AI maturity in clinical development: A practical framework and next steps for leaders

The challenge is not solely technological. It is operational, cultural, and strategic. Organizations must assess their readiness to adopt AI, not as a collection of tools, but as a core capability embedded across the clinical development lifecycle. A practical framework, grounded in real-world examples, highlights how leading organizations and regulators are evolving their approaches to support AI at scale. Understanding and advancing AI maturity is not just an aspirational goal, it is now an essential step for clinical leaders seeking to move from experimentation to enterprise-wide impact.

AI is no longer a peripheral innovation. It is becoming a foundational component of modern clinical development. From protocol optimization and site selection to patient recruitment and safety signal detection, AI is already influencing critical decisions. In a recent survey of Pharma and BioTech leaders, AI was identified as the top driver of innovation in clinical trials, followed by innovative trial designs, and personalization.

Importantly, regulatory expectations are evolving in parallel. In 2025, the US Food and Drug Administration (FDA) launched an agency-wide initiative to integrate generative AI across all centers, following the successful completion of its first AI-assisted scientific review pilot. These efforts are designed to reduce administrative burden, accelerate review timelines, and improve consistency in regulatory decision-making.

Simultaneously, the National Institutes of Health (NIH) is finalizing its AI Strategic Plan, which outlines a progression from current data-driven analytics to semi-autonomous and eventually fully autonomous AI systems. The plan emphasizes reproducibility, transparency, and shared governance—principles that will shape how AI is evaluated and adopted across the research ecosystem.

Together, these developments signal a shift: AI maturity, the extent to which an organization has adopted, optimized and scaled AI to deliver their business aims, is no longer optional. It is becoming a prerequisite for regulatory alignment, operational efficiency, and scientific credibility. Failing to understand your organization’s AI adoption trajectory or maturity level presents significant strategic, operational, and even ethical risks for Clinical Development organizations, who may pursue suboptimal investments and negatively impact patient outcomes.mpact patient outcomes.

A framework for assessing AI maturity

To help organizations benchmark their progress, we developed a four-level AI maturity model tailored to clinical development:

This model is designed to be diagnostic rather than prescriptive. Organizations may find themselves at different maturity levels across domains such as data readiness, infrastructure, governance, and workforce capability.

What maturity looks like in practice

Organizations that are advancing toward higher levels of AI maturity are not simply experimenting with tools, they are embedding AI into the core of their clinical workflows to build intelligent enterprises, treating it as a foundational capability rather than a peripheral enhancement.

Novartis has taken a similar strategic approach, applying AI to optimize site selection and patient recruitment. By analyzing historical trial data, demographic patterns, and site performance metrics, the company has improved enrollment efficiency and reduced screen failure rates, addressing two of the most persistent challenges in trial execution.

At Roche/Genentech, AI is being used to refine protocol design through natural language processing. By mining thousands of historical protocols and feasibility assessments, the organization has reduced the frequency of amendments and improved operational readiness. This illustrates how AI can enhance both the quality and agility of trial design.

Johnson & Johnson, meanwhile, has focused its AI efforts on pharmacovigilance. By automating the detection of safety signals across global trials, the company has accelerated its ability to identify and respond to adverse events—strengthening both patient safety and regulatory compliance.

These private-sector advancements are mirrored by innovation at the regulatory level. The FDA is actively reshaping expectations through its internal deployment of generative AI. By automating document summarization and scientific review tasks, the agency is demonstrating how AI can enhance—not replace—human expertise. These initiatives reflect a broader shift toward AI-enabled transparency, efficiency, and accountability across the clinical research ecosystem.

Scaling AI requires more than technical capability

Scaling AI demands a reconfiguration of how clinical development is organized and governed. The application of AI is driving significant disruption to existing functional groups within the development value chain and driving demand for an evolution of role types and skills sets, particularly in relation to pivotal monitoring and data management roles.

Key shifts in the operating model:

This means moving away from rigid, functionally isolated teams toward integrated squads that bring together clinical operations, data science, regulatory, and IT. These teams are empowered to co-develop solutions, iterate quickly, and respond to real-time insights without waiting for top-down directives or lengthy change control processes.

Governance shifts from centralized bottlenecks to federated models, where oversight is maintained, but innovation is not stifled. Decision-making becomes faster and more data-driven, with AI surfacing patterns and risks that would otherwise go unnoticed.

Perhaps most importantly, the shift from project-based pilots to platform-based capabilities means that AI is no longer confined to isolated experiments. Instead, it becomes a reusable, scalable asset, which is embedded in the organization’s digital backbone and accessible across studies, functions, and geographies.

As organizations evolve through different stages of the maturity model, those that succeed are the ones that empower their teams and partners – sites, participants, investigators, regulators, CROs and vendors – to explore new ways of working within clearly defined guardrails. They invest in change management, not just technology. They build trust in AI outputs by making them explainable and auditable. And they create space for ongoing experimentation, while ensuring alignment with regulatory expectations and clinical integrity.

Workforce implications: Building AI fluency

AI maturity also requires a shift in workforce capability. It is not sufficient to hire data scientists in isolation. Clinical teams must be equipped to interpret AI outputs, understand model limitations, and integrate insights into decision-making.

Key workforce strategies include:

• Training clinical operations teams to engage with AI-generated risk signals
• Redefining data management roles as data product owners
• Establishing communities of practice to foster AI literacy and shared learning
• Incentivizing experimentation and cross-functional collaboration.

Change management is critical. Executive sponsorship, clear communication, and targeted training are essential to building trust and adoption across the organization.

Next steps: Assess, align, advance

To move from experimentation to impact, organizations should:

1. Assess their current maturity across key domains using a structured framework.
2. Align leadership around a shared vision for AI adoption and governance.
3. Advance through targeted investments in infrastructure, talent, and operating model transformation.

AI has the potential to transform clinical development, but only if organizations are prepared to evolve. Maturity is not defined by the sophistication of a single model, but by the ability to operationalize AI across the enterprise in a way that is scalable, compliant, and sustainable.

This article was first published in Clinical Leader.