How a Modular Approach in Pharma Fixes MLR Challenges in 2026

You simply can’t treat medical legal regulatory (MLR) approval workflow as a guardrail when pre-review processes alone can stretch to 150 days per asset. You treat it as a bottleneck.

If you want to reach HCPs in time, you need to reduce that friction. Modular content was supposed to help. But many teams try a modular content strategy, get underwhelmed, and move on.

After more than 17 years working with pharma, we can confidently say the problem is how teams implement it rather than modular itself.

In this post, we’ll look at the key problems with traditional modular content and how we’ve learned to address them. We’ll also explain why modular remains relevant in the age of AI and explore the latest trends helping teams reduce MLR friction.

Four Challenges that Demotivate Teams Going Modular

Here are key challenges that gave modular content a bad reputation and what to do to address them.

The design element is disregarded

One of the biggest mistakes teams make is treating modular content as a design-free exercise. They create channel-agnostic modules, store them in the digital asset management (DAM) system, and reuse them whenever needed.

Even though it sounds logical because the absence of the design element is the key pillar of modular content creation, this approach suffers from serious problem. Someone still has to turn those modules into a channel experience. A DAM full of content blocks doesn’t tell you how those blocks should look when they become an email, banner, landing page, or other asset.

The moment a module enters a channel, it inherits a design context. You can’t separate content from design. This is the single biggest reason companies struggle to move beyond modular pilots.

Based on our experience working with high-performing teams, design should be built into the structure from the start. The authoring platform should be able to apply design automatically based on the module’s structure. That’s how we designed eWizard. The platform defines the logic to render each module correctly across channels and formats.

In other words, design can live in the rules that determine how that module is assembled and displayed. Get that structure right at the authoring stage, and consistent design can happen automatically downstream.

Organizations that establish this foundation can reduce time to market by up to 30%, increase content reuse by more than 50%, and produce up to three times more assets compared to pre-eWizard operations. These results are observed across implementations in our dataset.

Modular is supposed to help teams move faster. But if every new combination requires rebuilding the design and structure from scratch, that advantage disappears. You’re back to creating a new asset and sending it through MLR every time.

MLR teams still review finished assets

You can modularize your content, but if your MLR team still reviews every finished asset, you haven’t removed much friction from the approval process.

Most MLR teams aren’t ready to change their approval policies in favour of module-level approval. This is why we often suggest our clients to introduce an AI-powered MLR pre-approval process that works within the review structure you already have. Before submission, AI can identify content likely to trigger revisions, suggest improvements, and generate an approval likelihood score.

Then, after the asset goes through formal approval, break it down into its component modules. Each approved fragment goes into a reusable content library. Over time, that library becomes a bank of pre-approved content teams can use when assembling future campaigns.

Right now, you can try the following: find the ten claims that appear most frequently across your asset portfolio. If none are pre-approved at the module level, that’s probably where modular investment can pay back fastest.

Teams lack version control

Modular content reuse only works when people know what they’re reusing. When version history is complete and accessible, teams can reuse approved content with confidence. They can respond to regulatory questions and create market adaptations from approved master versions without restarting the review cycle.

Teams end up asking whether the version they’re using is approved, which leads to a lot of confusion, errors, delays, and frustration.

A quick test for you: pick any asset currently in market. Can you identify the approved version, approval date, approver, and claims it contains in under five minutes? If not, version control is a gap in your content infrastructure.

Local teams don’t see value in a global content

When the deck with 80 slides from a global teams arrive, local teams already know that they won’t use all 80 slides. In fact, our own experience shows that 80% of local content is created from scratch, which substantially increases the waiting period from MLR.

Since local teams don’t routinely localize every piece their global colleagues provide, sending a global team a library of modules doesn’t automatically create local reuse. What we’ve built is the platform that automatically localizes both individual modules and complete assets. That gives local teams a library of localized reusable content modules.

Why Modular Still Matters in the Age of AI

At this point, a fair question is: Why spend time building a library of pre-approved modules if AI can assemble content in seconds? Because AI needs something to assemble. Without modular content, your AI doesn’t have a clean, structured source to work from. It may produce something that looks good, but isn’t necessarily consistent, on-brand, or compliant.

When AI automatically selects and combines modules based on audience, channel, and context, modular content becomes the structured data AI needs to produce high-quality outputs. That means taxonomy, consistent tagging, and rich metadata matter.

Without them, AI has no reliable way to know which content to recommend, reuse, or combine for a specific audience, context, or channel. Modular content becomes the infrastructure that makes AI-assisted content generation in pharma possible.

Current Trends to Keep an Eye On

Modular can make a pharma MLR process more efficient when teams can quickly find, assemble, and reuse approved content. And AI makes that possible at scale. Three capabilities have become trendy today:

• Composition: automatically assembles modules into compliant, audience-specific content.
• Decomposition: breaks existing content into reusable building blocks for future campaigns.
• Smart search: surfaces the most relevant modules in context, eliminating manual browsing.

Today, some companies are building their own AI tools for content authoring, while others prefer ready-made solutions that can be tailored to their specific needs. Even companies building proprietary AI agents still need to connect them to the DAM and broader content supply chain. Why? Because AI is only as good as the context it can access.

That context includes approved content, metadata, workflows, and compliance rules. Connected to the DAM, an AI agent can find and retrieve the right modules and assets without human intervention.

Connected to the content supply chain platform, it can work within templates, brand rules, and compliance guardrails, while supporting approval stages without manual handoffs.

Modular Is Becoming AI Infrastructure

Modular maturity happens in stages. First, you build the foundation, manage a live library of reusable content, and, finally, AI takes over assembly. Each stage removes more manual work from producing compliant content at scale.

At full maturity, the modular approach in pharma becomes AI infrastructure. Teams stop manually searching for components and assembling assets, letting systems automatically combine pre-approved content with compliance already built in.

That’s why it makes sense to build the modular foundation now. Since brand are opting for AI content generation to reach more HCPs faster, modules become more valuable than ever. Structured, pre-approved modules give AI the clean data it needs to generate high-quality content at scale. And brands that build those libraries now will be structurally ready when AI-led content operations become the standard.

You don’t need to figure out every stage of the modular journey yourself. Viseven brings the technology, but also the content strategy, pharma content operations, and change management needed to make it work in practice.

That means we can support you from the first steps of building your modular foundation through to the mature stage, when your teams are confidently using AI to generate compliant content at scale.