AI for MLR Excellence: Balancing Innovation and Compliance

Medical, Legal, and Regulatory (MLR) reviews have long played a crucial role in protecting accuracy, scientific integrity, and compliance across both promotional and non-promotional content. Yet this rigor often comes with a trade-off. Traditional review cycles can be slow, resource heavy, and difficult to scale, especially as content volumes continue to rise.

This need for speed has become more important than ever. Pharma organizations are operating in a world where rapid scientific updates, competitive product launches, and increasing digital touchpoints demand faster content turnaround. Delays in MLR can directly affect market readiness, customer engagement, and ultimately patient outcomes.

AI has now entered this landscape at a pivotal moment. Generative AI and agentic AI offer new possibilities to streamline drafting, accelerate reviews, and reduce redundancies in MLR workflows. These technologies promise meaningful efficiency gains, but they also require thoughtful adoption to ensure compliance, accuracy, and trust are never compromised.

These themes were explored in a recent panel discussion at the Indegene Digital Summit 2025. The session was moderated by Mohit Jain, Associate Vice President, Client Services at Indegene, and featured Gareth Worthington, Global Head of Clearance at UCB, and Heather Sun, Senior Director of Medical Information and Review at Alnylam. They shared practical insights from real-world implementations and highlighted what it will take to achieve both speed and compliance in the AI era.

AI is often viewed as a tool that speeds up the review stage, but its most meaningful impact begins much earlier. Gareth highlighted that the real opportunity lies in helping content teams create materials that are already aligned with MLR expectations before they ever reach a reviewer. This shift reduces friction in the process and transforms how organizations think about content quality.

By supporting teams upstream, AI acts as a quality gatekeeper. It can flag incomplete claims, missing references, inconsistent statements, or outdated messaging even before drafts move forward. This allows writers, brand teams, and agencies to refine content early, reducing the load on MLR reviewers and eliminating avoidable delays.

With AI streamlining the initial stages, organizations can shift from reactive corrections to proactive preparation. Reviewers can then focus their expertise on nuanced scientific interpretation, risk assessment, and final refinement, rather than spending time resolving basic errors.

The upstream use of AI helps teams:

Successful AI adoption goes beyond purchasing a software solution. Heather highlighted the human and operational factors that often determine whether AI projects succeed:

Pharma organizations need internal expertise, dedicated time, and structured processes to integrate AI into existing workflows. Without these, even advanced AI systems may fail to deliver value, creating frustration and inefficiencies rather than improvement.

High-quality, well-structured data is essential for AI to deliver credible and consistent results. As Gareth Worthington from UCB highlights, the effectiveness of any AI system is directly shaped by the reliability of the information it is trained on and the inputs it receives. Poorly organized or inconsistent data inevitably leads to weak or inaccurate outputs, which can undermine confidence in the technology across teams.

Pharmaceutical organizations often handle large volumes of complex content across multiple languages, templates, and formats. When this information is fragmented or unstandardized, AI systems struggle to interpret and process it effectively. By ensuring that data is accurate, regularly updated, and consistently structured, organizations create the foundation needed for AI to operate at its full potential. This not only enhances performance but also builds trust among users who rely on AI to support critical decisions.

Looking ahead, agentic and generative AI in pharma marketing are expected to act as intelligent assistants to MLR teams, taking on routine checks and helping manage growing volumes of content. Heather Sun from Alnylam notes that these technologies are designed to strengthen human capability rather than substitute it. The emphasis that while tasks such as fact verification, use of pre-approved messages, and metadata review can be supported by AI, strategic interpretation and judgment must continue to be led by people, was not lost.

As AI evolves, MLR teams can use it to offload repetitive and data-driven activities, allowing reviewers to concentrate on strategic, creative, and patient-centered decision-making. Gareth added that while AI provides structure and precision, the nuanced art of communication within MLR will always rely on human expertise.

Even with promising technology, organizations face practical constraints. Global rollout of AI in MLR requires significant resources, change management, and regulatory alignment. Gareth explained:

Heather added that rapid technology evolution adds complexity: by the time a solution is implemented, it may already need updating. Small-scale pilots, thoughtful planning, and realistic timelines are essential for success.

The adoption of AI in MLR does not happen in isolation. Regulations shape what can and cannot be automated. Gareth noted:

Organizations must consider local and global regulatory requirements, fostering dialogue with authorities to ensure that AI-driven processes remain compliant while enabling innovation.

The panel discussion highlighted actionable lessons for organizations exploring AI in MLR:

MLR in the age of AI is about balance: leveraging technology to enhance efficiency and insight while ensuring integrity, compliance, and trust. With careful planning, strong foundations, and incremental adoption, organizations can harness AI to accelerate review processes and focus human expertise where it truly adds value.