Artificial intelligence (AI) is becoming widely adopted in clinical data management, but the most persuasive use cases are rarely the broadest. For data management teams within sponsors, contract research organizations (CROs) and sites, the key value lies in applying AI at specific points in the workflow to make study builds faster, more consistent and easier to review.
That was the central message from a recent Zelta webinar with Alimentiv, which explored the foundational processes for building a 24-hour study. As Mark Laney, senior director of sales engineering and partnerships at Zelta, described it, the 24-hour validated study build is a “North Star”: a target for building the validated foundation of a study more efficiently, from protocol interpretation through electronic data capture (EDC) configuration, validation and export readiness.
1. Accelerating clinical study build workflows
The idea of a 24-hour study build can be compelling, particularly in an environment where clinical trials are under pressure to start faster without compromising quality. However, this ambition should be understood as a process and technology direction rather than a universal operational guarantee. Complex studies that are first built on a new platform, or trials in unfamiliar therapeutic areas, may require more time. The more useful question is where AI and automation can reduce avoidable manual effort while preserving the review points that protect quality.
Laney positioned the concept to speed up the more repeatable elements of study build that can be automated. These include interpreting the protocol, identifying the right case report forms (CRFs), configuring the database, validating the build, and preparing downstream exports. What technology cannot remove is the need for sponsor alignment, client review, requirement setting and expert judgement.
Chris Walker, who leads clinical data management and programming teams at Alimentiv, made a similar point from the CRO perspective during the online event. Even when technology accelerates individual tasks, Walker highlighted how teams still need to liaise with clients, manage expectations, and make decisions that reflect the specifics of the study.
2. AI and protocol interpretation
Every EDC build begins with the protocol, which contains the endpoints, visit schedule, inclusion and exclusion criteria, instruments, assessments, and footnotes that define what data must be collected and when. Historically, translating that protocol into data collection requirements has been a highly manual process.
This is one of the most promising areas for AI. A clinical trial protocol often contains sections sufficiently structured for first-pass extraction, including the assessment schedule, study endpoints, and eligibility criteria. AI can help identify what needs to be captured and suggest how those requirements should flow into the EDC build. In addition, AI can review previous studies and study libraries to identify existing form components that could be re-used based on the new protocol. This means that instead of spending the earliest stages of study manually extracting every requirement, experts can focus on reviewing the AI-generated interpretation, checking the source protocol and resolving nuance.
3. Building standards before scaling automation
Data standards are one of the most important prerequisites for faster study builds. During the webinar, Walker described Alimentiv’s journey from reusing forms from previous studies to building a more formal standards program. Similarly to many organizations, the team began with practical reuse. Over time, as more people worked across more studies, the risk of inconsistency increased. Alimentiv responded by creating templates for commonly used forms, strengthening naming conventions, introducing request processes and eventually managing standards through a dedicated data standards manager and cross-functional committee.
Prebuilt and validated forms can reduce the rebuild effort, support automation, and improve consistency across studies. They also give AI-enabled tools a clearer foundation for recommending CRFs, mapping requirements and identifying where a new study differs from previous work.
4. Focusing AI in repeatable, logic-based tasks
Targeted AI is more likely to be successful than blanket automation. The strongest use cases are logic-based, repeatable, or performed at scale. In clinical data management, this can include medical coding, initial study design assistance, Clinical Data Acquisition Standards Harmonization (CDASH) aligned from suggestions, range checks, window checks, and first drafts of build components.
Medical coding is a useful example because it often requires searching large dictionaries and applying consistent judgement. AI can help narrow the field and surface likely options, while the coder retains responsibility for review and confirmation. Similarly, an AI-assisted study design workflow can suggest CRF structures or identify forms from a standards library, but the data manager still decides whether the suggestion is appropriate.
5. Designing validation and human oversight into the workflow
The most effective AI adoption strategies have oversight built into the process. In a regulated environment, human-in-the-loop review is part of responsible automation. Teams need checkpoints where they can confirm requirements, review AI-generated outputs, document decisions and preserve auditability.
In the webinar, Walker stressed that risk-based testing is not about cutting corners, incomplete validation, or simply testing less, but rather about testing smarter. Alimentiv’s approach began with a formal risk assessment at the start of the build process, supported by its standards library. If an item has already been validated as part of the library, its risk profile may be lower. If it relates to safety or a study endpoint, that risk can be elevated. This creates a more nuanced approach than testing everything in the same way.
Standardized and previously validated components can be treated differently from new or modified elements, provided the documentation and traceability are strong enough to support that decision.
Automation can also help with repetitive, reproducible testing activities. Validated scripts can carry out checks such as range or window testing, while the clinical data management team reviews the results. The goal is not to remove accountability, it is to focus expert attention where it matters most.
A smarter path to AI-enabled study builds
A faster clinical trial build depends on clear processes, standardized components, practical governance and a realistic understanding of where automation can add value. Protocol interpretation, standards-based form selection, repeatable configuration tasks, medical coding and risk-based validation offer meaningful opportunities, but only when teams can see, review, and trust the outputs.
The organizations best-placed to benefit from AI will be those that first prepare their foundations. They will reduce legacy process debt, formalize standards, define quality gates and use technology to support expert decision-making rather than replace it. For an in-depth look at how AI can successfully accelerate your clinical study build workflows, watch the webinar recording here.
