Agentic AI

A complete guide to AI agents in clinical trials, includingwhat they are, the infrastructure they need, where people stay in charge, and how to start. Discover how agentic AI is transforming clinical trials, and what's next. And, learn about ontologies, human-in-the-loop governance, build vs. buy, continuous validation/governance, and more.

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What exactly is a forward deployed engineer?

If you have spent any time around AI and enterprise software this year, you have probably heard the term FDE, or forward deployed engineer. It sounds like a buzzword, but the role is why we’re able to solve real problems for our clinical trial clients in record time.

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Industry leaders share real-world insights on using agentic AI to streamline clinical trials, accelerate decisions, and improve study execution.

The ground has shifted for FSO (functional service outsourcing) and Unitized FSP (functional service providers) contract research organizations (CROs). 

For years, the playbook for managing these types of CRO economics was familiar. Sponsors negotiated rates while CROs managed headcount and utilization around those rates .

That playbook still holds true today in theory. However, the conditions underneath it have shifted enough that it no longer produces the results it used to. Funding is tighter, sponsors are smaller and more price-sensitive, timelines are compressing ahead of the patent cliff, and AI has moved from an experiment on the roadmap to a baseline expectation in every RFP.

None of that is unique to any one segment of the market, but these pressures land differently for FSO and unitized FSP providers than they do for full-service CROs. That’s because these businesses are built on rate cards for CRAs, monitors, and other functional resources. When sponsors squeeze rates or expect more output per unit, there's no broader program fee to absorb the hit, here the unit economics are the business.

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The way clinical trials are monitored is once again about to change. 

This isn’t the first time clinical trial monitoring has evolved. Over the past three decades, the industry has undergone two major transformations in how monitoring is performed.

For much of the 1990s and early 2000s, monitoring relied on frequent on-site visits and extensive source data verification (SDV), with many studies aiming to verify nearly every data point. While rigorous in intent, this approach became increasingly difficult to sustain as trials grew larger and more complex, delivering diminishing returns relative to its cost and operational burden.

The industry responded by adopting risk-based monitoring (RBM) and centralized monitoring, shifting from exhaustive verification to a targeted, data-driven approach focused on the risks that mattered most. This evolution was reinforced by FDA and EMA guidance and ultimately codified in ICH E6(R2) in 2016.

While RBM improved efficiency and data quality, it did not fundamentally change how monitoring work was performed. CRAs still spent much of their time manually reviewing data, reconciling information across systems, documenting findings, and coordinating follow-up activities. 

We are now entering a third shift. Unlike the first two, which primarily redistributed how monitoring effort was allocated, this one fundamentally changes the old rules on who and what is monitoring trial performance. 

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The clinical trials industry is at an operational inflection point. Despite decades of process refinement, drug development timelines have continued to lengthen, and manual workflows still account for a significant portion of skilled operator time across key trial functions. The emergence of large language model-based AI platforms has introduced a new variable into this equation, with multiple vendors now positioning their technology as a solution to these structural inefficiencies.

Two categories of vendors appear frequently in sponsor and CRO evaluations. The first are  general-purpose AI platforms, whose large language model capabilities have become some of the most widely benchmarked in the industry. The second are purpose-built clinical technology platforms with an established operational presence across decentralized and hybrid trial environments. 

Both have made substantive investments in life sciences AI in 2026. Their approaches, however, differ significantly in architecture, regulatory readiness, and deployment scope. This analysis examines those differences to help sponsors and CROs assess which type of platform is better suited to their operational requirements.

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Discover what to look for from artificial intelligence tools in the clinical trial market.

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Artificial intelligence is reshaping clinical trial operations in 2026. Sponsors and CROs are no longer evaluating standalone tools. They are choosing platforms that can connect with existing systems, automate complex workflows, and scale without disrupting ongoing studies. This guide breaks down the vendors best positioned to integrate with the clinical trial infrastructure you already have.

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Artificial Intelligence is rapidly transforming clinical research. From patient recruitment and protocol design to medical writing and data review, AI-powered solutions are becoming embedded across the clinical development lifecycle. More recently, the emergence of Agentic AI (systems capable of planning, reasoning, and executing multi-step workflows with varying degrees of autonomy) has generated significant excitement throughout the industry.

However, not all AI solutions are created equal. While many vendors promise dramatic improvements in efficiency and productivity, clinical trial organizations operate in one of the most highly regulated environments in the world. Success depends not only on technical performance but also on compliance, validation, governance, security, and trust.

As sponsors, CROs, and technology teams evaluate potential AI partners, they need a framework that extends beyond traditional software procurement criteria. The following considerations can help organizations assess both AI and Agentic AI vendors and identify solutions that are truly ready for clinical research.

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Clinical data speaks dozens of languages. Ontologies are the translator. Discover how life sciences teams are using semantic layers, AI agents, and MCP connectors to cut months of data harmonization down to days.