The Amalgamation of AI and Hybrid Cloud

The combination of hybrid cloud IT systems, cloud native application development, artificial intelligence (AI) and machine learning (ML) capabilities is grabbing mindshare across the IT industry. On their own, each presents opportunities for businesses to innovate and grow. But melding these new capabilities can be immensely challenging and fraught with complexity, risk and uncertainty. 

“The thing I’m asked most about these days is the impact of generative AI on IT operations,” Steve McDowell, chief analyst at NAND Research, told The Forecast. “We’re still in the very early stages of how we’re going to use it.”

The widespread curiosity around AI and ML is breathing life into new use cases and endless possibilities from health care and agriculture to manufacturing and cybersecurity, yet the need to govern data, applications and the IT systems that power everything has many digging in their heels as they look around for best practices.

“There's a lot of hype around AI, and that impacts IT in a couple of ways,” McDowell said. 

“They need to figure out how to build the infrastructure to support it, because traditional computing does not account for it. We're seeing hundreds, even thousands of experiments across organizations. IT has to step up and support those, often in short order, so it's a challenge operationally.”

Many technology companies are jumping into the fray to help IT leaders create a strategy and pick the right pieces needed to leverage AI. Hybrid clouds are one essential piece of that equation. For example, Nutanix Enterprise AI software, including GPT-in-a-Box, offers a full-stack software-defined AI-ready platform with services aimed at simplifying or even jump-starting AI initiatives. IT teams can use it to control their critical data, intellectual property and build out infrastructure that meets their security, privacy and compliance requirements.

McDowell said IT practitioners need to focus on the value of AI will bring to their enterprise. 

“I don't care about hardware,” he told The Forecast. “That's why I like GPT-in-a-Box, because that's a software set of capabilities. I can deploy that at the edge or I can roll that out in the cloud, if that makes sense.” 

The recent, rapid rise of enterprise AI is causing a lot of disruption across the IT industry. “It's going to change the way that we think about infrastructure.”

Is IT Infrastructure Ready for AI?

As more businesses adopt new AI and ML technologies, increasingly these capabilities will run on hybrid cloud and hybrid multicloud IT systems.

“The wave of AI from an infrastructure perspective is gonna be tremendous,” Harmail Chatha, senior director of cloud operations at Nutanix, told The Forecast.

According to the 2024 Enterprise Cloud Index, 90% of IT teams surveyed are being “cloud smart” by leveraging the best environments for each of their applications, illustrating that hybrid multi cloud is an industry standard. In addition, the number of companies using hybrid multi cloud is expected to more than double over the next one to three years. The inherent flexibility, scalability and efficiency, especially when powered by hyperconvergence, of hybrid multi cloud systems make them suitable for meeting AI’s robust demands.

Pulling together the right AI-ready infrastructure can accelerate AI/ML initiatives, but it must be done with an eye on sustainability, cost management, security and other IT governance compliance. McDowell said IT teams continue to fixate on system efficiency, especially when new AI and ML applications are being considered.

He said more IT leaders are asking: How many GPUs (graphics processing units) can I put in a rack, how do I power and cool them? Those are the chips that many rely on to run AI and ML code.

“GPUs are very power hungry and heat generating more so than CPUs by far,” he said. 

Currently, Chatha said his IT team is at a chasm, doing some AI/ML for small level datasets while looking at how Open AI built and runs Chat GPT with its large language model-powered capabilities.

“That infrastructure is heavy,” he said. “They have to deploy so much gear so the systems can learn what to do and how to do it.” 

He said by taking the entire internet and dumping it into ChatGPT required thousands of data centers. As an industry standard, data centers already consume about 1.5% of global power, so Chatha imagines that energy consumption will be a major concern for companies built on AI. Especially those companies that release Environment, Social and Governance reports each year.

AI’s Impact on Data Center Operations

As many companies modernize their IT operations and leverage a variety of public and private clouds, they’re operating with a cloud native mindset, using cloud computing capabilities such as Kubernetes to build, run and move applications across different IT systems. This impacts the mindset behind many new AI and ML developments, which is to say these new applications must be able to run on a variety of infrastructures and maintain integrity in order to evolve with changing business needs or regulations. 

Underneath it all is the world of IT infrastructure, which is evolving in many directions. While many new companies are “born in the cloud” and others “move to the cloud,” many IT leaders are forging their future that connects and offers the best of owned and rented infrastructure. Growing complexity requires software simplification and increasingly that’s powered by or coming in the form of AI or ML. 

Cloud native technologies are increasingly playing a big role in the development and management of enterprise applications, including new AI capabilities, according to Dan Ciruli, senior director of product management for cloud native software at Nutanix.

