AI & Interoperability

in Clinical Trials:

AI was everywhere at SCDM 2026.
But underneath the conversations about agents, automation, large language models, and digital workers was a more important question:
What does it actually take to make AI work in clinical data management?
Across the conference and during our panel, Why AI Pilots Don’t Make It to Production — and What Actually Works in Clinical Data Management, one theme became increasingly clear:
The biggest barriers to AI adoption are not necessarily the models themselves.
They are the data, architecture, workflows, trust, and human factors surrounding them.
Here are some of the key takeaways we brought back from Raleigh.

1. Before asking what AI can do, ask whether your data is AI-ready

Healthcare AI is advancing rapidly. The FDA now reports more than 1,600 AI-enabled medical devices authorized for marketing in the United States.
Yet clinical research presents a particularly difficult data environment.
Study data may need to move between EDCs, EHRs, laboratories, imaging platforms, eCOA, wearables, safety systems, CTMS platforms, and other specialized technologies. Increasingly, organizations also want AI to work with information that does not arrive in a clean, standardized structure.
That creates a fundamental problem:
AI cannot overcome a fragmented data foundation simply because the model itself is sophisticated.
Before AI can reliably analyze clinical data, organizations need a way to collect information across sources, transform and harmonize it, preserve context and provenance, and make it available in a consistent form.
This echoed a question we have been asking at Adaptive for some time:
Is your data AI-ready?
AI readiness is not just about having enough data.
It means having data that is connected, harmonized, traceable, accessible, and trustworthy enough to support the intended use case.
Without that foundation, organizations risk introducing AI on top of the same fragmentation they were trying to solve.

    2. AI can be brilliant and brittle at the same time

    One of the most memorable ideas discussed at SCDM was the “jagged frontier” of AI.
    Modern AI can perform extraordinarily well on certain tasks and unexpectedly fail on others that appear straightforward.
    That distinction becomes especially important in regulated environments.
    A system that produces a convincing answer is not necessarily producing a correct answer. And when an AI system encounters incomplete, misleading, or unexpected information, its response may still sound authoritative.
    For clinical data management, that changes the question from:
    “Can AI perform this task?”
    to:
    “Can we trust, verify, trace, and govern how AI performs this task?”
    The healthcare industry is increasingly developing frameworks around exactly this challenge. Responsible AI initiatives are emphasizing areas such as lifecycle governance, risk assessment, responsible data management, organizational accountability, education, and continuous evaluation.
    For clinical research organizations, trustworthy AI therefore cannot be separated from the infrastructure surrounding it.
    The model matters. But so do the controls that determine what information it receives, how its output is evaluated, and when a person needs to intervene.

    3. “Human in the loop” may be becoming “human in the lead”

    As AI capabilities increase, the conversation around human oversight is evolving.
    “Human in the loop” has traditionally described workflows where AI performs an action and a person reviews or approves the result.
    At SCDM, another framing repeatedly surfaced:
    Human in the lead.
    The distinction matters.
    Clinical data management involves judgment, interpretation, review, and accountability. Those responsibilities cannot simply disappear because an algorithm can complete part of the workflow.
    Instead, AI can take on appropriate repeatable work while experienced professionals remain responsible for directing the process, evaluating exceptions, and standing behind the outcome.
    This is particularly relevant as organizations explore Digital Workers and more autonomous AI agents.
    The opportunity is not necessarily to remove clinical data managers from the workflow. It is to reconsider which parts of the workflow genuinely require their expertise.
    If AI can handle repetitive review, information gathering, comparison, or preparation tasks, CDMs can spend more time on the decisions where human judgment creates the greatest value.

    4. A successful proof of concept is not the same as successful AI adoption

    Adaptive Clinical Systems chaired a panel at SCDM focused specifically on this gap:
    Why AI Pilots Don’t Make It to Production — and What Actually Works in Clinical Data Management.
    The discussion brought together perspectives from Adaptive Clinical Systems, Merck, Syneos Health, and Axcellion.ai around what happens after an AI proof of concept demonstrates technical potential.
    This is where many initiatives become stuck.
    A sophisticated agent may work. A demo may impress stakeholders. A proof of concept may produce the expected result.
    But none of those automatically mean the organization is ready to deploy that technology across real workflows.
    The panel highlighted four distinct considerations:
    Good science.
    AI initiatives need a clearly defined problem, appropriate methodology, and evidence that the approach actually works.
    Robust architecture.
    An AI solution needs reliable access to the right data and systems, supported by infrastructure designed for production rather than a standalone demonstration.
    Balanced ROI.
    Organizations need to determine whether the value created justifies the implementation, validation, governance, and operational effort required.
    Organizational readiness.
    The people expected to use, supervise, and trust the technology need to understand how it changes their work and responsibilities.
    During the panel discussion, that final consideration emerged as especially important.

    5. Interoperability Is By Design — Not an Add-On

    AI initiatives often fail due to misaligned or siloed infrastructure. Without seamless data interchange across platforms, AI can’t access the right data, and teams waste time formatting, cleaning, or reconciling inputs manually.
    True interoperability means data flows in real time, remains compliant, and supports every phase of the trial.
    “Selecting best-in-breed tools and point solutions are recommended best practices, but only when they are “plugged in” with an interoperability backbone.”
    Adaptive’s platform is purpose-built to ensure:
    • No-code data connections between core systems
    • A user-friendly mapping wizard with a robust rules engine for clinical data transformation
    • Bidirectional, validated data pipelines that are monitored 24/7
    • Complete audit trails and automated logging

    6. Organizational Readiness Is the Real Bottleneck

    Even with the right tools, AI adoption fails without the proper buy-in from all parties within an organization.
    What gets in the way? Lack of clarity, insufficient training, and resistance to change.
    “AI isn’t just a technology project — it’s a change management initiative.”
    How to get ahead:
    Train clinical and data teams early Set realistic expectations on timelines and capabilities Invest in a platform that aligns with your team’s workflow, not just your vendor’s pitch deck

    Final Thoughts

    AI in clinical trials is here now — it’s not a future promise. But it’s not enough to buy a model or bolt on a dashboard.
    If you want AI to work, you need clean, interoperable, and validated data — from source to submission.
    Whether you’re running complex global trials or preparing to scale your data infrastructure, Adaptive Clinical helps bridge the gap between today’s data silos and tomorrow’s AI-powered clinical operations.

    Want the full webinar replay?

    Access it here or connect with our team to see how Adaptive Clinical
    can support your AI and interoperability roadmap.
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