AI-Enhanced Clinical Project Management For Cell And Gene Therapy Clinical Trials

By Jessica Cordes, senior consultant, Clinical Excellence GmbH

The pace and complexity of clinical trials have accelerated significantly in recent years, particularly for companies working with cell and gene therapies (CGTs). For small biotech organizations, where teams are often stretched across multiple responsibilities, managing these clinical trials requires more than just operational diligence. It demands real-time coordination, precise and detailed planning, and data-driven foresight.

AI is increasingly proving to be a practical and powerful tool in this landscape. Among the many areas AI supports, two of the most critical for CGT clinical trials are real-time patient journey coordination and predictive enrollment. Both present unique challenges due to the nature of the investigational products and the limited patient populations often involved. Missteps in either domain can create significant delays or jeopardize clinical trial outcomes. But when approached strategically, AI can mitigate risk, optimize planning, and support decision-making in ways that were previously impossible for lean clinical operations teams.

Real-Time Patient Journey Coordination: Synchronizing Supply, Site, And Patient Activities

Clinical trials with autologous CGTs involve highly individualized manufacturing processes. Each investigational product is typically created from the patient’s own cells, and timing is critical. A delay in patient preconditioning, a shipment delay due to missed information flow, or site staff unavailability can result in a missed infusion window and loss of product.

Real-time coordination becomes essential not only across clinical operations but also involving logistics partners, manufacturing units, and clinical trial sites. AI helps centralize and streamline this complex process.

Technology platforms are increasingly capable of integrating manufacturing status updates, courier logistics, and electronic patient schedules. When paired with predictive modeling and machine learning, they can proactively manage risk and recommend scheduling changes in real time.

How AI Adds Value In Practice:

• Integrated Decision Support: AI-powered scheduling systems connect disparate data sources (e.g., patient appointment calendars, manufacturing slots, and site readiness indicators, etc.) to ensure all preconditions align for treatment. This reduces the reliance on spreadsheets and manual coordination.
• Real-Time Exception Handling: If the cryogenic shipper carrying the cell product encounters a temperature deviation, AI alerts the logistics team, reroutes the shipment, or identifies backup plans instantly. Similarly, if a patient postpones their hospital admission, systems can automatically adjust the manufacturing and delivery schedule.
• Dynamic Task Management: AI can orchestrate workflows by triggering downstream activities. For example, once apheresis is complete, the system can automatically notify the manufacturing partner, update inventory status, and prompt the site pharmacist to prepare for final product receipt.

A practical approach to leveraging AI in CGT clinical trials begins with mapping all patient-related touchpoints (such as apheresis, hospitalization, and infusion) and identifying exactly where data is captured along the process. Organizations should consider piloting a single therapy batch at one clinical trial site to thoroughly evaluate each coordination point before scaling up more broadly. Selecting technology tools that support open Application Programming Interfaces (APIs) is also crucial, as this enables seamless integration with systems like eTMF, EDC, CTMS, or LIMS. To ensure effective use of AI-driven processes, it’s important to establish clear standard operating procedures (SOPs) for responding to AI-generated alerts and defining the appropriate escalation pathways, keeping a human in the loop. By laying this groundwork, the organizations can better harness AI to streamline workflows and reduce the risk of delays or errors.

Ensuring reliable data entry is fundamental to the effective implementation of AI in clinical trial workflows. If scheduling details or laboratory reports are outdated or incomplete, AI systems cannot deliver optimal results. To support a smooth rollout, organizations should appoint an AI implementation lead responsible for overseeing training, system configuration, and establishing feedback mechanisms. Additionally, it is important to monitor for alert fatigue; not every change warrants escalation, so fine-tuning notification thresholds is essential to maintain efficiency without overwhelming staff.

Predictive Enrollment: Targeting Feasibility, Eligibility, And Recruitment Bottlenecks

For CGT clinical trials, recruitment isn’t just about finding enough patients. It’s about finding the right patients, in the right place, at the right time. Eligibility criteria are often highly specific, involving rare biomarkers, disease stage, or Human Leukocyte Antigen (HLA) types. Traditional feasibility surveys and site self-reporting fall short in predicting actual enrollment performance.

This is where AI can provide exceptional value. Predictive models can forecast enrollment success by analyzing a combination of historical trial data, epidemiological statistics, genetic prevalence data, and real-time patient availability in EHR systems, if possible.

AI platforms specialize in natural language processing (NLP) and structured query models to identify patient matches within large hospital systems. These platforms can surface eligible candidates based on nuanced language in medical records or pathology reports, even when diagnoses or biomarker details are embedded in free text.

In-Depth Use Cases For Predictive Enrollment:

• Genetic and Biomarker Matching: AI tools can cross-reference eligibility criteria with clinical genomics databases, predicting the likelihood of identifying suitable patients at each participating clinical trial site. This enables clinical project managers to proactively redistribute recruitment targets based on data.
• Feasibility Accuracy Improvement: Traditional feasibility questionnaires are often inflated. AI-driven site selection tools compare sites’ historical enrollment in similar clinical trials, factoring in inclusion/exclusion criteria to predict actual yield.
• Recruitment Scenario Modeling: By adjusting site mix, eligibility criteria stringency, and/or screening procedures, AI simulation tools can forecast time-to-first-patient and total enrollment duration under various scenarios.

