging technologies (like generative AI, immersive media, etc.) playing in content for life sciences in 2026?
As an industry I think we still have a long way to go here. There’s a ton of talk around AI and using it in content creation and review and approval. At Vodori, we’re exploring just that, but we’re approaching it in a structured way to ensure we deliver capabilities that move the needle and aren't just AI for the sake of saying we’re doing AI.
When it comes to AI, I think those customers that are focusing on AI from a perspective of how and where it can eliminate bottlenecks in the review and approval process and create efficiencies have the right approach. In my opinion, that should be the priority focus and then we can see what role AI can and will play in our industry when it comes to content generation. Right now there’s a lot of unknowns around that: how does it align to regulatory requirements? What kind of oversight will AI generated content need from agencies and in-house teams?
As we approach a new era in life sciences as it relates to CRM systems, customers should expect to be able to easily see how content is performing. For too long, this has been a gap, meaning that marketing teams have invested a lot of time and money in creating extensive interactive tools for their field with no good way of seeing how that tool is used and what the ROI on it is. Instead, it goes back to the “rinse and repeat” mindset where every 3-6 months, that piece is updated with new information and pushed out to the field.
What this means for life sciences teams
As life sciences content strategy continues to evolve, the challenge for teams isn’t simply keeping up with change—it’s building the operational discipline to adapt consistently. Faster cycles, more channels, and increased complexity aren’t temporary conditions; they’re now part of the baseline.
Organizations that make progress in this environment tend to focus less on producing more content and more on improving how content moves through review, alignment, and measurement. That shift—from volume to execution—creates space for better decisions, clearer accountability, and more effective use of resources.
Ultimately, modern content strategy in life sciences is less about reacting to new tools or trends and more about designing systems and processes that can support speed and scrutiny at the same time. The teams that recognize this are better positioned to adapt as expectations continue to rise.
