The Non-negotiable Foundation for Scaling AI in Clinical Trials

The increasing complexity of clinical trials makes AI’s potential more critical than ever. And yet, most organizations see its value confined to initial pilot projects and struggle to scale across the enterprise. 

The life sciences industry is at a pivot point. To realize benefits like faster study builds, smarter automation, and improved data quality, we must solve a fundamental challenge: establishing the foundation to move AI from early-stage experiments to validated, production-ready protocol deployment.

This was the central theme of Medidata’s live webinar, AI Everywhere: Architecting the Future of Clinical Trials. The panel, featuring leaders from AbbVie and Everest Group, explored the necessary foundation for achieving true AI adoption and scale in life sciences. Discover the takeaways below.

The Strategic Shift: Simplifying Complexity

While AI adoption accelerates, life sciences maintains a necessary, measured pace rooted in patient safety and stringent checks and balances. But accelerating innovation requires a shift to a cohesive ecosystem approach. Success hinges on finding partners who embrace collaboration and openness.

“Those companies who have been working on interoperability, who have been building a partnership ecosystem, who've been investing in some core assets, but also partnering elsewhere to build those capabilities in-house. Those are the firms that have been succeeding.”

– Manu Aggarwal, Partner, Everest Group

The Process Imperative: Foundation Before Feature

Successful AI adoption requires rigorous process refinement as the necessary groundwork. Organizations must optimize internal workflows before integrating AI tools to avoid merely automating inefficiency. This strategic focus prevents the common pitfalls of isolated pilots that fail to deliver enterprise-wide value:

“One of the barriers that we don’t think about enough is actually process and the need to refine the process before applying AI...there can be death by a thousand POCs (proof of concepts).”

– Brooks Fowler, VP of Clinical Data Strategy and Operations, AbbVie

Ultimately, organizational readiness—including generalized training—must empower teams to adapt to the new, AI-enabled processes.

From Lab to Core: Embedding AI in Daily Workflows

Moving from experiment to production requires dedicated architecture built for scale and leverage from comprehensive, end-to-end data. This strategic shift moves AI from specialized applications into everyday workflows, targeting areas of maximum return like optimizing protocol design, predicting enrollment, and quickly identifying data anomalies.

According to the latest survey “The State of AI in Clinical Trials: Today and Tomorrow” conducted by Medidata in partnership with ISR Market Research and clinical trial leaders, 87% of respondents report that AI is already improving protocol design and optimization. 70% of AI users report improved data accuracy, while 61% have seen their data collection processes become more streamlined.

The ultimate goal is to transform manual processes like edit checks and study builds, freeing up users for higher-value activities.

The Dual Mandate: Speed Meets Experience

Long-term technology commitments require a dual mandate: balancing speed and quality with a superior user experience for sites and patients. For AbbVie, the decision to solidify a long-term partnership with Medidata across data management and patient experience tools (Medidata Rave and eCOA) was about a shared philosophy for the future. Medidata's aggressive vision for innovation was the key to accelerating the time required for trial setup and deployment.

“The aggressive nature that Medidata has shown with regard to deploying AI on the platform was definitely part of it. We were looking for a partner who we thought could be as aggressive as we wanted to be.”

– Brooks Fowler, VP of Clinical Data Strategy and Operations, AbbVie

The Evolving Role of the Human

As AI agents handle routine tasks, the role of the human operator evolves; critical thinking becomes more important, not less. The focus shifts from executing tasks to rigorous oversight, ensuring the agent’s output remains accurate and true to the scientific intent of the protocol. This essential partnership relies on transparent AI systems that build user trust, letting the workforce transition into a thinking model over a doing model.

"There can be a lot more idea people and less execution people…fundamentally, we see society and our workforce moving towards that thinking model—as opposed to the doing model that has been the bread and butter for us for the last 2 to 3 decades.”

– Manu Aggarwal, Partner, Everest Group

Conclusion

Innovation moves at the speed of trust, and the most successful AI strategies combine robust technology, aggressive partnership, and an unwavering commitment to refining core processes. By shifting success metrics away from traditional KPIs and toward concrete outcomes, organizations are moving beyond lengthy pilots and achieving real returns on AI adoption. This commitment helps the industry simplify complexity, enhance productivity, and achieve better patient outcomes, defining the future of clinical trials.

Watch the full webinar on demand: