From Pilot to Powerhouse: Scaling AI for Real Clinical Impact
The conversation around artificial intelligence (AI) in clinical development has matured. No longer confined to pilot programs or theoretical discussions, AI is becoming a tangible component of how sponsors and contract research organizations (CROs) approach protocol design, trial oversight and operational decision-making. This evolution reflects a broader shift—from innovation as an isolated budget item to AI as an embedded capability that supports scalable, system-wide efficiency.
At Syneos Health, cross-functional teams are deploying real-world applications of AI in life sciences—especially in areas where complexity, volume and variability benefit from intelligent automation and augmentation. These efforts aren’t about replacing human expertise, but rather enhancing it in ways that improve consistency, shorten development timelines and better align resources with areas of high risk or opportunity.
Connect with our experts to explore how AI-powered solutions can support your next trial.
Protocol Optimization with AI: Precision at Scale
Protocol optimization is a high-impact use case for AI in clinical trials. Traditionally, this work has depended on individual expertise, historical templates and literature reviews. But with AI, teams can reach beyond those limitations and analyze tens of thousands of completed protocols, extracting patterns that inform inclusion/exclusion criteria, streamline assessments and reduce amendment rates.
Patrick McMahon, Managing Director, R&D Advisory at Syneos Health, elaborates. “We’re seeing tools that can proactively identify elements of a protocol that are likely to introduce downstream challenges; whether that’s patient burden, recruitment complexity or operational feasibility. AI helps teams make more informed decisions upfront, reducing the likelihood of having to change course down the road."
This capability is particularly useful when teams face tight deadlines or constrained resources. Comparative study selectors powered by AI allow clinical designers to base their decisions not only on personal experience but on a global database of learnings—delivered at a speed and scale that would be impossible manually.
This approach supports more proactive risk mitigation, especially in large or complex trials where the sheer volume of data can obscure early indicators of deviation or noncompliance. AI’s role in this instance is not to decide, but to inform or guide human judgment with timely, context-rich insights.
AI Agents in Clinical Trial Oversight: From Reactive to Predictive
The role of AI in trial oversight is expanding. Agent-based tools can now monitor trial execution in near real-time, scanning data for quality issues, predicting site performance variability and surfacing signals that warrant human review.
Dyke Simpson, Executive Managing Director and Data, AI & Analytics Services Practice Lead in Consulting at Syneos Health notes that the shift from reactive to predictive oversight is among the most practical benefits. “We’re moving from manual data reviews to continuous, automated surveillance. That doesn’t eliminate the need for experienced monitors; it gives them better signal detection and more time for strategic interventions.”
Implementing Enterprise AI in Clinical Development: Key Considerations
Adopting AI in clinical development is not just a technology decision—it’s an organizational one. Teams that scale from pilot to enterprise tend to follow several consistent practices:
- Clarify objectives early: What specific decision or process is the AI meant to support, augment, or replace?
- Build cross-functional teams: Successful implementations involve clinicians, technologists and operations experts working together.
- Modernize workflows: AI can’t thrive in processes designed for paper. Re-engineering workflows to integrate and adopt intelligent automation is often essential.
- Establish governance: Clear protocols around validation, data integrity and regulatory alignment are required to build trust in AI outputs.
Addressing Bias in AI: A Prerequisite for Trust
Bias is a real-world challenge that can influence protocol design, patient eligibility and site selection if left unchecked. That’s why governance practices must include clear processes for bias detection and mitigation. From the outset, teams should assess the quality and representativeness of training data, establish model explainability protocols and plan for continuous monitoring over time.
“The best way to prevent bias later is to do the hard work now: clean your data, validate your sources and make your governance infrastructure flexible enough to evolve,” says McMahon. In other words, bias mitigation is an ongoing responsibility that’s essential for both scientific integrity and equitable trial execution.
At its best, AI serves as a set of intelligent tools that complement human expertise—allowing organizations to work faster and smarter while maintaining the scientific and ethical rigor that clinical development demands.
Want to go deeper? Connect with our experts to explore how AI-powered solutions can support your next trial.
Contributors
Patrick McMahon | Managing Director, R&D Advisory, Consulting
Dyke Simpson | Executive Managing Director and Lead, Data, AI & Analytics Services Practice, Consulting
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