This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Red Hat announced updates to Red Hat OpenShift AI and Red Hat Enterprise Linux AI (RHEL AI), with a goal of addressing the high costs and technical complexity of scaling AI beyond pilot projects into full deployment. IDC predicts that enterprises will spend $227 billion on AI this year, embedding AI capabilities into core business operations.
Our AI infrastructure orders with webscalers in Q2 surpassed $350 million, bringing our year-to-date total to approximately $700 million, and we are on track to exceed $1 billion of AI infrastructure orders in fiscal year 25, Cisco CEO Chuck Robbins said during the vendors financial call. Second, AI inference and enterprise clouds.
Always on the cusp of technology innovation, the financial services industry (FSI) is once again poised for wholesale transformation, this time with Generative AI. Financial institutions have an unprecedented opportunity to leverage AI/GenAI to expand services, drive massive productivity gains, mitigate risks, and reduce costs.
Pressure to implement AI plans is on the rise, but the readiness of enterprise networks to handle AI workloads has actually declined over the past year , according to new research from Cisco. However, between 2023 and 2024, global AI readiness in the enterprise has declined.
Hewlett Packard Enterprise (HPE) has signed a contract exceeding $1 billion to provide AI servers for X, the platform formerly known as Twitter, according to Bloomberg. The financial scope of the deal underscores its significance. The agreement could also set a precedent for other enterprises, particularly within Musks ecosystem.
Enterprise infrastructure that supports data center, cloud and edge networks could someday be dominated by one of its tiniest components--the smartNIC or data processing unit (DPU). Looking ahead, users and vendors see a way to reduce enterprise costs, improve performance and increase security with smartNICs.
With the incremental differences in the major enterprise cloud environments today, that may be enough. For sure, what AWS is announcing simplifies the life of enterprise IT. The key announcements included: Amazon FSx Intelligent-Tiering This is an AWS attempt to try and whittle down cloud costs at the enterprise level.
This is particularly true with enterprise deployments as the capabilities of existing models, coupled with the complexities of many business workflows, led to slower progress than many expected. Measuring AI ROI As the complexity of deploying AI within the enterprise becomes more apparent in 2025, concerns over ROI will also grow.
The bad news, however, is that IT system modernization requires significant financial and time investments. When addressed properly , application and platform modernization drives immense value and positions organizations ahead of their competition, says Anindeep Kar, a consultant with technology research and advisory firm ISG.
Our vision is to be the platform of choice for running AI applications, says Puri. Enterprises are wrestling with a gamut of tools and technologies that are always changing, he says. The system integrator has the Topaz AI platform, which includes a set of services and solutions to help enterprises build and deploy AI applications.
Its aimed at businesses and developers who need accurate and scalable transcriptions for applications like call centers, video captioning, voice assistants and more. Deepgram is addressing problems like poor customer experience and the financial risk associated with it ($3.7 High accuracy for enterprise-grade performance.
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.
For enterprises investing heavily in AI infrastructure, this development addresses a growing challenge. The phased release gives enterprises time to evaluate how optical interconnect technology might fit into their future infrastructure roadmaps. Lightmatters approach could flatten this architecture.
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 As experts in financial services and commodity markets, there must be standard evaluation methods, he said.
Technology leaders in the financial services sector constantly struggle with the daily challenges of balancing cost, performance, and security the constant demand for high availability means that even a minor system outage could lead to significant financial and reputational losses. Vendor lock-in.
As enterprises across Southeast Asia and Hong Kong undergo rapid digitalisation, democratisation of artificial intelligence (AI) and evolving cloud strategies are reshaping how they operate. We will also incorporate emerging application ecosystems such asHarmonyOSinto our environment to broaden customer coverage and serviceability.
IBM has rolled out the latest iteration of its mainframe, replete with AI technology designed to take data-intensive application support well into the future. AI use cases are growing , says IBM, which counts more than 250 for IBM Z including financial fraud detection, medical image analysis, and credit risk scoring.
