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research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology. AI applications rely heavily on secure data, models, and infrastructure.
In todays rapidly evolving business landscape, the role of the enterprise architect has become more crucial than ever, beyond the usual bridge between business and IT. In a world where business, strategy and technology must be tightly interconnected, the enterprise architect must take on multiple personas to address a wide range of concerns.
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?
But in conflict with CEO fears, 90% of IT leaders are confident their IT infrastructure is best in class. Still, IT leaders have their own concerns: Only 39% feel their IT infrastructure is ready to manage future risks and disruptive forces. In tech, every tool, software, or system eventually becomes outdated,” he adds.
Download the report to find out: How enterprises in various industries are using MLOps capabilities. How to determine the benefits of an MLOps infrastructure. It is based on interviews with MLOps user companies and several MLOps experts. Which organizational challenges affect MLOps implementations.
Artificial intelligence is an early stage technology and the hype around it is palpable, but IT leaders need to take many challenges into consideration before making major commitments for their enterprises. With AI and data proliferating everywhere in the enterprise, AI and data are no longer centralized assets that IT directly controls.
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.
For CIOs leading enterprise transformations, portfolio health isnt just an operational indicator its a real-time pulse on time-to-market and resilience in a digital-first economy. In todays digital-first economy, enterprise architecture must also evolve from a control function to an enablement platform.
But what goes up must come down, and, according to Gartner, genAI has recently fallen into the “trough of disillusionment ,” meaning that enterprises are not seeing the value and ROI they expected. Enterprises are, in fact, already seeing significant value when properly applying AI. Of course, good use cases are just the beginning.
By Katerina Stroponiati The artificial intelligence landscape is shifting beneath our feet, and 2025 will bring fundamental changes to how enterprises deploy and optimize AI. Natural language interfaces are fundamentally restructuring how enterprises architect their AI systems, eliminating a translation layer.
Broadcom and Google Clouds continued commitment to solving our customers most pressing challenges stems from our joint goal to enable every organizations ability to digitally transform through data-powered innovation with the highly secure and cyber-resilient infrastructure, platform, industry solutions and expertise.
This allows companies to benefit from powerful models without having to worry about the underlying infrastructure. This required dedicated infrastructure and ideally a full MLOps pipeline (for model training, deployment and monitoring) to manage data collection, training and model updates.
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. Now, EDPs are transforming into what can be termed as modern data distilleries.
Security risks can hold modernization back When modernizing IT infrastructure and applications, unforeseen issues such as security breaches and vulnerabilities are a major concern for organizations. Enterprises can appease these concerns by working closely with a trusted partner throughout the modernization journey.
We provide enterprises with one platform they can rely on to holistically address their IT needs today and in the future and augment it with an extensive portfolio of managed services – all available through a single pane of glass. It’s another way that Orsini believes a VMware-based infrastructure supports success in the cloud.
Enterprises in Germany, Austria, and Switzerland are accelerating their transition to cloud-based ERP solutions, with SAP playing a key role in their digital transformation strategies. However, the increased participation of larger enterprises in this years survey may have also influenced the budget trends.
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You ’re building an enterprise data platform for the first time in Sevita’s history. But because of the infrastructure, employees spent hours on manual data analysis and spreadsheet jockeying. We knew we had to bring the data together in an enterprise data platform. What’s driving this investment?
Salesforce AI Research today unveiled new benchmarks, guardrails, and models aimed at enhancing the agentic AI in the enterprise. If a model stumbles in executing tasks in the enterprise, it can mean disrupted operations , eroded customer trust, and potentially financial or reputational damage. Integrated infrastructure.
On the other hand, there are also many cases of enterprises hanging onto obsolete systems that have long-since exceeded their original ROI. Start by evaluating your organization’s current infrastructure, applications, and processes to identify critical pain points, inefficiencies, and opportunities.”
Air Force technologist turned enterprise security visionary, Marc is leading a security transformation that is less about red tape and more about unleashing speed, agility, and resilience at scale. A former U.S.
