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Analyst reaction to Thursday’s release by the US Department of Homeland Security (DHS) of a framework designed to ensure safe and secure deployment of AI in critical infrastructure is decidedly mixed. Where did it come from?
Spending on compute and storage infrastructure for cloud deployments has surged to unprecedented heights, with 115.3% billion, highlighting the dominance of cloud infrastructure over non-cloud systems as enterprises accelerate their investments in AI and high-performance computing (HPC) projects, IDC said in a report.
But for many, simply providing the necessary infrastructure for these projects is the first challenge but it does not have to be. Another problem is that the adoption of automation in infrastructure is not at the level required. Already, leading organizations are seeing significant benefits from the use of AI.
Days after Oracle missed Q1 2023 revenue expectations and gave a downbeat rest-of-year outlook, sending its share price to suffer the worst one-day performance in 21 years, the cloud provider announced a team-up with Microsoft to co-locate a portion of its infrastructure in the Azure cloud.
By modernizing and shifting legacy workloads to the cloud, organizations are able to improve the performance and reliability of their applications while reducing infrastructure cost and management.
The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed. Hidden costs of public cloud For St. Judes Research Hospital St.
Drawing from current deployment patterns where companies like OpenAI are racing to build supersized data centers to meet the ever-increasing demand for compute power three critical infrastructure shifts are reshaping enterprise AI deployment. Here’s what technical leaders need to know, beyond the hype.
The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. As the next generation of AI training and fine-tuning workloads takes shape, limits to existing infrastructure will risk slowing innovation. What does the next generation of AI workloads need?
As organizations adopt a cloud-first infrastructure strategy, they must weigh a number of factors to determine whether or not a workload belongs in the cloud. By optimizing energy consumption, companies can significantly reduce the cost of their infrastructure. Sustainable infrastructure is no longer optional–it’s essential.
This is true whether it’s an outdated system that’s no longer vendor-supported or infrastructure that doesn’t align with a cloud-first strategy, says Carrie Rasmussen, CIO at human resources software and services firm Dayforce. These issues often reflect a deeper problem within the IT infrastructure and can serve as early warning signs.”
At Gitex Global 2024, Core42, a leading provider of sovereign cloud and AI infrastructure under the G42 umbrella, signed a landmark agreement with semiconductor giant AMD. By partnering with AMD, Core42 can further extend its AI capabilities, providing customers with more powerful, scalable, and secure infrastructure.
In today’s rapidly evolving technological landscape, the role of the CIO has transcended simply managing IT infrastructure to becoming a pivotal player in enabling business strategy. Guiding principles Recognizing the core principles that drive business decisions is crucial for taking action.
As organizations continue to implement cloud-based AI services, cloud architects will be tasked with ensuring the proper infrastructure is in place to accommodate growth. Organizations have accelerated cloud adoption now that AI tools are readily available, which has driven a demand for cloud architects to help manage cloud infrastructure.
Much of it centers on performing actions, like modifying cloud service configurations, deploying applications or merging log files, to name just a handful of examples. It provides an efficient, standardized way of building AI-powered agents that can perform actions in response to natural-language requests from users.
Orsini notes that it has never been more important for enterprises to modernize, protect, and manage their IT infrastructure. It’s also far easier to migrate VMware-based systems to our VMware-based cloud without expensive retooling while maintaining the same processes, provisioning, and performance.”
This development is due to traditional IT infrastructures being increasingly unable to meet the ever-demanding requirements of AI. This is done through its broad portfolio of AI-optimized infrastructure, products, and services. Behind the Dell AI Factory How does the Dell AI Factory support businesses’ growing AI ambitions?
infrastructure and AI-powered applications. Dr. Ömer Fatih Sayan, Türkiye’s Deputy Minister of Transport and Infrastructure, gave the event a powerful message on the nation’s commitment to innovation. 5G, together with AI, will fuel unparalleled growth, as AI depends on robust 5G infrastructure to realize its full potential,” he added.
Broadcom has once again been recognized with a prestigious 2025 Google Cloud Infrastructure Modernization Partner of the Year for virtualization. This robust platform enables organizations to effectively virtualize their entire infrastructure on Google Cloud.
At the same time, many organizations have been pushing to adopt cloud-based approaches to their IT infrastructure, opting to tap into the speed, flexibility, and analytical power that comes along with it. As new technologies and strategies emerge, modern mainframes need to be flexible and resilient enough to support those changes.
Some will grab the low-hanging fruit offered by SaaS vendors such as Salesforce and ServiceNow , while others will go deep into laying the enterprise infrastructure for a major corporate pivot to AI. Enterprises are also choosing cloud for AI to leverage the ecosystem of partnerships,” McCarthy notes.
Digital workspaces encompass a variety of devices and infrastructure, including virtual desktop infrastructure (VDI), data centers, edge technology, and workstations. Productivity – Deliver world class remoting performance and easily manage connections so people have access to their digital workspaces from virtually anywhere.
While centralizing data can improve performance and security, it can also lead to inefficiencies, increased costs and limitations on cloud mobility. Those who manage it strategically, however, can turn data gravity into a competitive advantage, using it to enhance performance, security and agility across a distributed cloud infrastructure.
