Pharma companies are re-thinking their modular content strategies for marketing. Discover how AI is unlocking new possibilities to enable cost-effective content operations to provide personalized and relevant content to Healthcare Professionals (HCPs).

Authors: Leonie Paul, Georg Cebulla, Julian Modrow 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.  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. Hier kommen KI-Agenten ins Spiel: Sie agieren wie ein erfahrener Kollege, der das Unternehmen nie verlässt, Zugriff auf relevante interne Daten hat und daraus kontextbezogene Empfehlungen ableiten kann. Selbst die Erfassung neuer Informationen kann durch den Agenten unterstützt werden – etwa durch das strukturierte Sammeln von Risikoeinschätzungen im Team. Ganz im Sinne des Mottos: „Jedes Projektmitglied ist ein Risikomanager.“     

In der Praxis genügt heute bereits ein Projektsteckbrief, um den KI-Agenten zu aktivieren. Dieser analysiert vergleichbare Projekte, identifiziert bekannte Risiken, extrapoliert neue über Bereichsgrenzen hinweg und liefert konkrete Hinweise zu Eintrittswahrscheinlichkeiten, Kostenfolgen und bewährten Gegenmaßnahmen. So wird Risikomanagement nicht nur effizienter, sondern auch integrativer Bestandteil des Projektstarts.  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. Aus unserer Projektarbeit mit Pharma-Kunden lassen sich folgende Erfolgsfaktoren ableiten:

Strukturierte Daten & Content-Grundlagen

→ Etablieren Sie klare, zugängliche Datenquellen: medizinische Aussagen, freigegebene Informationen, Produktbotschaften.

→ Nutzen Sie Taxonomien und Metadatenstrukturen, die von KI-Systemen effizient verarbeitet werden können.

Integration von KI-Tools in Content-Prozesse

→ Wählen Sie KI-Tools, die sich nahtlos in bestehende Content-Management- und MLR-Systeme integrieren lassen.

→ Integrieren Sie KI-gestützte Content-Erstellung in Ihre Plattformen, um Rückverfolgbarkeit sicherzustellen und datenbasierte Insights von der Idee bis zur Veröffentlichung zu nutzen.

„Human-in-the-Loop“-Governance

→ Etablieren Sie Workflows, in denen KI-generierte Inhalte durch Experten geprüft werden – unterstützt durch automatisiertes Tagging und Variantenbildung.

→ Definieren Sie die Rolle von Agenturen im Content-Ökosystem neu.

MLR-Einbindung & Schulung

→ Entwickeln Sie gemeinsam mit MLR-Teams neue Prüfprotokolle für dynamisch generierte Inhalte.

→ Schulen Sie MLR-Prüfer im Umgang mit KI-gestützten Ergebnissen.

Change Management & Pilotprojekte

→ Starten Sie mit Pilotprojekten für konkrete Content-Typen (z. B. HCP-E-Mails, FAQs).

→ Nutzen Sie die Erkenntnisse und Erfolge aus den Pilotprojekten, um Vertrauen aufzubauen und die Skalierung schrittweise voranzutreiben.  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. Would you like to learn more about our services in the Life Sciences & Healthcare sector? Then feel free to visit us here or get in touch with us directly.

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