By Rajneesh Patil, Head of AI and Innovation for Clinical Development, Syneos Health
Earlier this year, my colleague Matt Harrington suggested that clinical leaders should stop asking whether their organizations have an “AI strategy”, not because strategy doesn’t matter, but because the question often produces the wrong conversation.
Too often, discussions about AI become conversations about platforms, vendors, models and roadmaps. What leaders really need to understand is how AI is being used to predict what matters, optimize what works and automate activities that improve clinical development performance. These are the capabilities that ultimately determine whether technology creates measurable value or becomes another underutilized investment.
Those questions remain important and continue to provide a practical framework for evaluating AI investments. But as organizations shift from experimentation to implementation, a new question is reshaping conversations among sponsors, technology leaders and clinical operations teams: How should clinical leaders think about agentic AI?
At its simplest, agentic AI describes systems that can do more than generate answers or complete tasks. They can evaluate information, determine next steps, interact with tools and systems, and adapt as conditions change in pursuit of a defined objective.
New products, announcements and demonstrations appear almost weekly. Nearly every technology provider now seems to be discussing agents, orchestration, autonomous workflows or digital workers. Yet despite the growing excitement, many organizations are still trying to determine whether agentic AI represents a meaningful step forward or simply the latest industry buzzword.
For clinical leaders, the challenge is not understanding the technology itself. The challenge is understanding where it creates value and how to separate practical opportunities from industry hype. That requires moving the conversation beyond what agentic AI can do and toward how organizations can apply it responsibly, pragmatically and at scale.
From Generating Outputs to Coordinating Clinical Workflows
Understanding where agentic AI adds value begins with understanding how it differs from the AI capabilities most organizations are already deploying today. Much of the AI currently used in clinical development is reactive by design:
- A user asks a question, and the system provides an answer.
- A team requests a document draft, and the system generates it.
- A workflow reaches a specific trigger, and an automated process executes the next step.
These capabilities deliver measurable value. They reduce manual effort, accelerate timelines and improve consistency, and many organizations are already realizing meaningful gains through these applications. But they largely operate within the boundaries of a specific task or workflow.
Rather than responding to prompts, agentic systems pursue objectives. They evaluate information, determine next steps, interact with tools, assess outcomes and adapt as conditions change. In practical terms, they move beyond completing individual tasks and begin coordinating multi-step activities toward a defined goal.
That distinction may seem subtle, but it fundamentally changes how AI can be applied in clinical development, where critical decisions rarely occur in isolation. Study startup, enrollment planning, monitoring, medical review and submission readiness all require information to move across teams, systems and functions.
The organizations that realize the greatest value from agentic AI will not necessarily deploy the most agents. Their advantage will come from creating environments where intelligence compounds across the development lifecycle. As information flows more effectively between planning, startup, execution and oversight, each activity strengthens the next: protocol insights improve enrollment decisions, enrollment data refines monitoring strategies, operational observations strengthen quality and compliance, and so on. The value comes not from individual agents working independently, but from the connected intelligence they create together.
Looking further ahead, this coordinated approach introduces the possibility of a hybrid workforce where human expertise and digital labor operate together. Clinical teams remain accountable for scientific judgment, patient safety and regulatory oversight, while digital workers increasingly coordinate information, monitor workflows, synthesize knowledge and execute administrative activities. The organizations that learn to orchestrate this blended workforce effectively may unlock a new level of productivity across the development lifecycle.
Four Realities Clinical Leaders Should Understand About Agentic AI
The vision of agentic AI is compelling, but it requires more than just deploying intelligent agents. As organizations begin moving beyond experimentation, a consistent set of implementation patterns is becoming clear. Understanding these realities can help clinical leaders distinguish practical opportunities from industry hype, avoid unnecessary complexity and build AI capabilities that create lasting value.
Reality #1: Not Every Problem Requires an Agent
One of the first realities organizations discover is that not every problem should be solved with agentic AI.
The current enthusiasm surrounding agents can create pressure to apply them broadly, but many clinical development activities remain highly structured, deterministic and rules-based. In these environments, traditional automation is often more efficient, predictable and easier to validate.
Agentic AI becomes valuable under specific conditions:
- When conditions change frequently
- When decisions span multiple steps
- When workflows require coordination across systems
- When human expertise remains critical, but complexity slows execution
Reality #2: The Pilot Trap
Selecting the right use case is only the first step. The next challenge is scaling those successes across the organization.
