Artificial intelligence is quickly becoming part of pharmaceutical Medical, Legal, and Regulatory (MLR) review. But a new category of AI is emerging that goes beyond summarizing documents or responding to individual prompts: agentic AI.
For pharmaceutical companies, agentic AI has the potential to fundamentally change how promotional content is prepared, reviewed, substantiated, and approved.
What Does Agentic AI Mean?
Traditional generative AI generally waits for a user to provide a prompt.
Agentic AI is designed to perform more complex, goal-oriented work. Multiple specialized AI agents can analyze information, perform individual tasks, interact with other systems, and provide results to human reviewers.
Within MLR, those agents can be assigned specific responsibilities.
For example, one AI agent might identify promotional claims. Another can search approved references for supporting evidence. Another can evaluate regulatory considerations. Another can compare versions or identify inconsistencies.
Together, these agents create an intelligent review environment.
How Agentic AI Can Work in MLR
Consider a pharmaceutical promotional asset entering review.
In a traditional workflow, regulatory, medical, and legal reviewers may need to manually identify claims, locate supporting references, evaluate substantiation, and determine whether language presents regulatory concerns.
With an agentic MLR platform such as ERMA Systems, AI can begin analyzing the material before the human review is complete.
The system can help:
- Identify potential promotional claims
- Connect claims with supporting references
- Analyze reference content
- Surface relevant regulatory considerations
- Identify potential compliance concerns
- Compare document versions
- Organize information for human review
Instead of replacing the reviewer, AI prepares the information needed to make the review more efficient.
Agentic AI vs. Generative AI
The difference is important.
Generative AI might answer:
"Does this claim appear to be supported by this clinical study?"
An agentic system can potentially identify the claim itself, locate relevant evidence, evaluate the relationship between the evidence and claim, surface applicable regulatory considerations, and present the findings to the reviewer.
The shift is from asking AI questions to giving AI responsibilities within a controlled workflow.
Why Agentic AI Matters for Pharmaceutical MLR
MLR teams face a fundamental scalability challenge.
Content volumes continue to grow while every promotional asset still requires careful review.
Agentic AI creates the possibility of performing significant portions of preliminary analysis before the reviewer begins.
That can help organizations reduce repetitive work, improve consistency, identify potential problems earlier, and focus experienced reviewers on decisions requiring professional judgment.
Human Oversight Remains Essential
Agentic AI does not eliminate the need for Medical, Legal, and Regulatory expertise.
Pharmaceutical promotion involves nuanced scientific and regulatory decisions that require human judgment.
Instead, AI can operate as an intelligence layer around those professionals.
ERMA's approach to agentic MLR is built around this principle: AI performs analysis; qualified reviewers remain responsible for decisions.
The Future of Agentic MLR
The first generation of MLR technology digitized the review workflow.
The next generation will make that workflow intelligent.
ERMA Systems is developing an AI compliance operating system for life sciences designed to bring agentic intelligence into regulatory and promotional processes from development through commercialization.
The future of MLR isn't simply moving documents faster.
It's understanding those documents before they reach the reviewer.