Due to their ability to enable parallelism and asynchronous execution, threads have an essential role in efficiently utilizing multi-core processors. Without them, handling concurrent tasks in modern applications like real-time inference in IoT or Asynchronous I/O in AI/ML would neither be feasible nor imaginable. The arrival of virtual threads has further grown such possibilities by eliminating the sole dependency on operating system threads.

It is estimated that between 80% and 90% of the world’s data is unstructured1, with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Despite containing a wealth of insights, this vast trove of information often remains untapped, as the process of extracting relevant data from these documents is challenging, tedious and time-consuming.

Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.

Gartner predicts that generative AI will be a workforce partner for 90% of companies globally by 2025. However, Alexander Bant, Gartner Finance’s chief of research, expressed a contrasting view at the Gartner CFO & Finance Executive Conference in Sydney in February 2024. He noted, “The large majority of CFOs continue to be displeased with the performance of digital investments across their organization.”

Making informed decisions on technology adoptions and ensuring the sustained growth and relevance of the organization in the market has never been more exciting than in the current times we live in. The Gartner hype cycle is a valuable tool in this pursuit with all its insights into the timing, adoption, and potential benefits of various technologies.