AI とハイブリッド・クラウドの融合

The combination of hybrid cloud IT systems and 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 challenging, 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, principal 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 possibilities, 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 compute 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. In August, Nutanix released GPT-in-a-Box, a full-stack software-defined AI-ready platform with services aimed at simplifying or even jump-start 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.

“Leveraging AI to more efficiently and effectively help our customers is a top priority for us but, as a regulated financial services organisation, maintaining full control over our data is necessary," said Jon Cosson, CISO at JM Finn in a press release.

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 2023 Enterprise Cloud Index, the majority (60%) of IT teams surveyed already leverage more than one type of IT infrastructure, whether it is a mix of private and public clouds, multiple public clouds or an on-premises datacenter along with a hosted datacenter. That number is expected to grow to nearly three quarters (74%) in the near future. The inherent flexibility, scalability and efficiency, especially when powered by hyperconvergence, of hybrid multicloud systems make them suitable for meeting AI 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 governmance 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 data sets while looking at how Open AI built and now runs Chat GPT with it’s 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 build on AI. Especially those companies that release Environment, Social and Governance reports each year.

Accelerated Digital Transformation

As more AI-ready infrastructure options emerge, IT leaders will need to remain focused on integrity and efficiency of their systems, but they’ll als have to manage 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 growing enthusiasm around finding ways for AI and ML to increase productivity. This is inherent in digital transformation, where business 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 took 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.

New AI innovations is advancing how enterprises develop, deploy and modernize applications. 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 AI is used for cybersecurity. It looks for patterns and anomalies across digital transactions or interactions. Different industries use AI for data governance and for monitoring compliance across systems. It’s being used to write code.

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

This can speed application development time to market.

Competitive Advantage

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, these 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’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 capabilitieis 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 infrustructures 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. 

But AI and ML workloads consume IT resources, so IT teams need to be strategic about the infrustructure 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 more about Nutanix GPT-in-a-Box, a full-stack software-defined AI-ready platform designed to simplify and jump-start your initiatives from edge to core. More details in this blog post The AI-Ready Stack: Nutanix Simplifies Your AI Innovation Learning Curve and in the Nutanix Bible.

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

Michael Brenner contributed to this story. He is a keynote speaker, author and CEO of Marketing Insider Group. Michael has written hundreds of articles on sites such as Forbes, Entrepreneur Magazine, and The Guardian and he speaks at dozens of leadership conferences each year covering topics such as marketing, leadership, technology, and business strategy. 

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