AI in pharma is getting real. What was once abstract hype about machine intelligence is quickly turning into practical, measurable impact — especially with the emergence of agentic AI.
Unlike AI assistants or chatbots that suggest or support, agentic systems are capable of completing tasks autonomously or semi-autonomously, with minimal human input. This level of autonomy opens the door to greater productivity, but also demands clarity, trust, and strategic alignment.
In pharma, where precision, compliance, and risk mitigation are paramount, agentic AI is not about futuristic disruption. It’s about helping teams work smarter within existing constraints. While total autonomy may never be appropriate for many healthcare applications, the real-world use cases already emerging are pragmatic, measurable, and increasingly valuable.
What makes AI “agentic”?
It’s important to distinguish agentic AI from other types. While much of the conversation has focused on predictive and generative AI, agentic systems are uniquely suited to operational execution. They don’t just inform or inspire — they take action within defined boundaries. This distinction is critical in pharma, where workflows often involve repetitive, tightly regulated tasks that benefit from consistency and efficiency without compromising compliance.
AI systems can be categorized by both technique (e.g., rule-based, machine learning, deep learning) and function (e.g., predictive, generative, agentic). Agentic AI differs in that it doesn’t just provide insight. It acts.
This action-oriented capability introduces both opportunity and responsibility. To be effective, agentic systems must be built with a clear understanding of the task, its context, and its constraints — particularly in high-stakes environments like clinical operations or regulatory submissions. When thoughtfully designed, they become powerful tools for scaling expertise and reducing bottlenecks.
These systems can follow workflows, trigger decisions, and adapt outputs based on structured parameters. Greater autonomy makes them ideal for automating routine but critical tasks — provided the right safeguards and oversight are in place.
Where it’s already working: Three practical pharma use cases
- Streamlining research and discovery – Agentic AI is increasingly being used to support early research by generating hypotheses, scanning literature, and even identifying potential intellectual property conflicts. By automating the groundwork, researchers can focus on evaluating and refining ideas rather than manually gathering information.
- Automating content creation across functions – In areas like medical affairs, marketing, and regulatory documentation, agentic systems are being deployed to manage workflows that span literature or internal documentation review, copywriting, and compliance checks. Multiple agents can work in tandem — drafting language, validating output against standard operating procedures, formatting documents — all while maintaining traceability and regulatory standards.
- Driving regulatory compliance with greater speed and accuracy – From converting submission data to required formats (like CDISC) to monitoring processes for deviations in real time, agentic systems can help ensure consistency and completeness in regulatory workflows. The result: fewer errors, faster review cycles, and stronger audit readiness.
The next frontier: AI as a decision-making partner
One of the most exciting emerging use cases for pharma is the ability to use agentic systems to interrogate both internal and external data sources in support of strategic decision-making.
For example, consider the critical question of which drug candidates to advance into clinical development. This decision hinges on a complex mix of preclinical and clinical data, market intelligence, competitive landscape, and regulatory precedent. AI agents can be trained to synthesize this information, highlight gaps or red flags, and generate comparative summaries that allow leadership teams to make faster, better-informed choices.
It’s not about replacing human judgment. It’s about reducing the time spent sifting through data and increasing the time spent interpreting it.
What’s holding companies back?
Agentic AI holds real promise, yet there are several persistent barriers that prevent broader adoption:
- Insufficient understanding of the value that different types of AI (e.g. predictive versus generative) can deliver for different use cases.
- Underestimating the relevance of traditional AI as a tool or input for agentic AI.
- Skepticism around AI-generated output combined with underutilization of robust agentic architectures.
- Lack of established governance processes to handle risks like data fragmentation or model drift.
The solution? Start small and scale.
Organizations should take a risk-based approach, starting with administrative and low-risk tasks, then gradually scale to include more critical applications like clinical operations or patient-facing tools. This mirrors how the industry already manages innovation: cautious, measured, and accountable.
From insight to action: Building a smarter, more agile pharma future
The pharmaceutical industry is no stranger to complexity, regulation, or the high stakes of innovation. What’s changing is how organizations are choosing to respond to these pressures. AI, particularly agentic AI, is fast becoming part of the answer.
The value isn’t just in automation for automation’s sake. It’s in freeing human talent to focus on strategy, innovation, and patient outcomes, while delegating the heavy lift of repetitive, rules-based, and data-intensive tasks to systems that can handle them efficiently and reliably.
But success with agentic AI won’t come from racing to adopt the flashiest tools. It will come from strategic alignment, understanding where AI can create real value, mitigating risk through thoughtful implementation, and ensuring transparency and oversight at every step.
For biopharma companies, this means starting with foundational use cases, like streamlining literature or documentation reviews, augmenting regulatory submissions, and accelerating compliant content creation, and then evolving toward more complex higher-risk applications, such as decision-making support.
Agentic AI isn’t about chasing hype. It’s about enabling better outcomes for teams, for patients, and for the business as a whole.
Image: Yuichiro Chino, Getty Images
Basia Coulter is a Partner in Healthcare and Life Sciences at Globant, specializing in digital transformation and AI strategy. With a deep background in pharma, biotech, and medtech innovation, she has led major AI deployments across the sector—transforming clinical trials, enhancing patient recruitment, and streamlining R&D and care delivery. Basia is passionate about solving complex industry challenges, including legacy tech limitations, compliance barriers, and building trustworthy AI systems. Her hands-on experience at the intersection of technology and science positions her as a trusted voice on how AI can drive meaningful progress in healthcare.
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