At the 2025 SCOPE Europe conference, industry leaders examined how artificial intelligence (AI) is reshaping feasibility assessment and patient recruitment models. Speakers from Pfizer’s predictive analytics team outlined an agile, service-focused approach that embeds feasibility early in program design and sustains it through study execution. The discussion emphasized a continuous visibility model—where enrollment data and site performance are monitored dynamically rather than episodically—to optimize trial activation and resource allocation.
Panelists noted that AI-driven systems now enable eligibility assessments in minutes rather than days, processing both structured and unstructured patient data. Traditional manual review methods, which required coordinators to analyze 10–12 patient files per hour, have been replaced by algorithms capable of screening thousands of records in the same time frame. These efficiencies not only accelerate patient identification but also expand geographic and demographic reach across previously underrepresented populations.
Reframing Internal Capabilities and Data Strategy
The Pfizer team described its internal “predictive analytics incubator,” operating with the agility of a startup within a large organization. This structure allows rapid proof-of-concept (POC) testing and iteration—accepting quick failures and scaling successful prototypes. Rather than relying solely on vendor partnerships, the group developed in-house models contextualized for clinical language, enabling nuanced interpretation of trial parameters such as cost drivers and unit correlations.
This approach responds to a persistent challenge in the industry: externally sourced AI solutions often lack domain-specific understanding and rely on limited or proprietary datasets. By leveraging internal expertise and data from ongoing studies, Pfizer aims to maintain greater control over algorithmic learning and adaptability. Once pilot systems reach maturity, they transition to the company’s digital infrastructure teams for global scaling—a model that balances innovation speed with compliance and standardization.
Executives argued that while vendor collaborations remain valuable, internal builds ensure data governance and proprietary model development aligned with the company’s therapeutic and operational priorities. The model underscores a growing trend across big pharma: creating self-contained innovation units that test, validate, and then transfer technology into enterprise pipelines.
Automation, Cost Reduction, and the Agentic Workflow
A focal point of the session was Pfizer’s “agentic workflow,” designed to streamline redundant feasibility surveys and automate due diligence across global sites. The framework progresses through three phases, starting with legally straightforward automation layers before advancing toward more complex integrations. The team reported that automating repetitive survey tasks has already freed substantial human resources, enabling staff to focus on higher-value activities such as relationship management and strategic planning.
Complementary automation initiatives—including an AI-based due diligence platform and a site activation tracker—are expected to go live by year’s end. Together, these tools reduce administrative friction, compress study timelines, and directly support cost-saving targets. Speakers linked these initiatives to measurable business outcomes: shortened activation cycles, reduced manual workload, and increased predictability in feasibility timelines.
Human Intelligence, Change Management, and Future Skills
While automation dominated the discussion, panelists repeatedly emphasized the irreplaceable human dimensions of trust, empathy, and stakeholder relationship-building. The future of feasibility teams, they suggested, depends on hybrid skill sets that combine technical literacy with interpersonal and organizational agility. Effective change management—ensuring teams can adapt to AI augmentation rather than resist it—was cited as critical to sustainable transformation.
Training programs now prioritize adaptability, data fluency, and cross-functional collaboration. By embedding AI literacy alongside traditional project management competencies, organizations prepare staff to interpret algorithmic outputs, communicate insights effectively, and maintain oversight over automated processes.
Integrating Conversational AI for Site Engagement
A significant portion of the discussion addressed Pfizer’s development of an AI chatbot designed to improve the feasibility survey experience. Initially introduced to reduce repetitive queries, the system evolved into a tool for real-time site engagement. Investigators can now pose study-related questions directly within the chat interface, eliminating multi-day delays associated with manual email correspondence between site personnel and feasibility specialists.
Beyond efficiency, the chatbot personalizes interactions, tailoring responses based on protocol-specific data and prior site behavior. This approach creates a “site-tailored interaction” model that streamlines survey completion and improves satisfaction among investigators. The team reported faster turnaround times and higher completion rates for feasibility assessments conducted through the chatbot system compared to traditional forms.
Balancing Build-versus-Buy and the Broader Industry Context
A lively Q&A explored the perennial question of building internal tools versus adopting vendor technologies. Panelists argued that while partnerships can accelerate deployment, in-house solutions allow customization around business-specific needs and maintain data integrity. External vendors, one participant noted, often lack access to global datasets—particularly outside regions like the U.S. and Western Europe—limiting their ability to support multinational feasibility modeling.
The conversation reflected an emerging consensus across the industry: hybrid ecosystems that combine internal innovation with selective vendor collaboration yield the most resilient AI infrastructures. Data interoperability, regulatory compliance, and scalability remain top considerations as companies evaluate these pathways.
Outlook: From Efficiency Gains to Strategic Transformation
The session concluded with a forward-looking perspective: AI in feasibility is no longer an experimental supplement but an operational necessity. The next phase will integrate predictive recruitment modeling with financial analytics, enabling dynamic adjustment of budgets and timelines based on real-time site and patient data.
Participants envisioned a near future where feasibility, costing, and recruitment planning converge within unified, intelligent ecosystems—allowing sponsors to simulate study scenarios, forecast enrollment bottlenecks, and adjust investment decisions on the fly.
Ultimately, the panel agreed that technological maturity must be matched by cultural readiness. As one speaker noted, “AI cannot replace relationships, but it can give us the time to build them.” The industry’s challenge now lies in leveraging automation not just to cut costs, but to elevate human capability—transforming feasibility from a static assessment into a living, data-driven strategy for accelerated, patient-centered research.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