Savvy IT leaders, Leaver said, will use that boost to shore up fundamentals by buttressing infrastructure, streamlining operations, and upskilling employees. “As Their top predictions include: Most enterprises fixated on AI ROI will scale back their efforts prematurely.
Threats to AI Systems It’s important for enterprises to have visibility into their full AI supply chain (encompassing the software, hardware and data that underpin AI models) as each of these components introduce potential risks. The post Securing AI Infrastructure for a More Resilient Future appeared first on Palo Alto Networks Blog.
AI spending on the rise Two-thirds (67%) of projected AI spending in 2025 will come from enterprises embedding AI capabilities into core business operations, IDC claims. Enterprises are also choosing cloud for AI to leverage the ecosystem of partnerships,” McCarthy notes. Only 13% plan to build a model from scratch.
With deep technical expertise, architects can navigate complex systems, platforms, and infrastructures. Unlike traditional executives, architects have a holistic view of both domains, allowing them to see the big picture and drive meaningful change. But their strategic thinking sets them apart—they don’t just focus on technology in isolation.
In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. It provides constructs to help developers build generative AI applications using pattern-based definitions for your infrastructure.
From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. Today, enterprises are leveraging various types of AI to achieve their goals. Learn more about how Cloudera can support your enterprise AI journey here.
The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.
AI-ready data is not something CIOs need to produce for just one application theyll need it for all applications that require enterprise-specific intelligence. Were seeing AI for data as one of the largest applications of AI in the enterprise at the moment, says Siz.
Over the past few years, enterprises have strived to move as much as possible as quickly as possible to the public cloud to minimize CapEx and save money. As VP of cloud capabilities at software company Endava, Radu Vunvulea consults with many CIOs in large enterprises. Are they truly enhancing productivity and reducing costs?
It’s a position many CIOs find themselves in, as Guan noted that, according to an Accenture survey, fewer than 10% of enterprises have gen AI models in production. “What’s Next for GenAI in Business” panel at last week’s Big.AI@MIT It’s time for them to actually relook at their existing enterprise architecture for data and AI,” Guan said.
They move fast in areas where the business ROI is clear, where theres mature data infrastructure, and where governance allows. Delivering agentic AI promises requires sovereign infrastructure that provides control of your data, logic, and business outcomes. But the more telling pattern, he says, is based on enterprise complexity.
SLMs catch the eye of the enterprise Nicholas Colisto, CIO at Avery Dennison, credits the rise of agentic AI as one reason fueling greater interest in SLMs among CIOs today. Were shifting from using them as general-purpose tools to using them as core infrastructure for more tailored, efficient AI systems that better serve the business.
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.
Monica Collings, chair and non-executive director at multiple energy and infrastructure organizations, highlighted the rising demand for electricity, the shift towards consumer-generated power and skyrocketing debt caused by the energy crisis. AI can help organizations adapt to these shifts.
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In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. An enterprise with a strong global footprint is better off pursuing a multi-cloud strategy.
These IT pros are tasked with overseeing the adoption of cloud-based AI solutions in an enterprise environment, further expanding the responsibility scope of the role. 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.
In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. 2] The myriad potential of GenAI enables enterprises to simplify coding and facilitate more intelligent and automated system operations. The foundation of the solution is also important.
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. Governments and enterprises will leverage AI for operational efficiency, economic diversification, and better public services.
Infinidat Recognizes GSI and Tech Alliance Partners for Extending the Value of Infinidats Enterprise Storage Solutions Adriana Andronescu Thu, 04/17/2025 - 08:14 Infinidat works together with an impressive array of GSI and Tech Alliance Partners the biggest names in the tech industry. Its rock-solid for high-end enterprise deployments.
This development is due to traditional IT infrastructures being increasingly unable to meet the ever-demanding requirements of AI. This is why Dell Technologies developed the Dell AI Factory with NVIDIA, the industry’s first end-to-end AI enterprise solution.
Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledge bases at scale.
With serverless components, there is no need to manage infrastructure, and the inbuilt tracing, logging, monitoring and debugging make it easy to run these workloads in production and maintain service levels. Legacy infrastructure. Our cloud strategy was to use a single cloud provider for our enterprise cloud platform AWS.
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