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As life sciences organizations increasingly adopt AI to enhance productivity, streamline workflows, and improve quality, aligning AI initiatives with business objectives and evolving traditional return on investment (ROI) metrics have become essential strategies. These practices not only secure executive buy-in but also ensure sustained success in AI adoption.

Fostering a culture of experimentation in AI and data science is crucial for organizations to stay competitive and drive innovation. In 2021, an MIT Sloan report found companies that prioritize AI experimentation are 2.7 times more likely to capture new value and improve their operations. By embracing a more experimental mindset, organizations can harness the full potential of AI-driven tools and unlock new opportunities for growth.

This article/interview analysis is sponsored by BenevolentAI and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

According to research from the American Society for Biochemistry and Molecular Biology, nine out of 10 drugs fail to make it to market in the clinical trials process. Researchers in the cited studies found that 40-50% of these failures are due to a lack of clinical efficacy, meaning that the drug is not able to produce its intended effect in people, and 30% were due to unmanageable toxicity or side effects.

Far beyond surface-level 'chatbot' software, and other customer-facing support systems, the same generative AI (GenAI) capabilities having direct impacts across language workflows in front-office tasks in financial and legal services is also having a direct impact on how research teams in the life sciences space are targeting solutions for rare diseases and novel treatments.