Traditional systems often can’t support the demands of real-time processing and AI workloads,” notes Michael Morris, Vice President, Cloud, CloudOps, and Infrastructure, at SAS. Business objectives must be articulated and matched with appropriate tools, methodologies, and processes.
Inevitably, such a project will require the CIO to join the selling team for the project, because IT will be the ones performing the systems integration and technical work, and it’s IT that’s typically tasked with vetting and pricing out any new hardware, software, or cloud services that come through the door.
Simultaneously, the monolithic IT organization was deconstructed into subgroups providing PC, cloud, infrastructure, security, and data services to the larger enterprise with associated solution leaders closely aligned to core business functions. Traditional business metrics are proving the new IT reorg and brand is bearing fruit.
Server equipment, power infrastructure, networking gear, and software licenses need to be upgraded and replaced periodically. In addition, enterprise IT must build its infrastructure to manage a maximum load. This could take weeks or, more likely, several months to accomplish.
For example, AI can perform real-time data quality checks flagging inconsistencies or missing values, while intelligent query optimization can boost database performance. Its ability to apply masking dynamically at the source or during data retrieval ensures both high performance and minimal disruptions to operations.
AI models rely on vast datasets across various locations, demanding AI-ready infrastructure that’s easy to implement across core and edge. The challenge for CIOs is that without the right tools in place, this new hybrid cloud estate can blur the visibility business technology leaders need to measure performance and costs.
Enterprise infrastructures have expanded far beyond the traditional ones focused on company-owned and -operated data centers. An IT consultant might also perform repairs on IT systems and technological devices that companies need to conduct business. The IT function within organizations has become far more complex in recent years.
Research on creating a culture of high-performance teams suggests there’s a disconnect between how leaders perceive their cultures compared to how individual contributors view them. In the study by Dale Carnegie, 73% of leaders felt their culture was very good or better concerning others being accountable, compared to 48% of team members.
What is needed is a single view of all of my AI agents I am building that will give me an alert when performance is poor or there is a security concern. If agents are using AI and are adaptable, youre going to need some way to see if their performance is still at the confidence level you want it to be, says Gartners Coshow.
Microsofts Azure infrastructure and ecosystem of software tooling, including NVIDIA AI Enterprise, is tightly coupled with NVIDIA GPUs and networking to establish an AI-ready platform unmatched in performance, security, and resiliency.
Despite the spotlight on general-purpose LLMs that perform a broad array of functions such as OpenAI, Gemini, Claude, and Grok, a growing fleet of small, specialized models are emerging as cost-effective alternatives for task-specific applications, including Metas Llama 3.1, Microsofts Phi, and Googles Gemma SLMs.
Not only is it capable of operating across various environments such as cloud, data centres, workstations and edge locations, the Dell AI Factory with NVIDIA is designed to simplify AI adoption, such that AI initiatives can be performed at speed and scale.
It is intended to improve a models performance and efficiency and sometimes includes fine-tuning a model on a smaller, more specific dataset. These improvements in inference performance make the family of models capable of handling more complex reasoning tasks, Briski said, which in turn reduce operational costs for enterprises.
With a wide range of services, including virtual machines, Kubernetes clusters, and serverless computing, Azure requires advanced management strategies to ensure optimal performance, enhanced security, and cost efficiency. These components form how businesses can scale, optimize and secure their cloud infrastructure.
Digital experience interruptions can harm customer satisfaction and business performance across industries. NR AI responds by analyzing current performance data and comparing it to historical trends and best practices. This report provides clear, actionable recommendations and includes real-time application performance insights.
CIOs manage IT infrastructure and foster cross-functional collaboration, driving alignment between technological innovation and sustainability goals. ESG reporting accountability: Blockchain can help organizations maintain accurate, auditable records of their ESG performance.
Here are 13 of the most interesting ideas: “Current spending on generative AI (GenAI) has been predominantly from technology companies building the supply-side infrastructure for GenAI,” said John-David Lovelock, distinguished vice president analyst at Gartner. CIOs will begin to spend on GenAI, beyond proof-of-concept work, starting in 2025.
Region Evacuation with DNS Approach: Our third post discussed deploying web server infrastructure across multiple regions and reviewed the DNS regional evacuation approach using AWS Route 53. While the CDK stacks deploy infrastructure within the AWS Cloud, external components like the DNS provider (ClouDNS) require manual steps.
These changes can cause many more unexpected performance and availability issues. Everyone with a vested interest in their organizations growth and evolution can appreciate the value of a significant performance benefit and the transformative change of simplifying the complex.
CFO ) AI in Action: AI-powered vendor analysis assesses software options based on performance, cost-effectiveness, and compatibility, so you make data-driven sourcing decisions. See also: How to know a business process is ripe for agentic AI. )
Their DeepSeek-R1 models represent a family of large language models (LLMs) designed to handle a wide range of tasks, from code generation to general reasoning, while maintaining competitive performance and efficiency. You can access your imported custom models on-demand and without the need to manage underlying infrastructure.
While many have performed this move, they still need professionals to stay on top of cloud services and manage large datasets. VMware ESXi skills include virtual machine management, infrastructure design, troubleshooting, automation, cloud computing, and security.
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