How to fix pharma's content duplication problem

At the heart of every pharma company lies a big problem: medical affairs and commercial teams are independently creating the same content.

Working in isolation due to regulatory firewalls, these parallel efforts lead to duplication, inconsistencies, and wasted resources. Can AI provide a solution to this long-standing problem?

The content duplication dilemma

In pharma companies worldwide, a curious phenomenon occurs daily: the same information gets researched, written, reviewed, and approved twice. Medical affairs teams produce evidence-based, non-promotional scientific content for healthcare professionals (HCPs), while marketing teams develop engaging materials on overlapping topics such as disease awareness, unmet needs, and clinical insights.

This duplication isn't just inefficient – it's expensive. Pharmaceutical companies invest significant resources in content development, with duplicated efforts across medical and commercial teams leading to substantial unnecessary expenditure annually. This redundancy affects speed to market, consistency, and strains limited medical and regulatory reviewer bandwidth.

“When similar content undergoes separate development paths, we create bottlenecks in review processes and risk inconsistent messaging.”

At its core, this issue often stems from poor inter-departmental communication and teams operating in silos. Without clear strategies and well-defined roles, teams can end up competing to create more content to justify their budgets, rather than collaborating efficiently.

Why two parallel content streams exist

The separation between medical and commercial content creation isn't arbitrary. Regulatory requirements, particularly in the US under FDA guidance and PhRMA codes, mandate clear separation between non-promotional scientific exchange and promotional marketing activities. Medical affairs must maintain scientific independence, while commercial materials require different review standards and claim substantiation.

This regulatory firewall serves an important purpose: protecting the integrity of scientific information and preventing inappropriate influence on prescribing behaviours. However, the practical implementation has resulted in organisational silos that create redundancies:

•Disease state information researched separately by both teams

•Similar patient journey insights gathered independently

•Overlapping clinical data interpreted and presented twice

•Common visual assets recreated with slight modifications

The result is extended development timelines, inconsistent messaging, and frustrated internal teams who recognise the inefficiency, but lack systems to address it.

The challenge becomes even more pronounced in global organisations, where content is not only duplicated across departments, but also adapted by each country affiliate. Even within the same language, regional nuances often require extensive reworking, multiplying the inefficiency.

AI as the bridge between medical and commercial content

But there is a solution to this within reach. Artificial intelligence, particularly advanced natural language processing (NLP), offers a potential solution through what can be described as "intelligent content adaptation". This approach centres on creating a single, comprehensive "master content" repository where core information is stored once, then intelligently adapted for different purposes.

Here's how it works in practice:

1.Content classification: AI systems identify which elements of content are scientific statements, clinical data, promotional claims, or contextual information

2.Contextual adaptation: Based on the intended use (medical or commercial), AI adapts the language style, claim structure, and supporting evidence requirements

3.Regulatory parameter enforcement: Automated guardrails flag content that would be inappropriate for specific channels, ensuring compliance

4.Citation and reference management: Automatic tracking of evidence sources maintains proper substantiation, regardless of adaptation

This technology doesn't eliminate the regulatory firewall – it respects it while creating efficiency. Medical affairs maintains control over scientific content, while commercial teams benefit from consistent, pre-approved foundational information that can be appropriately adapted for promotional contexts.

Importantly, medical and scientific expertise remains central to content development, ensuring accuracy and relevance. AI serves as a support tool, streamlining workflows and reducing redundancy while allowing internal experts to focus on providing strategic input, rather than repetitive content adaptation tasks.

When HCPs are involved in co-creation, they can contribute valuable real-world insights instead of reviewing duplicative materials. The most effective implementations establish clear workflows where medical experts create and validate core content, AI assists with adaptation, and human reviewers maintain appropriate oversight based on regulatory risk.

Implementation considerations

Successfully implementing AI-powered content adaptation requires careful planning and appropriate safeguards:

Regulatory involvement from day one: Compliance teams must help establish the parameters and rules that govern the AI adaptation system. This includes clear documentation of what transformations are permissible and what content must remain segregated.

Defined approval workflows: The system should maintain appropriate review pathways, with medical and legal teams reviewing content according to its classification and intended use.

Training and validation: Any AI system requires significant training with pharmaceutical-specific content and validation against established regulatory standards before full implementation. Medical professionals play a crucial role in this training process, helping the system understand industry-specific nuances.

Partial implementation approach: Organisations typically begin with lower-risk content areas, such as disease awareness materials, before progressing to more regulated content types.

Improved communication frameworks: AI tools should support, not replace, better communication between teams. The goal isn't to eliminate specialised content creation, but to reduce unnecessary duplication through clearer role definition and improved collaboration.

Global-local adaptation: For multinational companies, AI can help efficiently adapt global content to local market needs, saving country affiliates significant time in content localisation.

The future of pharma content strategy

The pharmaceutical industry has historically been cautious about adopting new technologies in sensitive areas such as regulatory, and for good reason. However, the pressure to improve efficiency while maintaining compliance has never been greater.

Implementing AI-powered content adaptation may result in significant reductions in content development time and improvements in consistency across channels. These efficiencies allow medical and commercial teams to focus their expertise where it adds the most value – interpreting data and creating strategic narratives – rather than recreating basic information.

“Success comes from organisational transformation, not just technology – rethinking how teams collaborate while respecting regulatory boundaries.”

The ideal approach balances innovation with the essential regulatory safeguards that protect patients and HCPs. Rather than eliminating the separation between medical and commercial content, AI enables more efficient workflows that respect these boundaries while reducing unnecessary duplication.

Key takeaways

The challenge of content duplication between medical and commercial teams has persisted for decades in pharmaceutical companies. While regulatory firewalls remain essential, AI now offers a path to significant efficiency without compromising compliance.

As the technology continues to mature, pharma companies that embrace intelligent content adaptation will gain competitive advantages in speed to market, messaging consistency, and resource optimisation. The question isn't whether AI will transform content development, but which companies will lead the way in this transformation.

For medical affairs and commercial leaders, the path forward involves both technological and organisational change: improving interdepartmental communication, clarifying roles and responsibilities, and using AI to reduce redundant work. This balanced approach can help teams focus on developing fewer, more targeted materials that better serve HCPs and patients alike. In pharma's future, the only thing that should be duplicated is success – not content.

About the author

Nick Lamb is the founder of PharmaTools.AI, a platform dedicated to developing innovative AI tools that streamline pharma MLR review and enhance patient engagement and health literacy. His work has earned industry recognition, driving innovation at the intersection of pharma compliance and digital health.