“All the big companies are running all of their AI stuff on Kubernetes,” he said. That’s because learning algorithms help them untangle the complexities of containerized development.

Companies that blend on-prem and cloud native development will still need some of their data behind the enterprise firewall, Ciruli said. And they won’t want their data finding its way into large learning models (LLMs) driving apps like ChatGPT. AI applications will help IT teams figure all this out.

Ciruli said that even as AI and cloud native dovetail on the backend, front-end users typically don’t know or care what infrastructure is delivering those digital experiences. 

Accelerated Digital Transformation with Hybrid Cloud

As more AI-ready infrastructure options emerge, hybrid cloud is taking center stage. But IT leaders will need to remain focused on the integrity and efficiency of their systems, and they’ll have to do so while managing expectations at the top, McDowell said.

“The IT practitioner already has his expectations set,” he said. “You have to manage up the chain and set expectations that this is not going to solve all the world's problems.”

Still, there is growing enthusiasm around finding ways for AI and ML to increase productivity. This is inherent in digital transformation, where businesses digitize processes and aspects of their operations in order to be more agile, efficient and higher performing. Digitizing processes can open the way for experimenting with automation AI and ML. 

One example is how a small IT team inside Nutanix documented data center infrastructure then used an open source tool called NetBox to automate IT inventory. A few months later, the team expanded that into a tool that helps manage resources across hybrid multicloud operations.

Ultimately, AI can potentially operate all aspects of a datacenter from managing resources and streamlining efficiency to enhancing security.

New AI innovations are advancing how enterprises develop, deploy and modernize applications. The technology can do everything from generating the software’s code to assisting with debugging and even doing predictive maintenance on the finished product. The promise for developers is getting to the market a lot quicker, while reducing their overall spend.

New AI tools can enable the automated translation of development code and even remove pain points for companies undergoing cloud migration toward a hybrid cloud model. 

McDowell said that one exciting use case for AI is cybersecurity. AI analyzes patterns in massive heaps of data and looks for anomalies across digital transactions or interactions that could potentially raise red-flags. The end result is a more secure, responsive and adaptable cyber network with greater efficiency and minimized costs. Different industries are also using AI for data governance and for monitoring compliance across their systems. It’s even being used to write code.

“Generative AI's pretty good at writing it, if you ask it correctly,” McDowell said.

The Competitive Advantage of Hybrid Cloud

Venerated business technology industry pioneer IBM considers hybrid cloud and AI to be the most transformative technologies in the present-day IT space. These tools can help improve products, customer service and business operations. All combined, hybrid cloud AI can grow a competitive advantage.

Challenges such as inflation, sustainability requirements and cyberthreats are obstacles, but companies that use human and digital tools can quickly adapt to changing circumstances. Furthermore, some experts believe AI will add trillions of dollars to the global economy by the next decade, including Martin Ford, author of Rule of the Robots: How AI Will Transform Everything.

“Companies that fail to adopt this technology and leverage it to the fullest extent possible are certain to fall behind and eventually become irrelevant,” Ford said in a Forecast article by business futurist Scott Steinberg titled Businesses Bet Big on AI Benefits.

“By contrast, companies who act immediately and begin to develop an AI strategy [have the chance to create] competitive advantage. Recent analyses from McKinsey and PwC estimate that AI will add as much as $13 trillion to the global economy by 2030. This likely explains why there are massive investments pouring into AI… both internally among corporations and in the VC space.”

AI and ML workloads consume vast computing resources, so IT teams need to be strategic about the infrastructure where those applications run and data reside.

“It’s going to require a lot more power, more real estate,” said Chatha.

But these capabilities will also benefit data centers.

“Through AI, we'll be able to self-heal a lot of the problems at the software tier,” he said, rather than having to monitor or fix things when they break. 

His team is planning to deploy robots in their data centers to troubleshoot problems when they arise. 

“That will be based on a lot of AI telling the robot what to do and how to fix it.” 

He said AI and ML will be used more and more to manage IT operations. 

“It’s gonna grow and continue to grow. It will have a huge impact on data, data centers and sustainability.”

Editor’s note: Learn how Nutanix software, including Nutanix GPT-in-a-Box, can jumpstart AI transformation with optimal infrastructure that delivers control, privacy and security to maximize AI success.

This is an updated article originally published August 28, 2023. 

Ken Kaplan is Editor in Chief for The Forecast by Nutanix. Find him on LinkedIn and X @kenekaplan.

Michael Brenner and Chase Guttman contributed to this story. 

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