How Clinical Trials Can Maximize Generative AI Investments
Generative AI (GenAI) continues to dominate headlines. But for most organizations, the return on investment remains elusive. According to MIT’s State of AI in Business 2025 report, enterprises have poured $30-40 billion into GenAI—yet 95% are seeing no measurable value.
The reason is clear: while generic tools like ChatGPT can improve individual productivity, enterprise-level deployments are failing to scale. Workflows remain siloed, systems don’t learn and adapt, and solutions aren’t aligned with daily operations. MIT notes that for healthcare and pharma, the challenge is even steeper, and that meaningful disruption and structural change are still limited.
But for clinical trial organizations, this gap isn’t a weakness—it’s a strategic opportunity. Those who invest now in embedding adaptive, workflow-driven AI can accelerate timelines, reduce risk, and deliver stronger outcomes, positioning themselves as leaders in a space where many are still experimenting. With intentional deployment, sponsors and CROs can not only realize near-term gains but also shape best practices and establish lasting competitive advantage.
How Clinical Trials Can Maximize AI Investments
1. Focus on Deep Workflow Integration and Customization, Not Generic Tools
MIT’s research shows that generic large language models are trusted for simple tasks but fall short for mission-critical work because they lack memory, context, and customization. In clinical trials, where precision and regulatory compliance matter, that gap is even more costly. Instead of layering generic GenAI on top of existing systems, teams should embed AI directly into high-value workflows.
For example, instead of teams spending weeks manually reviewing a protocol, breaking it into individual data points, and configuring forms in an electronic data capture (EDC) or electronic clinical outcome assessment (eCOA) system, AI can take the uploaded protocol and automatically convert its unstructured elements into structured specifications. From there, it can pre-build case report forms, map data fields, and even run automated testing to validate that the system is configured correctly. What once required intensive manual effort across multiple teams can now be accomplished in a fraction of the time. This type of deep integration not only accelerates study startup but also improves accuracy, reduces rework, and frees up experts to focus on higher-value activities—building trust that AI is delivering tangible, reliable results.
2. Prioritize Learning-capable and Adaptive Systems
MIT highlights the “learning gap”, meaning that most GenAI models don’t retain feedback or adapt over time. For clinical trials, this makes the difference between one-off assistance and lasting ROI.
By adopting agentic or adaptive AI solutions, clinical trial teams will achieve more value. For instance, in medical coding, AI algorithms flag omissions, propose solutions, and learn from specialists' decisions to improve efficiency and consistency while maintaining the organization's standards and building trust, and increasing productivity over time. In data reconciliation, AI models evaluate complex associations and learn from human-in-the-loop adjudication, improving future recommendations.
By choosing systems that learn, remember, and evolve, teams can make sure that value compounds with each use rather than stalling after initial deployment. These gains can be reinvested back into higher-value work, accelerating the next round of clinical development.
3. Target High-value Operational Processes for ROI
MIT also found a mismatch in AI spending: 50-70% of GenAI budgets go toward visible, top-line functions like sales and marketing, even though back-office automation often delivers higher ROI.
Clinical operations provide some of the clearest examples of impact:
- AI has been shown to reduce development timelines by an average of six months per asset1.
- AI-driven validation tools deliver a 15–20% reduction in trial timelines and 20% faster discrepancy detection2.
- As trial data volumes continue to grow, AI ensures cleaner, more reliable, and more actionable data—well beyond human capacity alone.
These are direct, measurable outcomes that demonstrate why operational processes should be priority targets for AI investment.
4. Leverage Specialized Solutions
MIT’s research shows that AI deployments succeed twice as often when organizations work with external partners (66% success compared to just 33% for in-house builds).
For clinical trial teams, this is especially important. Specialized vendors offer solutions built specifically for the complexities of clinical research—from site operations to contract management. Consider:
- AI-powered contract review tools cut negotiation times by up to 50%3, accelerating site activation.
- Streamlined payment processing improves site satisfaction, strengthening long-term partnerships.
By working with partners who understand trial workflows, organizations gain faster time-to-value and better alignment with operational needs.
The Path Forward
While many industries remain stuck on the wrong side of the GenAI Divide, the lack of adoption in life sciences creates the opportunity to chart a clearer path to success from the start and realize value more quickly.
Success depends on moving beyond generic AI adoption and strategically embedding intelligence where it adds clear, demonstrable value—integrating it into trial workflows, choosing systems that learn and adapt, targeting high-impact operational processes, and selecting solutions built for the complexities of clinical research.
With this deliberate approach, sponsors and CROs can capture lasting value and deliver real outcomes for patients and sites. The future of AI in trials isn’t about hype; it’s about thoughtful deployment that makes a measurable difference.
Learn more here.
References:
- McKinsey & Company. January 2025b. Unlocking peak operational performance in clinical development with artificial intelligence. Available at: https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinical-development-with-artificial-intelligence
- Vellanki, J. EXPLORE HOW AI-DRIVEN VALIDATION TOOLS CAN ENHANCE DATA INTEGRITY AND COMPLIANCE IN CLINICAL TRIAL MANAGEMENT. The International Research Journal of Modernization in Engineering Technology and Science. Volume 7, Issue 2, February 2025. DOI : https://www.doi.org/10.56726/IRJMETS68334. Used under Creative Commons Attribution (CC BY) license (https://www.irjmets.com/termsandcondition.php)
- Top Legal. 2025. Automated Contract Review: Faster, More Precise, More Secure.Available at: https://www.top.legal/en/knowledge/automatic-contract-review
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