International Clinical Trials, Clinical Trials Supply and Logistics Supplement, August 2026, pages 20-22 . ©️ Samedan Ltd – Suvoda
Author: David Geismar, Senior Vice President, Global Client Delivery at Suvoda
AI in RTSM can take on manual, repetitive tasks under clear guardrails. The result: faster study builds and more time for teams to focus on the scientific and operational decisions that impact trials.
Clinical trial startup is one of the most coordination-heavy phases of drug development. Historically, study and vendor teams have spent significant time on technology and trial system setup: documenting detailed requirements, reviewing specifications, configuring and testing systems, and reworking configurations when protocols change. Getting to an approved study system is a critical step, but the current process is rigid and time-consuming. Not only does this work extend the startup timeline, but it pulls clinical and operational experts away from other areas where their judgment is most valuable.
The integration of artificial intelligence (AI) into Randomization and Trial Supply Management (RTSM), sometimes known as Interactive Response Technology (IRT), is changing where trial teams focus their attention. In AI-enabled RTSM, purpose-built AI agents, under human oversight, can now take on the structured, repetitive execution tasks required for system setup and maintenance. The positive impact is immediate: faster access to candidate systems, more agile reviews and updates, and less disruption when mid-study changes occur. Additionally, by shifting this tactical work to AI, it creates space for specialists across sponsors, clinical research organizations (CROs), and vendor services teams to focus on what humans do best: trial design decisions, site and patient support, and managing operational risk.
The significance of AI in RTSM is not simply automation. It is a shift in how study teams spend their time. When AI agents handle configuration-heavy tasks within clear guardrails, clinical and operational experts can spend more of their effort on the scientific and operational decisions that shape outcomes, rather than on transcribing those decisions into systems. As a result, teams have more room to navigate complex trials, adjust designs without losing momentum, and keep promising therapies moving toward the patients who need them.
Trial startup puts pressure on every team
Today’s clinical trials present a range of increasingly complex protocol, operational, data management, and regulatory challenges. Translating this complexity into a working, study-specific RTSM system requires detailed requirements, unambiguous specifications, and validation to satisfy both regulatory expectations and internal quality standards.
Traditionally, RTSM setup has followed a linear, time-consuming manual sequence:
- Requirements gathering
- Specification drafting, review, and approval
- System configuration and internal testing
- User acceptance testing (UAT) preparation and execution
- Change cycles triggered by protocol amendments or operational updates
Vendor services teams spend a large share of their time on configuration and documentation. Sponsor study teams spend hours reviewing lengthy requirements documents, clarifying design decisions, and waiting for a candidate system to be ready.
The opportunity here is not only to configure systems more quickly. It is to reduce the administrative burden surrounding startup so experts can shift more of their attention to keeping the trial moving: anticipating operational issues, supporting sites, and making informed trade-offs when the protocol meets real-world constraints.
Agentic AI changes where expertise is applied
Common AI tools, such as chatbots and assistants, respond to individual questions or need one interaction at a time. Agentic AI systems go further. Composed of families of autonomous AI agents, these systems plan and carry out sequences of tasks on a user’s behalf, within defined constraints and with human experts setting the rules.
In AI-enabled RTSM, domain-specific AI agents are embedded within the system build. They follow structured instructions written by subject matter experts, who also review and sign off on outputs before anything moves forward. They can help to:
- Translate approved protocol and design documentation into study-specific configurations and customizations
- Create requirements and specification documents
- Produce validation test scripts and automation assets
- Regenerate affected study components when changes occur
Applying agentic AI to these structured, repeatable tasks compresses some of the most time-intensive parts of technology setup and maintenance. For example, change orders that once required broad rework can be handled through targeted updates by AI agents, with human experts reviewing and validating the results. Preliminary testing by Suvoda has shown that using AI to handle execution tasks in this way can reduce kickoff-to-UAT timelines, in some cases, by up to 80 percent.1
For sponsor study teams, reducing the time spent managing specification documents results in more time applying clinical and operational judgment to enrollment readiness, site engagement, and supply planning. For vendor services organizations, it shifts time away from manual system build and toward the proactive consultation and support that sponsors and CROs value most, such as weighing protocol decisions that carry risk, asking questions study teams have not yet thought to ask, and bringing lessons from prior trials into the design of the next one.
Guardrails that make AI usable in a regulated environment
AI can help study teams move faster, but in a regulated trial environment, it must do so in a way that meets the same expectations for control, quality, and accountability as any trial-critical system. Additionally, humans must remain actively engaged and accountable for the choices and approvals that affect participants, drug supply, and trial integrity.
Guardrails for responsible AI use in RTSM focus on four areas:
- Human control: While AI agents manage execution support, they should not replace human judgment. Domain experts define the instructions agents follow, review the outputs, and sign off on what is approved for use.
- Data and access protection: Study data must remain within a secure, closed, and controlled environment and never used to train public or third-party AI models. The same role-based access controls and rules for randomization, drug assignment, and participant data that govern the clinical system should also apply to anything AI does. Users and AI components only access what they are authorized to see. A zero-retention approach, where AI retains data only long enough to complete a specific request, should also be considered another baseline layer of protection.
- Clearly defined operating boundaries: AI should operate within clearly defined boundaries. In RTSM, agents can prepare study-specific configurations and documentation and automate testing, but they should not alter validated core code. From a technology perspective, keeping a clear boundary between the validated platform and the configurable study layer helps preserve platform stability and limits AI to the space where teams already expect study-specific changes to occur.
- Transparency: AI-supported work must be understandable, traceable, and auditable. Systems must log every action, including inputs, outputs, and resulting configuration changes or artifacts. This record allows teams to see what happened and how a result was reached.
Together, these guardrails make it possible to use AI in RTSM to accelerate trials while addressing the concerns that operations and technology leaders care about most: output quality and reproducibility, traceability of decisions, data protection, and regulatory risk. They also align with emerging guidance on AI in drug development and clinical trials, which emphasizes human-centric design, alignment with existing standards, strong data governance, and clear accountability over the full life cycle.
AI support that extends beyond startup
The impact of AI in RTSM does not end once a system goes live. While agentic AI is particularly well-suited to accelerate startup and mid-study change work, AI assistants can also reduce friction during trial execution by helping teams find information more quickly.
After activation, study teams need visibility into real-time operational details, such as enrollment velocity, depot inventories, drug lot releases, and site performance trends, to quickly perform day-to-day activities and make faster, better-informed decisions. When an AI assistant can help operations and site specialists quickly find the information they need from approved data sources and reference materials, teams spend less time navigating technology and more time managing the trial.
This is the broader promise of AI in RTSM. Not only does it help accelerate setup, but it creates a more responsive environment throughout the study where the technology remains transparent, and experts can stay focused on participants, sites, and study outcomes.
A more human-centered model for trial delivery
The significance of AI-enabled RTSM lies in how it redistributes work across the trial ecosystem. Study teams will continue to face tight timelines, complex designs, and the realities of globally distributed sites. The opportunity is to use domain-specific AI to take on the repetitive configuration and information-retrieval tasks that slow them down, while human experts spend more time on the decisions that require their experience and judgment.
AI can help sponsors and CROs move faster without sacrificing the quality and control that trials demand. In an environment where time, complexity, and operational burden shape which therapies are tested and how quickly they reach patients, AI-enabled RTSM is a meaningful step toward a human-centered model where clinical and operational experts can support more studies and technology serves to support improved clinical trial delivery.
1 Suvoda. “RTSM (IRT) that takes weeks off study startup.” Accessed June 22, 2026.
Author
David Geismar
SVP, Global Client
Delivery, Suvoda