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Artificial intelligence is bringing new levels of automation to everything from cars and kiosks to utility grids and financial networks. But it’s easy to forget that before the enterprise can automate the world, it has to automate itself first.
As with most complicated systems, IT infrastructure management is ripe for intelligent automation. As data loads become larger and more complex and the infrastructure itself extends beyond the datacenter into the cloud and the edge, the speed at which new environments are provisioned, optimized, and decommissioned will soon exceed the capabilities of even an army of human operators. That means AI will be needed on the ground level to handle the demands of AI initiatives higher up the IT stack.
AI begins with infrastructure
In a classic Catch-22, however, most enterprises are running into trouble deploying AI on their infrastructure, in large part because they lack the tools to leverage the technology in a meaningful way. A recent survey by Run: AI shows that few AI models are getting into production – less than 10% at some organizations – with many data scientists still resorting to manual access to GPUs and other elements of data infrastructure to get projects to the finish line.
Another study by Global Surveys showed that just 17% of AI and IT practitioners report seeing high utilization of hardware resources, with 28% reporting that much of their infrastructure remains idle for large periods of time. And this is after their organizations have poured millions of dollars into new hardware, software, and cloud resources, in large part to leverage AI.
If the enterprise is to successfully carry out the transformation from traditional modes of operation to fully digitized ones, AI will have to play a prominent role. IT consultancy Aarav Solutions points out that AI is invaluable when it comes to automating infrastructure support, security, resource provisioning, and a host of other activities. Its secret sauce is the capability to analyze massive data sets at high speed and with far greater accuracy than manual processes, giving decision-makers granular insight into the otherwise hidden forces affecting their operations.
A deeper look into all the interrelated functions that go into infrastructure management on a daily basis, sparks wonder at how the enterprise has gotten this far without AI. XenonStack COO and CDS Jagreet Kaur Gill, recently highlighted the myriad functions that can be kicked into hyperspeed with AI, everything from capacity planning and resource utilization to anomaly detection and real-time root cause analysis. With the ability to track and manage literally millions of events at a time, AI will provide the foundation that allows the enterprise to maintain the scale, reliability, and dynamism of the digital economy.
Intelligence to the edge
With this kind of management stack in place, says Sandeep Singh, vice president of storage marketing at HPE, it’s not too early to start talking about AIOps-driven frameworks and fully autonomous IT operations, particularly in greenfield deployments between the edge and the cloud. The edge, after all, is where much of the storage and processing of IoT data will take place. But it is also characterized by a highly dispersed physical footprint, with small, interconnected nodes pushed as close to user devices as possible. But its very nature, then, the edge must be autonomous. Using AIOps, organizations will be able to build self-sufficient, real-time analytics and decision-making capabilities, while at the same time ensuring maximum uptime and failover should anything happen to disrupt operations at a given endpoint.
Looking forward, it’s clear that AI-empowered infrastructure will be more than just a competitive advantage, but an operational necessity. With the amount of data generated by an increasingly connected world, plus the quickly changing nature of all the digital processes and services this entails, there is simply no other way to manage these environments without AI.
Intelligence will be the driving force in enterprise operations as the decade unfolds, but just like any other technology initiative, it must be implemented from the ground up – and that process starts with infrastructure.
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