A look at how efficiency, access, platformization, AI, non-traditional players, and regulatory recovery are expected to reshape clinical operations in 2026.
As sponsors, CROs, regulators, and technology vendors head into 2026, clinical operations leaders are navigating a shifting landscape shaped by efficiency pressures, expanding ecosystems, and the growing expectation that artificial intelligence (AI) becomes foundational rather than experimental. While many of these trends have been building for years, industry leaders now expect them to converge in ways that materially change how trials are designed, staffed, and delivered.
Over the past year, sponsors have pushed aggressively to improve efficiency across feasibility, site selection, and enrollment, increasingly relying on AI and digital technologies to accelerate timelines. According to Liz Beatty of Inato, this
One of the most common efficiency tactics has been narrowing trials to a smaller group of familiar research sites to streamline selection, contracting, and startup. While effective on paper, Beatty noted that this approach further concentrates trials in limited locations, reducing geographic reach and limiting who can realistically participate.
Rather than abandoning
In 2025, organizations across biotech, pharma, CROs, and technology vendors began working to capture more of their value chain through new partnerships and collaborative ecosystems. These efforts are gradually breaking down historical silos. In 2026, this
Non-traditional players expected to gain influence in 2026 include compounding pharmacies, telehealth providers, and medical-grade consumer wearable manufacturers. New payment models such as
As non-traditional players expand their roles, stakeholders are being pushed to reconsider boundaries between regulated and non-regulated environments. These distinctions carry implications for payment models, privacy expectations, access to care, and the handling of experimental medicines across the industry.
Platformization refers to consolidating fragmented tools into unified, end-to-end systems. In 2026, industry leaders expect this concept to move from theory into practice as technology, data standards, and operational models are harmonized into intelligent systems. This shift is expected to mark the beginning of the end for piecemeal clinical trial innovation.
Traditional EDC and protocol management systems are expected to give way to “living protocols” supported by automated data capture and regulatory harmonization, including ICH M11 and revisions to E6. These systems go beyond digitization, enabling dynamic, self-learning models that support faster and more inclusive trials.
Living protocols are dynamic, machine-readable protocols created from libraries of biomedical concepts. Supported by ICH M11, they emphasize consistency in data reporting and continuous optimization. This approach allows for faster development cycles and increased reuse of data across studies.
Hyper-personalized protocol tailoring, supported by seamless integration with healthcare datasets, is expected to mature in 2026. This will enable more targeted recruitment and allow teams to tune trial designs during execution without the historical barriers associated with protocol amendments. Study teams may also experiment with multiple delivery approaches that can be adjusted rapidly with minimal portfolio disruption.
After a significant reduction in capacity and loss of experienced staff, the FDA has focused primarily on maintaining core review functions. Policy development and guidance work slowed in 2025. In 2026, a gradual recovery is expected, though with a prolonged refractory period as momentum is rebuilt.
Clinical development leaders can expect a slow resumption of engagement opportunities, such as scientific advisory meetings. However, concerns remain for innovators, as the industry continues to look to the US for
The impact of
A sharp divide is emerging between organizations embedding AI fluency across all clinical processes and those relying on isolated use cases. By 2026, AI fluency—measured through talent, governance, and operational agility—is expected to become a primary determinant of organizational survival.
Layoffs and automation are expected to drive a major shift in skill requirements. Demand is projected to rise for clinical data product managers, digital trial architects, and AI governance leads. Adjacent roles, such as monitoring and data management, are also expected to converge as automation removes redundant processes.
Industry leaders point to several additional developments, including broader access to secondary data through AI-powered retrospective analysis, increased use of in silico and synthetic trial models to reduce costs and risk, and the continued expansion of non-traditional players offering new diagnostic, monitoring, and care delivery models.
Together, these shifts suggest that 2026 will be less about experimentation and more about execution—forcing clinical operations leaders to decide which capabilities become foundational and which are left behind.
Editor’s note: This FAQ article was generated based on two previous pieces of Applied Clinical Trials content—
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