Contributed Commentary by Brian Mallon, ICON 

July 11, 2025 | Feasibility, site selection, and study startup remain some of the most time-consuming and unpredictable phases of drug development. They are also tightly interconnected, making even small delays or inefficiencies during this period especially impactful, rippling across a study’s timeline and budget and affecting site relationships and patient access. Ultimately, this affects the speed at which new therapies reach market.  

To improve performance, sponsors and CROs must design more proactive, insight-driven strategies that leverage the abundance of data at our fingertips. A strategic approach to data can turn challenges into opportunities, optimising feasibility and study startup for more predictable trial delivery.  

In this article, we explore how AI-driven site selection, predictive analytics and automation are enhancing speed, efficiency, and predictability in feasibility, as well as startup for faster, more cost-effective studies.  

Opportunities in Evolving Data Infrastructure  

Feasibility, site selection, and study startup are persistent challenges in clinical development. However, innovative approaches are now transforming these operational hurdles into strategic enablers of faster, more predictable clinical development.  

Sponsors and CROs are moving away from manual, fragmented workflows and embracing connected data, automation, and collaboration to support trial readiness. Centralized data environments that integrate internal and external sources, such as historic site performance, enrolment rates, protocol complexity, and geographic and therapeutic trends, are proving invaluable in driving efficiency and identifying opportunity for innovation. 

By harnessing these integrated environments, organizations can replace static surveys and anecdotal feedback with dynamic, data-driven feasibility assessments to support precise site selection and optimal study design. Predictive analytics enhance early risk assessment and allow for more exact enrolment forecasting, helping to strengthen site partnerships and build a more agile, responsive feasibility function.  

Innovation Meets Intuition: Data-Driven Confidence 

Emerging technologies and AI innovation are enabling a more informed, data-rich approach to decision making during study startup. AI-powered evaluations help to identify high-performing sites by analysing a broad range of factors, from operational capability and engagement history to speed of delivery.  

This analysis is often informed by strategic site networks built on long-term partnerships, helping sponsors to deliver faster enrolment, especially in specialised therapeutic areas or underserved populations.  

Human expertise remains vital in generating higher quality insights at site selection. When specialists analyze the study protocol and prioritise nuances in the selection criteria, such as therapeutic fit, patient access, operational execution, and experience, they strengthen the predictive power of AI.  

Sponsors working with pre-qualified, well-supported sites, whose profiles are enriched with real-world data and digital tracking tools, benefit from smoother activations and higher retention. The combination of data, technology, and human insight provides a more balanced approach to boost site performance, reduce attrition, and accelerate enrollment. 

Smarter Feasibility for Stronger Starts 

A well-structured feasibility assessment is crucial for identifying risks early, refining trial design, and setting realistic expectations. Cross-functional teams now use a blend of historical data, therapeutic expertise, and structured site input to tailor feasibility strategies. 

In addition to traditional feasibility surveys, qualitative interviews with investigators provide critical insights into regional standards of care, protocol expectations, site capacity, and patient availability. These insights help refine protocols and optimize site selection.  

Meanwhile, digital platforms that streamline communication, training, and documentation, from onboarding through to administration, are helping to reduce burdens on site staff. Integrated technology helps teams move faster with fewer errors and minimizes noncompliance risks.  

Over-selecting sites by a strategic margin up front mitigates the risk of dropouts and ensures that studies stay on track. The volume of sites selected provides flexibility and ensures seamless site activation while reducing the risk of delay or site shortages. Most importantly, this approach provides more predictability around the site activation dates and expected performance, which are critical milestones in the overall study delivery plan.  

Too often, startup activities are treated as separate from recruitment and execution plans, when they are operationally interconnected. A data-informed, integrated approach to feasibility and startup enables more dynamic planning, realistic forecasting, and ensure alignment across the entire study lifecycle. 

Prioritizing the Site Experience 

As protocols become more complex, sites face increasing administrative and operational pressures. Supporting them effectively is critical for sustained success.  

User-friendly digital portals give sites greater visibility into timelines, deliverables, and updates, helping reduce confusion and improve communication and collaboration.  

Listening is also key. Proactively collecting regular feedback ensures that sites feel heard and supported, allowing sponsors to respond to quickly to challenges and strengthen engagement. 

Smaller sites or those new to a therapeutic area may benefit from targeted training, added resources or specialized help to reach their full potential. Providing this level of continuous assistance through dedicated teams and centralized tools helps ensure all sites perform optimally. Sites that are well-informed and engaged from the outset recruit faster, stay active longer, and contribute more meaningfully to study outcomes. 

Smooth is Fast: Automation and Activation 

Manual, siloed workflows across teams can lead to inconsistencies, misaligned timelines and avoidable errors. Treating study startup as a coordinated, end-to-end workstream rather than a sequence of isolated hand-offs improves overall coordination, accountability, and execution quality. 

AI-enabled tools are now being used to assist with critical activities, such as contract generation, regulatory submission tracking, and task automation. These tools reduce time to site activation, improve document accuracy, and support more consistent delivery. As these systems learn from prior activities, they evolve, offering templates that reflect historical performance and regional requirements. The result is a smoother, faster start-up process with stronger alignment across functions and fewer unexpected challenges. 

Predictability is Exciting 

Sponsors increasingly expect greater transparency and real-time visibility into study progress. Predictive modelling and fit for purpose automated intelligence tools now deliver these insights, and this predictability brings exciting opportunities for progress.  

Interactive dashboards and custom data visualization tools highlight key performance metrics, flag bottlenecks early for corrective action and allow stakeholders to track critical components, such as site progress, document completion, and contracting timelines at a glance. By translating data points into actionable intelligence sponsors gain the clarity and control needed to build more collaborative partnerships and scale clinical development with more confidence. 

Strategic Startup, Confident Delivery 

Innovation in study startup will continue to advance, incorporating AI-powered feasibility modelling that simulates recruitment scenarios and refines protocol design before trials even begin. These activities will increasingly take place within integrated digital ecosystems that support a holistic approach to feasibility, site selection and startup planning.  

Optimising feasibility and study startup offers significant opportunities to strengthen trial delivery from the earliest stages. Organisations that embrace a comprehensive digital environment and adopt a data-driven, technology-enabled, and site-centric approach will accelerate timelines, improve data quality, and foster stronger partnerships. 

As clinical trials continue to grow more complex, reimagining early-phase planning as a strategic driver of success is essential. Sponsors and CROs who capitalise on these evolving tools and methodologies will be better positioned to navigate complexity, improve predictability and bring new therapies to patients faster. 

Brian Mallon is Executive Vice President of Sites, Patients & Study Start Up at ICON plc. Based in Dublin, he brings over 15 years of experience at ICON, where he has held a range of senior leadership roles across legal, procurement, and commercialisation. Prior to his current role, Brian led ICON’s Commercialisation & Outcomes division, overseeing global teams in Real World Solutions, Medical Device & Diagnostic Research, and Patient Centred Services. He is known for building high-performing teams and driving innovation across operational functions that support the delivery of clinical trials. He can be reached at Brian.Mallon@iconplc.com.