The Takeaway From CDMI: Clinical Data Teams Are Getting More Practical About AI

While AI was once again a central topic at CDMI, what stood out to me this year was the specificity of the conversations. Across sessions, booth conversations, and discussions with sponsors, questions focused on how AI fits into day-to-day clinical data workflows: which tasks it can support, how its outputs are evaluated, and where human oversight is needed.

For me, those questions reflect a growing focus on what responsible, useful AI adoption looks like in practice.

Based on what our team heard at CDMI, three themes stood out.

1. AI Needs Clinical Context to Be Useful

One message came through repeatedly during the first day of sessions: applying AI to clinical data requires an understanding of the trial’s context.

Several speakers emphasized the importance of human oversight and that AI without the right study context can create risk. That same emphasis on clarity came through in conversations that my team and I had with sponsors. Attendees wanted clear answers about what AI can and cannot do, whether it makes decisions for users, and whether adoption of certain AI tools within a given system is mandatory.

2. Fragmented Data and Manual Review Are Still Prevalent Issues

Another familiar but important theme was the need to operate more efficiently in an increasingly complex data environment. A recurring message was that teams need to bring clinical data together and make it easier to review and analyze. AI also featured prominently in these discussions.

Several presentations explored how to aggregate data from multiple sources and use AI to support data management and analytics. These discussions reinforced the importance of connecting AI capabilities to the operational challenges clinical data teams are trying to solve.

3. AI Validation and Oversight Are Becoming Part of the Buying Conversation

Another important takeaway from conversations with sponsors was the emphasis on trust. Questions about what AI can do were accompanied by questions about how its outputs are evaluated and when teams can rely on them.

For technology providers like us, building trust means being transparent about capabilities and limitations, explaining how tools are developed and evaluated, and helping teams understand their appropriate use. Demonstrating functionality is only part of the equation; giving teams a clear basis for confidence in that functionality is just as important.

What Comes Next for AI in Clinical Data Management?

As AI capabilities continue to advance, the opportunity is to translate that innovation into meaningful improvements in clinical workflows

That means connecting AI to the right study context, keeping people involved where judgment matters, and being clear about validation and control. And it means proving that the technology can reduce the work that consumes clinical data teams’ time today.

For me, the takeaway was clear: clinical data teams are looking for AI that fits the way they work, helps them focus their expertise, and reduces time spent on manual tasks. Connecting those capabilities to the right data, study context, and oversight is an important part of turning AI’s potential into practical value.

For more information in how elluminate uses embedded intelligence to accelerate clinical development, click here.