Sunitha Rao

There’s a widening gap between AI ambition and AI architecture. Most organizations have plenty of the former, but not enough of the latter. About 78% of enterprises are already piloting AI tools. But the real question is: Who is actually able to scale those pilots into production applications that generate value? All the stalled pilots that are tossed aside share a common reason. Companies treated AI as a technology experiment rather than a business reinvention challenge.

In the race to develop and incorporate AI, enterprises are upending assumptions about the demands on modern infrastructure. With major advances from AI-assisted imaging and drug discovery in healthcare to AI-driven fraud detection and risk modeling in finance, AI workloads are pushing exponential growth in data volume, model complexity and real-time processing. This stretches storage architectures.

Timely Detection And Treatment Can Help Manage The Disease, So Don’t Ignore Common Symptoms When B Jitendra Bhat, a banker from Bengaluru, suddenly felt uneasy and started perspiring, he sensed something was amiss. He had just eaten lunch. He started vomiting blood and was unusually breathless. He was rushed to a private hospital within 15 minutes and his condition improved gradually.