To initiate AI-powered patient enrollment in clinical trials, a strategic approach is essential. Organizations should start with a select group of clinical trial sites that already utilize EHR-integrated tools. Clearly defining the clinical data fields the AI should target (such as references to “B-cell lymphoma,” “CD19+,” and “refractory” within patient notes, ICD codes, etc.) will help optimize the identification process. Furthermore, it is important to implement a structured workflow for prescreening patients flagged by AI, ensuring that only the most promising candidates are routed to trial coordinators for further evaluation and potential recruitment.

By harnessing AI-driven predictive enrollment strategies, organizations can expand the diversity of clinical trial populations, identifying eligible participants beyond the traditional confines of academic medical centers. This approach not only mitigates the risk of underpowered clinical trials caused by recruitment challenges but also empowers research teams to respond in real time. For instance, when enrollment slows, AI tools can recommend protocol modifications or suggest new geographic regions to target, ensuring ongoing adaptability and success throughout the clinical trial process.

One of the primary barriers to implementing AI-driven patient enrollment in clinical trials is the presence of data silos and non-interoperable systems, which can restrict access to crucial EHR information. Ensuring ethical oversight and safeguarding patient privacy are also essential considerations whenever automated EHR mining is involved, requiring close collaboration with site compliance officers. Additionally, training AI models effectively depends on the availability of robust historical data. For organizations without prior experience in CGT clinical trials, leveraging external data sets may be necessary to develop accurate and reliable models that can guide initial efforts.

Smarter Clinical Trials Require Smarter Tools

Clinical project managers overseeing CGT clinical trials are at the center of an exceptionally complex matrix of tasks and stakeholders. From coordinating bespoke therapies to navigating niche patient populations, every decision has amplified operational impact. AI presents a way to streamline and de-risk two of the most sensitive areas in this environment: patient journey coordination and enrollment.

As we’ve seen, AI doesn’t just make operations faster, it makes them smarter. It equips clinical project managers with insights that enable timely interventions, more accurate forecasts, and stronger partnerships with sites and vendors. Importantly, it also helps small biotechs punch above their weight, leveraging automation and intelligence to operate with the precision of larger sponsors.

Adopting AI doesn’t require a complete transformation from day one. Pilot a focused application, perhaps improving site selection or automating patient scheduling alerts, and expand based on feedback and measurable value. What matters is creating momentum and learning as you go.

Ultimately, AI will not replace clinical project managers. But it will become one of their most essential allies, allowing them to focus on what truly matters: guiding teams, supporting patients, and delivering breakthrough therapies with confidence and clarity.

Implementing An AI Strategy

Successfully implementing AI in clinical trial project management begins with clarity and intention. Organizations should first define where AI can deliver the greatest value, whether accelerating patient recruitment, refining site selection, optimizing clinical trial oversight, or streamlining data flows. Articulating specific actionable objectives is key to keeping the strategy focused and measurable. Early collaboration among senior management, clinical project managers, and stakeholders helps ensure that these goals address the most critical needs within the clinical trial process.

Equally important is a realistic assessment of data readiness. Teams must evaluate the availability, quality, and interoperability of existing data, especially EHRs and clinical trial data acquisition tools. Identifying gaps, data silos, and compliance issues at the outset enables stronger governance and paves the way for successful integration of AI. Engaging IT and data privacy experts early on ensures that both technical and ethical requirements are addressed.

The success of AI initiatives depends on a cross-functional approach. Building a multidisciplinary team that includes clinical trial specialists, data scientists, IT professionals, compliance officers, and patient advocates provides a solid foundation for effective oversight and decision-making. Clear delineation of roles and escalation pathways helps manage risk and foster accountability throughout the process.

Instead of attempting a broad transformation, organizations are advised to launch targeted pilot projects. Focusing on specific high-impact use cases (such as predictive site selection or automated patient prescreening) enables clinical trial teams to set clear metrics for success and collect valuable feedback. Early wins not only demonstrate tangible value but also inform the case for broader AI adoption.

Selecting the right technology is another pivotal step. Surveying available AI platforms and vendors for compatibility, ease of integration, scalability, and transparency ensures that the selected solutions align with organizational needs. Prioritizing tools that provide clear explanations of AI-driven decisions helps maintain trust and regulatory compliance.

Change management cannot be overlooked. Providing comprehensive training, detailed documentation, and ongoing support empowers staff to adapt and thrive in an AI-enhanced environment. Open communication about the role of AI, emphasizing its function as an ally rather than a replacement, encourages a culture of innovation and continuous improvement.

Finally, organizations should establish robust metrics to monitor pilot performance and outcomes. Using real-world data to refine AI models and workflows enables iterative improvement and adaptation as new challenges appear. Celebrating successes, learning from setbacks, and maintaining transparency throughout the journey are essential to sustaining engagement and momentum.

Through this measured stepwise approach, clinical project management teams can confidently harness the power of AI, enhancing efficiency, agility, and the overall success of clinical trials while retaining a human-centered focus on patient care and breakthrough therapies.

About The Author:

Jessica Cordes started her clinical operations career in 2009, working at various companies, including Big Pharma and several small to midsize biotech companies. She gained extensive experience on different levels from country study management to global study management and, since 2018, leadership in clinical operations. During her time at Medigene and Immatics, she structured the clinical operations department, built cohesive global teams, and implemented GCP and ATMP-compliant processes. For more than 12 years, she has been working in oncology clinical trials (including hemato-oncology as well as solid tumors) and with ATMPs since 2018. Since 2023, she has been working as an independent consultant and trainer, supporting small companies in building their clinical operations group and setting up their clinical trials for success. She provides a GCP refresher course via her Clinical Excellence Training Academy.