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Conducting a clinical trial is an intricate operational challenge. They can cost millions and take several years to complete - figures that reflect not just the scientific complexity, but the administrative burden that comes with coordinating across systems, departments and regulatory frameworks.
Most organizations are still managing this complexity through fragmented processes, manual data entries and endless email chains. The results are timeline delays, compliance risks and costs that spiral beyond initial projections. Meanwhile, a small number of forward-thinking companies are beginning to automate these workflows and they're seeing measurable advantages in both speed and operational reliability.
The question isn't whether this automation wave will reach clinical trials more broadly. It's a question of how organizations can create advantages by being among the early adopters instead of playing catch-up later. An integral technology to enable this will be Agentic AI - systems that don't just respond to commands, but can pursue complex, multi-step objectives with genuine autonomy.
However, the regulatory landscape has fundamentally shifted. With the EU AI Act now in force and guidelines that emphasise technology-enabled, risk-proportionate approaches, organisations must navigate an entirely new compliance framework when pursuing operational excellence.
Understanding Agentic AI in a Clinical Context
Traditional AI operates in a request and response pattern. You provide input, receive output and decide what to do next. This works well for discrete tasks but breaks down when you need sustained, adaptive action across interconnected processes.
Agentic AI represents a fundamentally different approach. These systems can:
- Maintain context across multi-step workflows
- Integrate data from several different sources in real-time
- Adapt their approach based on changing conditions
- Escalate to human oversight at precisely defined decision points
In clinical trials, this translates to systems that can manage entire process segments autonomously while maintaining the oversight and control those regulatory environments demand.
For organizations operating across European markets, this capability becomes particularly valuable. Different regulatory frameworks, varying data localization requirements, and complex stakeholder coordination are exactly the kinds of challenges that benefit from intelligent automation.
The new Regulatory Reality
New AI regulation, updated clinical guidelines, and data protection requirements creates both challenges and opportunities for intelligent automation in clinical trials
The EU AI Act classifies AI used in healthcare, medical devices, or systems affecting health and safety as potential “high-risk”, requiring transparency, oversight and risk management. Rather than being a barrier, this regulatory clarity actually enables strategic automation deployment. Organizations that design agentic AI systems with built-in compliance architecture can operate with greater confidence across the EU.
Simultaneously, new guidelines explicitly encourage the use of digital tools and risk-based monitoring in clinical trials. The updated framework recognizes that traditional manual oversight simply cannot manage the complexity and volume of modern trial operations. This, on its own, creates regulatory support for the intelligent automation that agentic AI enables.
GDPR Article 22 adds yet another critical dimension, reinforcing the need for transparency and oversight in AI systems, especially when automated decisions might significantly affect individuals. It guarantees the right to human intervention, explanation, and contestation. This fits naturally with agentic AI’s architecture where human-supervised autonomy is a core design principle.
Practical Implementation Areas
Based on our work with life sciences organizations, four areas show immediate potential:
Intelligent Document Orchestration
Rather than manual document collection, agents can automatically gather, validate and cross-reference materials from Clinical Trial Management systems (CTMS), Electronic Trial Master File (eTMF) and Electronic Health Records (EHR). They identify information gaps, flag inconsistencies and ensure regulatory packages meet submission standards before human review. This saves hours of admin work and reduces errors.
Predictive Timeline Management
Agents can continuously track timelines, milestones and resource availability. If, for instance, a recruitment deadline is at risk or a compliance check is overdue, they can escalate proactively, enabling amendment long before it becomes a problem.
Adaptive Stakeholder Reporting
AI agents can support more tailored reporting by compiling relevant information for different stakeholders. Whether it's operational overviews for sponsors, compliance checklists for regulatory teams or patient tracking views for site coordinators. These summaries can be updated dynamically and routed automatically, reducing the burden of manual reporting.
Guided Communication Support
In regulated environments, communication is more than just sending messages. It’s about traceability, timing and control. AI agents can assist in managing communication workflows by drafting updates, tracking approval chains, and routing information to the right stakeholders at the right time.
Measurable Outcomes
The objective of deploying Agentic AI in clinical operations isn’t about replacing experts. It’s about giving them more time to focus on science, strategy and care. By implementing intelligent automation in clinical operations, organisations can see outcomes such as:
- Shorter trial timelines through efficient process automation
- Lower regulatory risk via consistent compliance handling
- Reduced overhead by eliminating repetitive admin work
- Improved stakeholder collaboration through streamlined communication
All without having to completely rebuild your existing tech stack.
The strategic advantage becomes clear when you consider competitive dynamics. While others struggle with manual coordination across sponsors, CROs, and regulatory bodies, organizations with intelligent automation can respond to changes faster and operate with greater predictability.
Implementation Considerations
In order to succeed with Agentic AI in clinical trials, careful attention is required for several factors:
Process Selection Strategy
Start with workflows that are both high-impact and well-defined. Document management and timeline monitoring often provide good pilot opportunities because they're structured enough for automation while delivering clear value.
Data Infrastructure Assessment
Agents require reliable, consistent data to make sound decisions. This means evaluating integration between your CTMS, EDC, and eTMF systems, ensuring data quality standards, and establishing clear governance around information access.
Regulatory Compliance Architecture
In GxP environments, any automated system must demonstrate transparency, explainability, and complete auditability. This requires proper validation protocols, change control procedures, and clear documentation of how and why decisions where made for regulatory inspection.
System Integration Complexity
Consider how agents will interact with your existing clinical technology ecosystem. Integration with EDC platforms, regulatory submission systems, and data localization requirements across European markets adds layers of technical complexity that require careful planning.
Partner Capability Requirements
Successful implementation sits at the intersection of AI technology, clinical operations expertise, regulatory compliance knowledge, and systems integration capability. Few organisations possess all these competencies internally, making strategic partnering integral for success.
Our Perspective
At twoday, we've observed that successful Agentic AI implementation in clinical trials isn't primarily a technology challenge - it's an organizational design problem. The companies that succeed are those that can reimagine their processes around intelligent automation while maintaining the control and oversight that regulatory environments demand.
Our experience across Nordic markets has shown us that regulatory complexity, rather than being a barrier to automation, can actually be an advantage. Organizations that get this right can operate more efficiently across multiple jurisdictions while maintaining higher compliance standards than manual processes typically achieve.
The key insight we've developed is that Agentic AI works best when it amplifies human expertise rather than replacing it. The most successful implementations preserve human judgment for strategic decisions while delegating operational coordination to systems that excel at managing complexity consistently.
This represents a fundamental shift in how intelligent systems can support regulated, high-stakes processes. Not just better tools, but genuine collaborative systems that enhance organisational capability.
Curious about how this might apply to your clinical operations?
With the regulatory landscape now providing both frameworks and incentives for intelligent clinical trial automation, the window for strategic advantage through early adoption is narrowing rapidly.
We'd welcome the opportunity to explore your specific challenges and discuss what intelligent automation could mean for your organisation's competitive position.