The Growing Pressure on MLR Teams

Pharmaceutical companies are producing significantly more promotional and medical content than ever before.

Several industry trends are driving this growth:

• omnichannel engagement strategies
  
• global-to-local content reuse
  
• personalised HCP communications
  
• stricter compliance and regulatory scrutiny
  
  

As a result, MLR teams must review a growing number of materials while maintaining extremely high standards of accuracy and compliance.

Even with robust systems to manage content workflows and approval processes, the review workload itself can still be intensive.

Why Manual MLR Comparison Slows Down Approvals

One of the most time-consuming parts of MLR review is comparing new content with previously approved materials.

Reviewers frequently need to:

• check whether claims match approved references
  
• confirm that citations remain valid and up-to-date
  
• detect subtle wording changes in promotional copy
  
• verify text embedded within images or visual assets
  
• analyse multiple document versions to understand what changed
  
  

These tasks are essential for compliance, but they often rely on manual comparison across documents and versions, which can slow down review cycles.

As content volume increases, this manual work becomes harder to scale.

How AI Is Supporting MLR Review

AI is beginning to support MLR teams by automating parts of the comparison and risk-detection process.

AI-assisted review tools can help teams:

• automatically compare new content with approved materials
  
• highlight changes in claims, references, and text
  
• detect inconsistencies across versions
  
• identify potential compliance risks earlier in the process
  
  

By surfacing these insights before full MLR submission, teams can focus their expertise on the most important issues rather than scanning entire documents manually.

This allows MLR teams to work more efficiently without compromising compliance standards.

AI as a Complement to Existing MLR Systems

While these systems provide essential governance, workflow management, and compliance oversight, AI solutions can add an additional layer of intelligence by:

• analysing content differences across versions
  
• surfacing claim or reference mismatches
  
• highlighting potential issues before formal review
  
  

This combination helps teams reduce friction in the review process while maintaining full regulatory oversight.

The Future of AI in MLR Review

AI will not replace the role of MLR reviewers.

Human expertise will always remain essential for evaluating scientific accuracy, regulatory interpretation, and risk assessment.

However, AI can significantly improve how teams operate by:

• reducing time spent on manual document comparison
  
• helping content creators identify issues earlier
  
• improving first-time approval rates
  
• enabling MLR teams to manage growing content volumes
  
  

As pharmaceutical organizations continue to scale their omnichannel engagement strategies, AI-assisted review is becoming an important capability in modern content operations.

Supporting Smarter MLR Workflows

New AI capabilities are now being integrated into content authoring and review environments to support faster, more efficient MLR processes.

One example is Shaman’s MLR Compare Assistant, which uses AI to identify similarities between new materials and previously approved content, helping teams quickly focus on what has actually changed before submission.

By detecting changes in claims, references, and image text, these tools help reduce manual comparison work and support more efficient review cycles.

Final Thoughts

MLR review will always require deep expertise and careful regulatory oversight.

But as the pharmaceutical industry continues to produce more content across more channels, the review process must evolve.

AI is helping organizations modernize MLR workflows by reducing manual effort, improving visibility into content changes, and enabling teams to focus their expertise where it matters most.

For many pharma companies, the future of MLR review will combine:

• trusted governance platforms
  
• smarter authoring solutions
  
• and AI-assisted comparison capabilities
  
  

Together, these technologies can help teams deliver compliant content faster while maintaining the highest standards of review.

How is your team handling the growing complexity of MLR review?

Let’s connect.
Book a consultation with one of our Content Excellence experts to explore how AI-assisted comparison can help your teams reduce review cycles and improve first-time approvals.

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