Use Case: Accelerating Pharma Research with AI Agents

Pharmaceutical research is a race against time. Every breakthrough matters, yet the path to discovery is often slowed by mountains of data. Scientific papers, citations, and experimental results accumulate faster than any human team can process. Important insights remain buried, and innovation stalls. AI is changing that story. 

The Challenge 

Imagine a researcher tasked with reviewing thousands of papers to identify potential drug targets. It’s not just overwhelming, it’s nearly impossible. Traditional literature reviews take weeks or months, and even then, critical correlations can slip through the cracks. The sheer volume of information is the enemy of speed. 

The Turning Point 

This is where AI steps in, not as a replacement for human expertise, but as a partner that handles the heavy lifting. AI agents can scan thousands of scientific papers, extract key insights, and map relationships across citations. They surface patterns that humans might never see, enabling faster hypothesis generation and smarter decisions. 

A Practical Example: Literature Review Agent 

Picture this: an AI agent that combs through research databases, connects related studies, and highlights promising correlations. Instead of spending weeks gathering data, researchers can focus on what they do best, interpreting results and shaping strategy.  

Start Small: The NDA Agent 

But here’s the truth: most pharma companies don’t start with complex research workflows. They begin with something simple, something practical, for example an NDA review agent. 

Every pharma organization deals with NDAs, contracts that govern partnerships, trials, and research collaborations. Reviewing these documents manually is tedious and error-prone. So, what if an AI agent could do it for you? 

Using Microsoft Copilot Studio, you can build an NDA agent in just 20 minutes. It checks NDAs against internal policies, flags non-compliant clauses, and even creates a clear compliance table for legal teams. No coding marathon. No complex setup. Just a quick, tangible win. 

Start small

Once you see the impact of a simple agent, the possibilities open up. Literature review agents accelerate drug discovery. Compliance agents streamline regulatory checks. Together, they transform the way pharma works, making innovation faster, safer, and more cost-effective. 

Don’t think big first. Think small. Identify one repetitive, rule-based task, like NDA review, that consumes time but adds little strategic value. Build an agent for it. Test it. Learn from it. Then scale to more complex workflows like research automation and clinical trial orchestration. This approach reduces risk, builds confidence, and creates a foundation for long-term transformation. 

Ready to explore what your first AI agent could look like? Let’s talk about the small steps that lead to big change. 

Want more Inspiration? Explore other AI use case in Pharma: 
- Smarter Manufacturing and Quality Control with AI 
- Post-Market Surveillance and Safety Powered by AI  
- Clinical Trials Reimagined with AI Agents 

Source: ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models