AI Articles and Analysis about Customer analytics

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​.

In a global economy where customer experience (CX) is the ultimate competitive battleground for so many sectors, businesses across industries face  daunting challenges in scaling CX solutions effectively. Despite massive investments in customer service technologies, many companies need help to deliver consistent, personalized support that meets user expectations, leading to frustration, lost revenue, and damaged reputations.

This article is sponsored by Arkestro 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.

This article is sponsored by Interactions 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.

Blue Cross Blue Shield (BCBS) is a federation of 35 independent health insurance companies that provide coverage to over 100 million Americans. The organization's national headquarters is located in Chicago, Illinois, while individual BCBS companies operate in various states across the country.

This interview analysis is sponsored by CLARA Analytics 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.

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.