The rate at which AI is transforming business in every industry is accelerating. It’s being used to enhance customer experiences, make data-driven strategic decisions and automating operations…and much more. CIOs and IT teams that can’t manage and scale computing resources across different infrastructures could get tripped up or hit potholes forging their path into the AI era.
According to a Forbes Advisor survey, 56% of organizations are using AI to improve business operations, 51% to bolster cybersecurity and fraud management and 46% for customer relationship management. These percentages are likely to grow quickly since AI is the top investment priority, according to finding the 2024 Nutanix Enterprise Cloud Index (ECI), a worldwide survey of 1,500 IT and business decision-makers.
“Once you start talking about what the business use is, and the value you're trying to extract from any technology, that then is going to drive the technology decisions,” Steve McDowell, chief analyst at NAND Research told The Forecast.
He said a good IT strategy pulls things forward and enables the enterprise to stay innovative.
“How am I going to use AI to enable the next generation or the next iteration of digital transformation?” asked McDowell. “But it's also, how do I use it to make my own IT operations more efficient?”
But the rapid rise of GenAI and new AI infrastructure innovation is pushing IT leaders into the unknown. It’s forcing business decision makers to rethink strategies and underlying IT system architectures to keep their data, applications and models secure, according to Thomas Cornely, SVP of Product Management, Nutanix.
“In this uncertain landscape, enterprises are encountering common challenges on their AI journey: the cloud vs. on-premises, open models and how to keep their data, applications and models secure,” Cornely wrote in a Forbes Council in an article titled Bringing AI to the Enterprise: Challenges and Considerations.
“For the past 40 years, enterprise infrastructure for business applications has been built to deliver scale, performance, resilience and security for the databases at the heart of critical business applications. The next decade will be about new infrastructure solutions that can provide scale, performance, resilience and security for the models and the inference endpoints at the heart of enterprise AI applications.”
Software-Define IT Strategies to Run AI Across Private and Public Clouds
Many CIOs and IT decision makers already enabled AI adoption by modernizing their operations, ensuring they can scale easily and securely as data and application demands grow. While public cloud services are poised to play an important role in the rollout of enterprise AI, IT leaders will also use their own data centers powered by virtualization, hyperconverged infrastructure and cloud native capabilities that allow IT teams to distribute and manage applications and data across different IT infrastructures. This allows enterprises to build, test, deploy and manage new AI capabilities more quickly than relying on a variety of incompatible or traditional IT systems.
These modernized IT environments allow for dynamic resource management and operational agility needed to continuously evolve over time. That includes leveraging advancements in GPUs and CPU accelerators to meet AI applications and data needs.
In recent years, IT teams can many new things due to new capabilities. For example, they can replicate their virtualized private IT environments and run in public cloud services, using technologies such as Nutanix Cloud Clusters (NC2) software. This can simplify system updates, recourse management and other IT team responsibilities.
“The right solution can make your AI projects on-premises easy to deploy, simple to use and safe because you control everything, from the firewall to the people that you hired,” Conely stated. “Furthermore, you can size what you need for the value that you're going to get instead of using the cloud, with its complex pricing and hard-to-predict costs.”
Managing AI at the Edge
A virtualized IT environment can extend to the edge, often where data is generated. This includes manufacturing plants, retail stores, warehouses and other location-based operations. For example, an HCI-powered IT system can allow for AI models to be deployed and run closer to where data is generated and powering real-time AI applications, like autonomous vehicles or industrial automation machines. The edge is another focal point for IT investment, as IDC expects that edge computing spending could reach $350 billion by 2027, fueled largely by AI deployments.
A conventional enterprise edge network is centralized, while an AI edge network is distributed, explained McDowell in his report Taming the AI-enabled Edge with HCI-based Cloud Architectures. Enterprise networks are largely homogenous, while AI edge networks have device and application diversity.
He said hardware virtualization via HCI can make AI-enabled edge networks much more manageable. With hypervisor software orchestrating virtual networks, servers and storage from a central console, IT teams can substantially reduce the complexity of their environments.
“An HCI-based platform's centralized management tools allow for easier monitoring and management of resources across multiple edge locations while allowing for remote updates, increased security, and comprehensive data and network protection,” McDowell stated.
Leveraging API for IT Operations Innovation
IT teams that rely on and manage modernized IT systems leverage application programming interfaces (APIs), allowing the exchange of data between applications and systems. This opens all kinds of opportunities for using new tools and adding new services.
Specialized tools can help solve complex problems in much the same way that human specialists work together on larger projects, Induprakas Keri, senior vice president and general manager for hybrid multicloud at Nutanix, told The Forecast.
“If you have an underlying infrastructure that is API-driven, then your AI journey will be much more automated and much more painless,” Keri said.
Ken Kaplan is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
Michael Brenner contributed to this story.
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