“Big corporations can’t rely on their internal speed to match the transformation that is happening in the world. As soon as I know a competitor has decided to build something itself, I know it has lost.” 

These candid sentences from Sanofi CEO, showcase one of the most common questions that’s at the forefront of every pharmaceutical company’s mind; whether to build or buy your way into the agentic and generative AI revolutions. 

In life sciences, many teams start with the same instinct. They see a capable large language model, stand up a proof of concept, and feel close to a breakthrough. For most of us, AI prototypes can look magical. A chatbot summarizes visit reports, drafts emails, or answers protocol questions in minutes. The experience is so strong that teams assume production is a short step away.

Unfortunately, the gap is much bigger than it looks. 

According to a recent MIT study, 95% of AI pilots will fail, as they note that “Only 5% of custom GenAI tools survive the pilot-to-production cliff, while generic chatbots hit 83% adoption for trivial tasks but stall the moment workflows demand context and customization.”

Like MIT’s example shows, moving from prototype to production in clinical research means building something validated, compliant, scalable, and integrated into real workflows. That takes far more than clever prompts. It requires domain grounding, continuous monitoring, retraining loops, robust tool orchestration, and evidence that the system is safe and auditable under regulations like GxP, HIPAA, and 21 CFR Part 11.

Many organizations only discover the hidden costs after they have committed. Internal teams often invest for two years, spend millions in sunk cost, and still never reach a dependable clinical grade system. The illusion comes from how easy it is to get an early demo working, and how hard it is to make that demo survive contact with trial reality. 

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Artificial intelligence continues to influence nearly every industry, and life sciences are no exception. In a recent conversation on the AI and Business podcast, Damien Nero, Head of Data Science in US Medical at Takeda Pharmaceuticals, shared his perspective on how AI is changing the clinical trial landscape. With over 15 years of experience applying machine learning and real-world data to drug development, Nero outlined both the progress already being made and the challenges that still stand in the way of broader transformation. His insights highlight how pharmaceutical leaders can think strategically about deploying AI to balance innovation with operational efficiency.

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It has become clear that our industry has reachedthe limits of human-only clinical development. As clinical trials have become increasingly complex, the endeavors that people alone can perform are no longer sufficient to generate the momentum needed to address the growing burden of human disease. This has led to longer drug development timelines and significant delays for patients. One large are of lost time is “white space,” definied simply as unproductive time caused by manual, sequential processes and fragmented data systems. Thankfully, a solution lies in agentic AI and its abilities to perform series of tasks.