Generative Artificial Intelligence Strategy Approach

Artificial intelligence has been around since the 1950’s, yet even for skeptics, recent advances in Generative AI (GenAI) have significantly moved the needle forward. Due to massive early adoption, Goldman Sachs estimates that Generative AI could raise global GDP by 7% within 10 years [1]. However, while the focus has been on GenAI, it’s important to note that any GenAI strategy requires the right data and the ability to govern the data effectively with the GenAI tool. A strong GenAI strategy will include the following key components.

Trust Is Good; Control Is Better

Risk Mitigation Strategies for Artificial Intelligence Solutions in Healthcare Management
There are a growing number of examples of how Artificial Intelligence Solutions (AIS) can assist in improving healthcare management: early diagnosis, chronic disease management, hospital readmission reduction, efficient scheduling and billing procedures, and effective patient follow-ups while attempting to achieve healthcare's quintuple aim.

Edge Computing and IoT in a Connected AI Healthcare Ecosystem

AI in healthcare now involves the use of machine learning algorithms and patient data fed in timely ways to these algorithms. IoT is making its way into hospital devices and equipment and is sending this data to computing facilities for AI to work its valuable guidance on it. This consistent data flow streamlines the patient experience by producing inferences from multiple data points, which help to guide and improve healthcare management capabilities with information and data while handling patients, equipment, and procedures.

Infusion of Machine Learning Operations with Internet of Things

With advancements in deep tech, the operationalization of machine learning and deep learning models is burgeoning in the machine learning space. In a typical scenario within organizations involving machine learning or deep learning business cases, the data science and IT teams collaborate extensively in order to increase the pace of scaling and pushing multiple machine learning models to production through continuous training, validation, deployment and integration with governance. Machine Learning Operations (MLOps) has carved a new era of the DevOps paradigm in the machine learning/artificial intelligence realm by automating end-to-end workflows.

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