AI Articles and Analysis in Pharma

This interview analysis is sponsored by MinIO 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.

Gilead Sciences, a biopharmaceutical company founded in 1987, has emerged as a leader in the development of innovative therapies for a range of diseases, including HIV, liver diseases, and oncology. With a commitment to advancing the field of medicine, Gilead has increasingly recognized the vital role that AI plays in its operations. Gilead Sciences achieved an annual revenue of approximately $28.3 billion in 2024, driven by its robust portfolio, and employs around 18,000 people globally, reflecting its substantial size and reach within the biotech industry​.

From intelligent sourcing and predictive analytics to automated contract analysis and risk mitigation, AI is enabling procurement teams to focus on strategic activities while delivering significant cost savings and improved supplier relationships.

In 2020, we wrote about the early but fertile stage of AI development at Johnson and Johnson. Since then, the COVID-19 pandemic and improvements to machine learning have forced important changes in how J&J is using artificial intelligence. 

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.

Lilly and Company is a global pharmaceutical corporation founded in 1876 and headquartered in Indianapolis, Indiana. The company has offices in 18 countries and sells its products in approximately 125 countries worldwide. As of 2024, Lilly employs over 38,000 people globally and has an annual revenue of $34.12 billion. 

Explainable AI models are essential in pharmaceutical R&D because they provide transparency and understanding of how AI-driven predictions are made. In drug discovery and development, stakeholders, including researchers, regulatory bodies, and healthcare professionals, need to trust and understand AI models' outputs to make informed decisions. Without explainability, AI models can be seen as "black boxes," leading to skepticism and reluctance to adopt these technologies in critical decision-making processes. 

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.