This article is based on a discussion between Kumar Saurabh, principal and Jason Sundberg, content business solutions lead.

As HCPs and patients expect more timely, relevant interactions, many pharma organizations are rethinking the content, review and operating models needed to deliver those experiences effectively.

To help pharma companies meet those rising expectations, Kumar Saurabh, a principal in ZS’s digital, data and technology practice, and Jason Sundberg, ZS’s content business solutions lead, recently discussed how to modernize the life sciences content supply chain to support more personalized engagement at scale. They explored practical ways to overcome common barriers, including fragmented workflows, slow medical, legal and regulatory (MLR) review, limited content reuse and weak visibility into existing assets—by embedding compliance earlier, improving tagging and using agentic AI to accelerate review, increase reuse and support more adaptive engagement.

Q: Why do personalization efforts so often stall in life sciences?

Kumar Saurabh: Most teams understand the value of more relevant, tailored engagement. The problem is that the underlying content supply chain was not built for the speed, scale and adaptability that personalization requires.

In many organizations, content still moves through fragmented workflows, manual handoffs and sequential review steps. Teams create large volumes of assets, but much of that content is difficult to find, reuse, adapt or approve quickly. At the same time, traditional MLR review cycles can stretch for weeks, which makes it difficult to keep pace with the demands of omnichannel engagement and more tailored customer experiences.

The result is a familiar pattern: by the time content is ready for market, the moment has changed, the audience need has shifted or the asset is no longer as relevant as it should be.

Q: Why is this becoming such an urgent issue now?

Jason Sundberg: Life sciences companies are at an inflection point as expectations from HCPs and patients have shifted toward more timely, relevant and compliant experiences.

HCPs are exposed to thousands of pharma messages each year; yet they engage with only a small fraction of the ones they feel are personally relevant.

But many organizations still rely on legacy content processes that are slow, siloed and difficult to scale for personalization. Leading organizations, however, are using generative AI and connected platforms to bring together content generation, review, tagging, reuse and workflow orchestration in a more unified environment. That shift is helping modernize the content supply chain to increase content speed and volume, streamline MLR review and evolve operating models to better meet HCP expectations.

The next phase is not just about doing more with AI. It is about redesigning workflows across planning, creation, review, deployment and optimization so teams can move from static assets to more adaptive, decision-enabled content.

Q: What does a modern content supply chain actually look like?

KS: The goal of a modern content supply chain is not simply to produce more content, it is to get relevant content to market faster, guided by performance signals and customer context.

That vision has several defining characteristics:

• Compliance is embedded continuously rather than treated as a late-stage checkpoint.
• Personalization happens through small, intelligent decisions about what content to serve, to whom and when, often without requiring an entirely new asset each time.
• Content becomes more than a static deliverable and instead functions as a dynamic asset that can adapt within approved guardrails.
• Agentic AI acts not only as an insight engine, but also as an execution layer that helps operate workflows, enforce policies and keep work moving.

In other words, the most advanced model is not just smarter content creation. It is a more connected system where content strategy, generation, MLR, tagging, activation and intelligence all work together.

Q: How does agentic AI change the way content work gets done?

KS: Agentic AI is not just about generating draft copy faster. Its bigger value is in helping coordinate and execute tasks across the entire workflow.

Instead of treating the content process as a rigid sequence of disconnected steps, agentic systems support work in context and in parallel. For example, compliance guidance can be surfaced during creation rather than after drafting is finished. Claims can be identified and linked earlier. Existing approved assets can be compared automatically. Metadata and tags can be generated so content is easier to search, reuse and personalize later. Publishing readiness can also be checked before handoff to downstream systems.

This matters because many current workflow tools merely digitize old linear processes. But agentic AI opens the door to workflows that are more adaptive, context-aware and responsive—while still keeping humans in control of judgment, oversight and final approval.

Q: How can the ZAIDYN® platform support modernizing the content supply chain?

JS: Platforms can support multiple stages of the content supply chain rather than solving only one isolated task.

That end-to-end view is important. It reflects the reality that speed comes not from a single AI feature, but from connecting creation, review, intelligence and activation.

The most effective systems are designed to work with a client’s existing taxonomy, content repositories and review environments, rather than requiring organizations to start over from scratch.

For teams trying to scale personalization, capabilities like tagging, retrieval and content intelligence are especially valuable because they make it easier to understand what exists, what is reusable, what is performing and what should be deployed next.

Teams can generate new content using approved inputs, brand parameters and life sciences guardrails. They also can create derivatives from existing approved assets, run MLR prechecks, identify similarity to prior approved materials, suggest references and automatically tag content for downstream use.

Q: How can marketing, content and MLR teams adapt their workflows for new technology?

KS: Content supply chain transformation is not just about adding AI on top of existing bottlenecks. Teams need a shared way of working across strategy, content creation, review, operations and analytics.

That starts with better structure: clearer taxonomies, reusable content components, stronger claim and reference management and workflows that reduce unnecessary handoffs. It also requires governance that is designed for AI-enabled work, including guardrails, role clarity and confidence in where human oversight is essential.

When those foundations are in place, marketing teams move faster, content teams create with greater confidence and MLR teams spend less time on avoidable issues and more time on higher-value review. The broader payoff is a supply chain that behaves less like a bottleneck and more like an enabler of relevance at scale.

Q: Where is the best place to start?

JS: MLR acceleration is often the fastest path to value and adoption because review and approval remain one of the most visible friction points in the content supply chain. Manual reviews, repeated back-and-forth and inconsistent submission quality can create long delays and significant rework.

Improving this area can generate immediate operational value. Prechecks help teams catch guideline issues earlier. Similarity scoring shows whether new content closely resembles something previously approved. Auto-referencing reduces manual effort when linking claims to sources. Tiered pathways help route lower-risk content more efficiently. Together, these capabilities can shorten cycle times, improve first-pass quality and free teams to focus their expertise where it matters most.

MLR acceleration also creates trust. It gives organizations a practical way to use AI in a highly governed environment while showing measurable gains in speed, consistency and compliance readiness.

Q: How can pharma teams make content more relevant without simply creating more of it?

KS: The path to more relevant engagement is not just about creating content faster. It comes from understanding what content you already have, what is approved, what is performing and what fits the audience, channel and moment you are trying to serve.

That is where things like tagging, taxonomy and content intelligence become very important. If teams can find content more easily and understand how it has performed, they can reuse and adapt the right assets instead of starting from scratch every time.

The broader shift is from thinking about relevance as a creative output to treating it as a decisioning capability embedded in the content supply chain. That is how organizations begin to move from static personalization efforts to more adaptive engagement at scale.

Organizations that modernize content generation, MLR review, tagging and workflow orchestration will be better-positioned to scale relevance, reduce friction and improve the return on their content investments.

Q: What other strategies should pharma content teams put in place?

JS: Start with the bottlenecks that are creating the most friction today. For many organizations, that may be MLR cycle time, poor content reuse, weak tagging, limited visibility into existing assets or a combination of all four.

From there, focus on practical, high-impact moves: introduce prechecks before formal review, make existing content easier to find and reuse, apply structured tagging and use AI-assisted generation within approved guardrails. These steps create near-term value without requiring a wholesale redesign on day one.

The key is to begin where the need is greatest, prove what works and then scale toward a broader transformation vision. The companies making the most progress are not trying to solve every part of the content supply chain at once. They are building momentum through targeted improvements that compound over time.

Listen to more of the discussion