How Leading Pharma Brands Increase Content Velocity

What is content velocity? In physics, velocity measures an object’s speed in a specific direction. In life sciences, content velocity means measuring whether the right content reaches an HCP at the speed of need. It also requires direction: producing content that delivers value while that value is still relevant.

Someone who has just been diagnosed with cancer cannot afford to spend weeks navigating a confusing website in search of answers. Content that arrives too late has already lost much of its value. As experience management expert Richard Schwartz said about people navigating a cancer diagnosis:

“I needed it two months ago. And I needed it specifically.”

So, what are leading teams doing to run with their assets faster?

They Align Teams to Plan the Key Decisions

Brand, Medical, Commercial, and Sales often enter the process with different priorities, KPIs, and interpretations of what the campaign should achieve. These differences then haunt these teams during briefing, creation, review, and deployment.

According to McKinsey, many pharma companies repeat the previous year’s strategic objectives and tactics rather than setting more ambitious priorities. One contributing factor is limited cross-functional collaboration during planning.

When each team optimizes only its own part, campaign decisions are repeatedly reopened downstream. It is not uncommon for Medical teams to review content without enough strategic context, and local teams reinterpret briefs that were never fully aligned at the global level.

A shared brief prevents much of this rework. Brand, Medical, Commercial, and Sales agree early on the audience, business objective, key messages, evidence, channels, KPIs, and compliance boundaries. The resulting brief can move through the content supply chain, and there is no need to renegotiate everything at every stage.

Teams move from separate, channel-specific briefs to cross-channel use cases supported by shared dashboards and regular planning sprints. At the most advanced level, performance data continuously informs the brief, while next-best-action recommendations help adjust journeys and campaign logic.

Content velocity improves because teams make major decisions once, use previous campaign learning, and give everyone downstream the same definition of success.

They Don’t Rebuild What Already Exists

Pharma and biotech companies are producing more content than ever, yet much of it is still built from scratch. According to a Veeva survey of pharma and biotech professionals, 86% of teams were creating more content than they had just six months earlier.

That growth places visible pressure on Medical, Legal, and Regulatory (MLR) teams. The same claims, references, and design elements are recreated and sent through approval repeatedly. This consumes time and budget that could be spent producing new, creative content for HCPs.

Modular content should solve this problem. In practice, many companies try it, struggle to scale beyond a pilot, and conclude that the model does not work.

Teams are often quick to blame modular content itself. Based on our experience working with global brands, however, the problem is usually how modularity has been implemented.

A module still needs to become an experience

Teams are often told that modules should be channel-agnostic and design-free: pure, structured content stored in a digital asset management system (DAM), ready to be used anywhere.

Yet, what we often see happening is that a company has a DAM full of approved modules that nobody knows how to turn into an email, banner, eDetailer, or landing page.

A module may not need a fixed design while it sits in the DAM. But the moment it enters a channel, it inherits a design context. In other words, content can be divorced from a particular layout, but it cannot be divorced from design logic.

This is one of the biggest reasons modular content programs fail after the pilot stage. Teams create reusable content components but do not establish the rules required to assemble them into consistent channel experiences.

Put design into the system

Content supply chain platforms, like eWizard, apply design automatically based on the module’s structure. Standardized content blocks define what each element is and how it should behave. The platform can then render the same module correctly across different channels, formats, and screen sizes.

Design still exists, but it lives in the rules governing how content is assembled and displayed.

If teams agree on the structure of common elements such as headlines, claims, visuals, calls to action, safety information, and references, they can automate much of what happens downstream. If that structure is inconsistent, every new asset will require more time to manually adjust.

When implemented well, modular content allows our clients to reduce time to market by up to 30%, increase content reuse by over 50%, and produce up to three times more assets. Results vary, but the common reason is that teams can adapt and launch content without rebuilding their structure and design each time.

They Remove Friction in the Content Supply Chain

Modular content makes individual assets faster to produce, but content velocity depends on the entire content supply chain: planning, creation, review, approval, distribution, and analysis. A delay at any stage can cancel out the time saved during content authoring.

If we drew the content supply chain on paper, it would look more like a circle than a straight line. Pharma teams continuously create content, measure its performance, and use those insights to plan what comes next.

Tracking previous campaigns helps them decide what to modularize, reuse, localize, update, or build from scratch. Without this feedback loop, teams risk producing content that already exists or investing in assets that have little value.

The bigger obstacle is that the systems supporting these stages often operate independently. CRM, authoring, MLR workflow, DAM, and analytics platforms may each hold part of the required information. As content moves between them, teams have to transfer files, re-enter metadata, verify approval details, and reconcile different versions manually. These frictions cost you time and make it impossible for your teams to scale.

According to a BCG benchmark of more than 100 senior omnichannel leaders, only one in ten biopharma and medtech companies have fully integrated data platforms, while 90% cite siloed systems as a major barrier to delivering the right content on time.

Content also needs consistent taxonomy, tagging, and metadata. A DAM may contain thousands of approved assets, but if the system cannot reliably identify their audience, market, channel, claims, or approval status, neither people nor AI can easily determine what should be reused.

The order matters: connect the ecosystem, structure the content data, and then apply AI. Only then can AI recommend existing materials, identify localization opportunities, or help assemble relevant assets. Otherwise, you will scale the same fragmentation that already slows you down.

They Use AI Once the Right Foundation Is in Place

Modular content and a connected supply chain create the foundation for content velocity. Meanwhile, pharma-specific AI agents capitalize on that foundation and help you scale.

At the production stage, AI can support three essential modular content tasks:

• Composition: assembling approved modules into content for a particular audience and channel
• Decomposition: breaking existing assets into reusable components
• Smart search: finding relevant modules without requiring teams to browse large content libraries manually

When connected to the DAM, an AI agent can retrieve approved copy and visuals, apply brand rules, and enrich content with consistent metadata. When connected to the wider supply chain, it can also help move assets through content localization, approval, publishing, and analysis without repeated manual handoffs.

Solutions, like eVa AI inside eWizard, work within approved sources, templates, brand guidelines, and compliance guardrails. eVa AI can pull approved copy and images from the DAM, assemble them into a layout, and link each output back to its source documents. Teams can therefore trace where the content came from rather than treating the output as an AI black box.

AI can also accelerate what happens before formal MLR review. According to Veeva, the journey from content creation to approval can take up to 150 days, while 80% of large pharma companies are exploring AI to optimize the process.

A pharma-specific AI agent can pre-check an asset against approved claims, references, brand rules, and local requirements before it reaches reviewers. It can then flag potential issues or assign a risk score. This lets MLR teams narrow-focus their attention on content assets that require human judgement, industry expertise, knowledge of cultural nuances, and empathy.

The system should also improve through feedback. When reviewers reject a claim or request a change, that decision can be captured and used to identify similar issues earlier in future workflows. Relevant insights can also be shared across markets, with local regulatory differences preserved, so teams do not repeatedly submit the same problematic content.

Reaching Customers with the Speed of Need

It is safe to assume that a drug molecule is not the only factor shaping the patient experience. Content velocity also matters, as it helps life sciences companies support better healthcare when a patient feels lost, confused, and scared.

For life sciences companies, content velocity is partly about competitiveness. More importantly, it is about reaching HCPs with the information their patients need now or may need in the near future.

We can help you connect your technology ecosystem, improve content operations, scale modular content, accelerate MLR review, and align teams so critical information reaches HCPs at the speed of need.

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