A modular content strategy once promised pharma companies an efficient way to scale personalized marketing asset creation. Designed to streamline content creation and personalization—especially in regulated environments—it was hailed as the future of omnichannel engagement. However, pharma companies that have implemented a modular content strategy have yet to fully reap its benefits. More importantly, recent major developments in AI capabilities have opened up new possibilities to enable cost-effective personalization at scale. The pharma industry is now rethinking its approach, moving toward AI-driven content creation and intelligent automation to achieve speed, scale, and relevance.
This article explores the evolution of modular content, why it may lose relevance, and what pharma organizations must do to modernize their content operations.
A Brief History: The Rise of Modular Content in Pharma
Modular content emerged in the mid-2010s as a response to increasing content volume demand and regulatory complexity in pharma marketing. The idea was simple but powerful:
- Break down content into reusable, pre-approved modules (incl. claims, components, references, etc.).
- Assemble these modules into emails, detail aids, websites, and other assets.
- Reduce the duplication of Medical-Legal-Regulatory (MLR) reviews and maintain compliance while enabling personalization at scale.
- Leverage granular content atoms to generate data insights to tailor HCP engagement to preferences and improve content relevance.
This approach gained traction as pharmaceutical companies embraced omnichannel marketing. Major content management vendors integrated modular systems, and processes were redesigned around claims libraries, reference tagging, and metadata models.
Expected Benefits:
- Faster time-to-market through reuse & transcreation.
- Reduced MLR reviews & balanced compliance risks.
- Improved scalability for global and local markets.
- Efficient omnichannel content orchestration & cross-channel content usage.
- Higher relevance through personalization & individual variation.
While the concept proved effective in theory and in small-scale settings, real-world execution at scale revealed organizational challenges.
Why Modular Content Is Losing Relevance
The limitations of modular content are increasingly difficult to ignore with the latest emerging trends. As the technical ecosystem evolves, modular content’s semi-rigid structure and heavy operational requirements have become liabilities.
Operational Adoption
A modular content strategy requires a significant mindset change in how marketing teams and creative agencies approach content. Upfront investments in governance, taxonomy, tagging, authoring tools and asset management often face resistance with brands and medical, legal, regulatory reviewers. Therefore, in early-stages rollouts often lead to a slow-down rather than an acceleration in content time-to-market, sometimes causing teams to revert to traditional practices.
Limited Organizational Scaling
While modular content was built for scale - meaning reuse, transcreation and localization - actual content volumes often do not match initial planning assumptions of business case calculations, also due to slow proliferation across markets caused by unaligned taxonomy standards, distinct local regulations and process.
Risk Awareness in MLR
A risk-averse approach often adopted by life science companies partially resulted in MLR teams still required to conduct a full review of assembled content to ensure medical and regulatory accuracy in context. This cautious approach often undermines expected benefits of reduced MLR efforts.
Shift in Channel Dynamics
The explosion of new digital channels, including personalized HCP portals, social media, and hybrid events, demands more agile and tailored content creation. Modular systems struggle to support the pace and personalization these channels require.
AI and Automation Outpacing Human-driven Models
The emergence of generative AI tools has raised the bar. AI can now produce context-aware, channel-specific variations of content that meet compliance rules without the rigid constraints of pre-built modules.
The New Approach: AI-Driven Content Creation and Tagging
Instead of relying on static modules, the future of content in pharma lies in dynamic, AI-enabled creation and variation. While the creation of master content will most probably remain a human-driven task for now, the derivation of variations e.g. for the use in different channels or individual tailoring offers a wide range of applications for AI-tools. Tagging, long perceived as a burdensome exercise, can increasingly be automated with the help of AI— which can also suggest new taxonomy recommendations by identifying patterns across content assets. More efficient tagging lets pharma companies tag content assets on an even more granular level than previous modules. This will result in more detailed information on the performance of content after deployment which then informs new content creation. AI tools, when properly integrated with regulatory frameworks and organizational processes, can generate compliant, customized content at scale while maintaining traceability and quality.
Key Characteristics of the AI-Driven Model
- AI models trained on brand and medical data to generate context-aware content.
- Real-time content generation or derivation for specific channels and audience segments.
- Automated claims linking and reference tagging to support MLR workflows.
- Adaptive learning during content creation to improve quality and reduce review cycles over time.
Insights: How to enable AI-Driven Content Creation
From our recent project work with pharma clients, we can derive the following approaches to leverage the potential of AI in content creation.
Structured Data & Content Foundations
- Establish clean, accessible sources of truth: medical claims, approved data, product messaging.
- Use taxonomies and metadata frameworks that AI can ingest and process effectively.
Integrated AI Tools within Content Operations
- Choose AI tools that can be embedded in content management systems and MLR platforms (e.g. automated tagging, source document pre-referencing, metadata checks).
- Integrate AI-supported content creation tools in content management platforms to ensure traceability and leverage data insights form ideation to deployment.
Human-in-the-Loop Governance
- Set up workflows where AI-generated content is reviewed by human experts for quality and compliance supported by AI (e.g., automated reference tagging, variation of master content).
- Redefine and refocus the role of creative, production and digital agencies in the content ecosystem.
MLR Enablement & Training
- Develop new review protocols that account for dynamically generated content together with MLR teams - – MLR buy-in is a linchpin for success.
- Train MLR reviewers to work with AI-assisted outputs.
Change Management & Pilot Programs
- Start with pilot projects for specific content types to get first insights and prove the concept (e.g., HCP emails, FAQs).
- Leverage quick wins and success stories to build confidence and scale gradually across channels, brands and geographies.
Outlook
The declining importance of modular content in pharma is not a failure of the approach—it is a transition. Modular content served its purpose during a period of digital and regulatory maturation. But the industry now needs more agility, personalization, and scale than static modules can offer. AI-enabled content operations represent a transformative leap forward.
For pharma organizations, the imperative is clear: Don’t just replace modular systems—rethink your entire content ecosystem. Invest in the foundational capabilities, governance, and cultural readiness to embrace AI as a core driver of future content creation.
We recommend a pragmatic, phased approach—grounded in regulatory discipline, enabled by data infrastructure, and accelerated by intelligent automation. Those who lead this transformation will not only improve efficiency—they will redefine what effective HCP engagement looks like in the future.
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