gentic AI represents the next generation of AI in business. This type of AI has the “agency,” or capability, to make decisions, take action, and even interact with outside environments beyond the original data independently, on behalf of a person or system. Gartner projects that in less than three years (by 2028), 33% of enterprise applications will incorporate agentic AI capabilities, which also indicates significant adoption to date.

Imagine you’re managing a complex clinical trial involving thousands of patients across multiple research sites. You need to ensure that patient enrollment targets are met, data collection remains compliant, and protocol deviations are minimized. If you ask generative AI for advice, it might produce a helpful checklist based on historical best practices or industry guidelines. However, it stops there; the execution is still on you.

Agentic AI takes it a step further. It doesn’t just provide recommendations and shortcuts. It takes action: It can generate and execute tasks such as monitor real-time enrollment rates, spot delays at specific sites, and reroute recruitment efforts to optimize results. It can even flag compliance issues before they occur and suggest corrective actions. In this scenario, agentic AI collaborates with you like a helpful research or laboratory assistant that solves problems and drives the trial forward.

Unlike generative AI, which depends solely on human input to generate output, agentic AI uses autonomous “agents” to perform tasks, make decisions, and independently interact within their environments without constant human administration. These agents can automate routine tasks like data collection or payroll. They can also execute staggeringly complex tasks, such as interacting with diverse systems, analyzing pertinent data sets, and providing thoughtful responses and personalized solutions. Like generative AI, agentic AI can create reports about that data in simple, human language.

Agentic AI is helping to improve various aspects of drug development, and clinical trials more specifically. Some of the leading early use cases are recruiting patients (to quickly identify and target eligible patients); optimizing protocol design (including study duration); analyzing large data sets to identify trends, patterns, and insights that would be missed by human analysis; monitoring adverse events in real time, flagging potential adverse events early; and accelerating drug discovery by identifying most-promising drug candidates. More use cases are evolving every day, so this list is probably old already.

• Agentic AI will redefine life sciences, according to many experts, by accelerating innovation. Imagine that you’re conducting an orchestra. Each instrument has its part, perfectly timed to contribute to building toward the final crescendo. In a way, that’s what agentic AI can do. In life sciences, tasks and tools are soloists or individual agents operating under the conductor to deliver flawless commercial planning and execution.
• Agentic AI is more than just automation. Each agent excels in a unique task; for instance, they can excel specifically at budget allocation for brand managers and segmentation for marketing but also site assessment or patient enrollment of trials. Combining these moving pieces using traditional workflows takes months (sometimes years!) of manual processes and endless spreadsheets and presentations. However, agentic AI transforms this process and reduces timelines from six to 18 months to less than two months.

The drug development industry has so many different pain points where agentic AI can help, but where to start? What’s the best potential use case and how close are we to it? Global Forum asked three experts these two questions and compiled their responses below.

This shift doesn’t just improve efficiency: It fundamentally transforms clinical development by collapsing timelines, expanding access, and reducing reliance on fragmented manual processes. As agentic AI absorbs more of this operational load, technology’s share of clinical budgets will inevitably grow, ushering in a new paradigm where digital agents work alongside clinical teams to drive faster, more inclusive, higher-quality research.

Agentic AI differs fundamentally from traditional automation in that it doesn’t just execute predefined tasks; it proactively reasons, adapts, and makes decisions within dynamic and complex workflows. While automation streamlines repetitive actions like data entry or scheduling, agentic AI can autonomously coordinate cross-functional activities, generate context-aware insights, and respond to emerging trial conditions in real time. This distinction creates a new level of leverage in clinical development roles that are historically bogged down by tactical burdens.

Clinical trial managers, for example, often spend disproportionate time triaging site queries, tracking enrollment deviations, or manually reconciling protocol changes. With agentic AI handling these operational tasks, those professionals are free to focus on strategic priorities, such as optimizing trial design, improving diversity in enrollment, and accelerating timelines.

Similarly, medical monitors, data managers, and clinical operations leaders can reallocate effort from task management to decision-making, scenario planning, and innovation. In essence, agentic AI doesn’t just make clinical trials more efficient; it shifts the human role from operator to orchestrator, unlocking greater strategic capacity across the organization.

How close is this potential to actually happening?
When integrating agentic AI into clinical development, it’s critical to differentiate low-risk operational opportunities—where adoption can proceed rapidly—from high-sensitivity areas that will require close collaboration with regulators. This distinction helps clinical operations leaders prioritize implementation while ensuring compliance and trust.

Low Regulatory Risk Areas (High Operational Opportunity)
These domains involve well-understood workflows with low patient risk, where agentic AI can be deployed with minimal regulatory friction:

• Site Engagement and Communication: Automating responses to site queries, coordinating visit schedules, and tracking site activation milestones can be safely managed by AI agents under human oversight.
• Trial Document Drafting and Review: Agents can assist with protocol synopses, monitoring plans, and regulatory submissions using validated templates, enabling faster iterations and quality checks.
• Study Start-up Orchestration: AI can coordinate cross-functional start-up tasks (e.g., budget negotiations, ethics submissions), detect delays, and recommend mitigation strategies.
• Operational Analytics and Reporting: Synthesizing enrollment trends, query resolution rates, and site performance key performance indicators falls squarely within existing data governance practices.
• Patient Engagement Logistics: Chat-based agents can support patients with reminders, visit coordination, and FAQ handling—already seen in decentralized and hybrid trials.

These areas are largely process-driven and benefit from the AI’s ability to coordinate, summarize, and adapt without intervening in medical decisions or participant-level data analysis.

Higher Regulatory Sensitivity Areas (Require Alignment with Regulators)
In these domains, agentic AI interacts with elements of the trial that may affect data integrity, participant safety, or protocol compliance, requiring proactive dialogue with regulatory agencies and potentially new validation frameworks:

• Synthetic Control Arms/Simulated Participants: Using AI to generate synthetic patient data as comparators in lieu of control groups introduces questions of statistical validity and bias and will need regulator consensus on methodology.
• Adaptive Protocol Management: Allowing agents to propose or execute changes to study design based on real-time data requires rigorous traceability, version control, and regulatory pre-approval frameworks.
• Eligibility Decision Support: When AI agents assist or automate inclusion/exclusion decisions, transparency, interpretability, and audit trails must meet the standards of informed clinical judgment.
• Medical Signal Detection and Adverse Event Triage: Agents surfacing safety concerns or prioritizing adverse events must operate under strict safety monitoring systems, including real-time human oversight.
• Dynamic Informed Consent Workflows: Using AI to adapt or personalize consent content based on participant behavior or language introduces ethical and compliance complexities.

Agentic AI is immediately deployable in low-risk, high-efficiency areas such as document workflows, site coordination, and patient communications, offering quick wins with little regulatory resistance. However, as its use expands into core clinical decision-making and data generation, alignment with regulators will be essential to ensure trust, compliance, and long-term viability. A phased, risk-aware approach—starting with operational augmentation and progressing toward deeper clinical integration—will enable safe, scalable adoption. As agentic AI grows in capability and reach, ethical design, bias mitigation, and explainability will be key pillars in sustaining regulatory and public trust.

The adoption of agentic AI signals more than just a technological upgrade; it heralds a new era of human-machine collaboration in clinical research, where intelligent systems act as strategic partners, accelerating progress while upholding the rigor and empathy that define responsible science.

— Michelle Longmire, Medable