AI Articles and Analysis about Predictive analytics and forecasting

AI and machine learning have promising potential in the field of healthcare. However, there is one significant hurdle healthcare companies often encounter when trying to implement AI and machine learning. The data architecture of current legacy systems in healthcare settings prevents implementing many use cases and limits a company’s ability to benefit from these technologies fully.

Due to technological advancements, automakers are uniquely positioned to benefit from an industry-wide transformation. Technology now allows a direct connection between car manufacturers and the customers who purchase those cars. Such a relationship has never before been possible in the history of the automotive industry.

The introduction of GenAI tools in the enterprise signifies a significant shift in how businesses operate in the current technological landscape. These tools enhance various aspects of daily operations, from data analysis to decision-making processes. 

Edge AI, also known as edge intelligence, is a term that refers to the convergence of two dynamic technological trends: edge computing and Artificial Intelligence. In today's tech landscape, the proliferation of mobile devices and advancements in communication technologies have fueled the rise of edge computing, which focuses on processing data at the network edge. 

Founded in 1833 and headquartered in Irving, Texas, since 2019, McKesson is a leading distributor of pharmaceuticals and medical supplies. They also provide health information technology and care management tools.

Traditional business analytics has undergone significant changes thanks to the internet revolution, cloud computing and the shift to self-service analytics, contributing to the recent rise of embedded analytics in business operations. Embedded analytics involves embedding and integrating visualizations, dashboards, reports or predictive analytics in everyday business applications. 

AI is driving change across many industries, including logistics and manufacturing. Companies need to strike the right balance between automation and human intervention. This interaction represents a specific area of study known as human-automation interaction. 

AI has profound implications for drug discovery, ushering in a new era of innovation and efficiency. Recent milestones - such as the first AI-designed drug molecule to enter human clinical trials and the prediction of protein structures for millions of proteins - showcase the transformative power of AI in this domain.