Over the past several years, many life sciences organizations have successfully demonstrated that AI can improve individual workflows. For example, protocol teams can synthesize evidence to strengthen protocol design; operations teams can forecast enrollment and improve study planning; medical writers can accelerate document creation; and safety organizations can automate portions of case processing.
Each of these initiatives may deliver measurable improvements in productivity, quality or speed. The challenge begins when organizations try to move beyond isolated successes.
When AI capabilities operate independently, they become isolated islands of intelligence. They evolve independently, with each team optimizing its own process, data and technology. The result is a collection of successful AI initiatives that operate in parallel but rarely reinforce one another.
This is what we refer to as the Pilot Trap: the point at which organizations discover that scaling AI requires a fundamentally different set of capabilities than proving a single use case.
The conversation shifts from, “Can this capability work?” to “How do we create an environment where intelligence compounds across the organization?” Answering that question requires a different set of priorities. Instead of optimizing individual agents or isolated workflows, organizations need shared foundations that allow information, context and workflows to move seamlessly across functions.
That means investing in more than intelligent applications: it means building a unified data context, consistent governance and connected workflows that allow decision-grade intelligence to accumulate rather than remain confined within functional silos. Without those foundations, organizations often discover that scaling value is far more difficult than proving feasibility.
Reality #3: Technology Isn’t the Hard Part
As organizations mature in their AI programs, many discover that the greatest barriers to adoption are not technical — they’re organizational.
Technology receives most of the attention because it is the most visible part of the solution. Yet successful implementation depends just as much on process design, governance, change management and domain expertise as it does on model performance. In many cases, these organizational capabilities become the limiting factor long before the technology itself.
Clinical development is inherently multidisciplinary, and successful AI programs must be as well. Scientific expertise, operational experience, regulatory knowledge and technical capability each contribute a different perspective, but none is sufficient on its own. The organizations creating sustainable value are finding ways to integrate these perspectives early, ensuring that AI solutions are designed around real-world workflows rather than simply technical possibilities.
That reality is prompting organizations to rethink not only their technology strategies, but also how they build teams, develop new skills and organize work. New roles that bridge technical and domain expertise are beginning to emerge, reflecting a broader shift toward multidisciplinary AI operating models. Organizations that make these investments early will be better positioned to translate AI into sustained business value.
Reality #4: Autonomy Requires Guardrails
As AI systems move from reactive tools toward goal-driven behavior, governance becomes increasingly important. The more autonomy organizations give AI, the more confidence they need that its recommendations, actions and decision pathways remain transparent, explainable and aligned with scientific and regulatory expectations.
That is particularly important in clinical development, where AI may influence decisions that affect study quality, patient safety and regulatory submissions. Successful implementations are built on a shared data context, clearly defined operating boundaries and traceability by design, allowing organizations to understand not only what recommendations an AI system makes, but how it arrived at them.
Rather than treating governance as a compliance exercise, leading organizations view it as an enabler of trustworthy AI. By establishing objective standards for traceability, explainability, observability and accuracy, they create the confidence needed to apply AI responsibly in increasingly complex clinical workflows while preserving scientific integrity and human accountability.
Ultimately, the goal is not autonomous decision-making. It is decision-grade intelligence: AI that provides transparent, trustworthy recommendations that help clinical teams make faster, better-informed decisions while preserving expert judgment, scientific integrity and regulatory confidence.
The Next Phase of AI in Clinical Development
The life sciences industry has entered a new phase of AI adoption. The conversation is no longer about whether AI can create value — most organizations have already demonstrated that it can. The challenge now is translating that value from individual workflows into coordinated capabilities that improve performance across the development lifecycle.
For the past decade, clinical development organizations have focused on digitizing work. The next decade will be defined by how effectively they orchestrate intelligence across people, processes and technology. The organizations that make that transition successfully will not simply automate more work — they will create new ways of making faster, better-informed decisions that accelerate development and ultimately improve outcomes for patients.
You can explore additional perspectives on AI, clinical development and emerging technologies by visiting syneoshealth.com/ai.
The author would like to thank Tapasya Bhardwaj, Director of Regulatory Operations, Syneos Health, for her contributions to the article.