Smart AI, Smarter Oversight: What Sponsors Really Need from Their CRO 

In clinical trials today, artificial intelligence (AI) is everywhere and nowhere all at once. Conferences are packed with AI panels. Vendors promise intelligent automation. Sponsors ask how AI will speed them to database lock or regulatory submission. But when it comes to real, practical implementation, many teams are still figuring out what AI can do, what it should do, and what actually counts as AI at all. 

At MMS, we believe that’s where a data-focused CRO makes all the difference. AI doesn’t replace expertise—it needs to be guided by it. Whether it’s video and audio analysis, document generation, data harmonization, or next-gen analytics, our Biometrics,  Medical Writing and Data Science teams help sponsors cut through the noise and securely use AI where it matters, as part of our end-to-end data-focused services and solutions. 

Here’s how we’re thinking about AI use cases right now, and how we’re helping sponsors turn interest into impact. 

Making Complex Data Easier to Interpret 

In one recent pilot, our team explored how to use AI to streamline the review of long-form video and audio data—media increasingly common in decentralized studies that utilize patient-reported outcomes (PRO). Rather than requiring investigators to manually review hours of footage, we’re developing AI-enhanced analytics based on transcripts, keywords, and sentiment extraction, that help reviewers quickly identify areas of interest. 

Think of it as a heatmap layered onto a video timeline. It’s not about drawing conclusions automatically. It’s about giving experts better tools to focus their time where it counts. Rather than just plugging AI into workflows, we are working with domain experts to apply the right model, visualize the output through our Datacise platform, and ensure results are interpretable and useful in the clinical context. 

Anonymizing and Harmonizing Large Datasets 

Another powerful application of AI lies in preparing large clinical datasets for reuse. Our data science & programming teams frequently curate data from multiple sources—sometimes contributed by different institutions—and publish them in a way that protects patient privacy site anonymity, client confidentiality while enabling teams to ask the right questions. Using AI-powered tools, we can automate the anonymization process, removing personally identifiable details and standardizing data structures across the dataset. This enables secure data sharing among researchers while maintaining compliance with privacy regulations. 

But here too, automation alone isn’t enough. We also need to understand the context how the data is to be used, select the right anonymization partner, and build robust governance around how data is handled at every stage. 

Clarifying the Line Between AI and Automation 

One theme that comes up repeatedly in our internal discussions—and one that’s top of mind for many sponsors is the blurred line between true AI and smart automation. In many cases, what’s marketed as AI is simply well-executed automation: tools that accelerate routine processes but don’t “learn” or make decisions the way AI models are intended to. 

That’s not a bad thing. 

In fact, automation is often the best first step toward improving efficiency in clinical trials. At MMS, we’ve developed automated mapping tools for SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) datasets that reduce manual programming effort by up to 80%. While we have explored ways to layer AI onto that process—for example, to suggest mappings or detect errors—our core value lies in ensuring quality and traceability first. 

As one of our team members put it, we prefer to think in terms of “automation intelligence” rather than artificial intelligence. For sponsors, the takeaway is this: what matters most isn’t whether a process is powered by AI, but whether it saves time, improves quality, and withstands regulatory scrutiny. 

Keeping a Human in the Loop 

In biometrics especially, as well as other high potential areas like medical writing, AI is only useful when paired with oversight. Sponsors rely on data integrity to make billion-dollar decisions. While AI can flag anomalies or assist with report validation, it can’t replace the judgment of experienced statisticians, programmers, medical writers, and other professionals 

That’s why at MMS, we maintain a “human in the loop” philosophy. We use AI to suggest improvements to statistical code, identify formatting errors in tables, or accelerate documentation—but every output is reviewed, verified, and contextualized by our teams.  This integrated approach allows efficient pinpointing of effort towards the most critical aspects, where experienced judgement and decision making are essential. 

It’s the right balance of innovation and responsibility, and it’s what sponsors expect from a CRO partner. 

Helping Sponsors Navigate the Hype 

With so much buzz around AI, it’s no surprise that sponsors are asking: “How are you using AI?” The better question might be: “How are you using AI to help me?” 

At MMS, we approach these conversations with honesty and insight. We explain where AI is currently valuable, for example speeding up medical writing, anonymizing data, or supporting video review. We also make sure to discuss where the AI is still evolving, such as CRF development or automated statistical plan generation. 

We also guide sponsors to successfully navigate the risks: data governance, model explainability, and the need for validated, reproducible results. Many AI models are “black boxes” that can’t provide transparent reasoning for their outputs—something regulators won’t accept. MMS helps sponsors strike the right balance between innovation and compliance. 

The Road Ahead: Natural Language Interfaces and Beyond 

As we look ahead, one of the most exciting areas we’re exploring is natural language interaction with clinical data. Using a combination of large language models, retrieval-augmented generation, and vector databases, we’re testing ways to allow users to ask plain-language questions about clinical trial data—and receive accurate, interpretable responses. 

Imagine querying a dataset with: “Which patients reported headaches and received medication within 24 hours?”—and getting a visual table in return. That’s where the next generation of clinical data exploration is headed, and MMS is already building toward it. 

Why the Right CRO Partner Matters 

Ultimately, AI is not a plug-and-play solution. It requires context, governance, and domain expertise. Sponsors don’t just need technology, they need a partner who can help them make smart decisions about how to use it. 

At MMS, our biometrics, medical writing and data science teams combine technical skill with deep clinical experience. We don’t just build models. We ask the right questions. We align with regulatory expectations. And we tailor solutions to each trial’s goals. 

AI may be the future, but in clinical trials, it takes human intelligence to get there responsibly.

To explore data focused in more detail, download our eBook Solving for Complexity: Practical Across the Clinical Data Lifecycle – Download now!