And some large, cutting-edge enterprises are already beginning to spend money on quantum technology. This means that they have developed an application that shows an advantage over a classical approach though not necessarily one that is fully rolled out and commercially viable at scale. billion so far in 2024.
After all, a low-risk annoyance in a key application can become a sizable boulder when the app requires modernization to support a digital transformation initiative. Accenture reports that the top three sources of technical debt are enterpriseapplications, AI, and enterprise architecture.
With the AI revolution underway which has kicked the wave of digital transformation into high gear it is imperative for enterprises to have their cloud infrastructure built on firm foundations that can enable them to scale AI/ML solutions effectively and efficiently.
HorizonX Consulting and The Quantum Insider, a market intelligence firm, launched the Quantum Innovation Index in February, ranking enterprises on the degree to which theyve adopted quantum computing. Prioritize Because of the complexity of the tasks, ISGs Saylors suggest that enterprises prioritize their efforts.
Give up on using traditional IT for AI The ultimate goal is to have AI-ready data, which means quality and consistent data with the right structures optimized to be effectively used in AI models and to produce the desired outcomes for a given application, says Beatriz Sanz Siz, global AI sector leader at EY.
A PwC Global Risk Survey found that 75% of risk leaders claim that financial pressures limit their ability to invest in the advanced technology needed to assess and monitor risks. CIOs should adopt a proactive, preventative approach managing enterpriseapplications holistically to prevent security gaps before they emerge.
Financial regulations exist to ensure stability and trust in global banking systems. They protect customers, preserve systemic integrity, and help mitigate risks of financial crises. Despite their differences, both emphasize the interconnected nature of financial systems.
For more and more enterprises, it’s an application you run in house. Of 292 enterprises who’ve commented to me on AI plans, 164 say that they believe their real AI benefits will accrue from self-hosting AI, not from public generative services. You can’t buy hardware in anticipation of your application needs,” one CIO said.
To ensure every IT initiative directly contributes to measurable business outcomes, CIOs must move from operational managers to strategic partners, collaborating with business leaders to align IT decisions with enterprise goals.
By Michael Cullum, VP of Engineering at Bud Financial Generative AI (genAI) is a powerful tool that is transforming the financial industry and empowers financial services professionals. From refining risk decisions to shaping innovative propositions and offering predictive customer service, the potential applications are vast.
Currently we are seeing this phenomenon with the new chief AI officer role being established in some enterprises. There has also been the establishment of the chief transformation officer , as some enterprises have chosen to give the keys to making change happen to another C-suite executive.
Agentic AI was the big breakthrough technology for gen AI last year, and this year, enterprises will deploy these systems at scale. According to a January KPMG survey of 100 senior executives at large enterprises, 12% of companies are already deploying AI agents, 37% are in pilot stages, and 51% are exploring their use.
From the start, Meta has made the Llama models available to other enterprises under a license it describes as “open source,” but the creation of the new business group makes clear that Meta’s interest is commercial, not philanthropic. Keeping control However, anyone wanting to use the latest Llama 3.2
We may look back at 2024 as the year when LLMs became mainstream, every enterprise SaaS added copilot or virtual assistant capabilities, and many organizations got their first taste of agentic AI. AI at Wharton reports enterprises increased their gen AI investments in 2024 by 2.3 CIOs should consider placing these five AI bets in 2025.
Poor resource management and optimization Excessive enterprise cloud costs are typically the result of inefficient resource management and a lack of optimization. Many enterprises also overestimate the resources required, leading to larger, more expensive instances being provisioned than necessary, causing overprovisioning.
Vendor support agreements have long been a sticking point for customers, and the Oracle Applications Unlimited (OAU) program is no different. That, in turn, can lead to system crashes, application errors, degraded performance, and downtime. Understanding your current security posture.
Computing costs rising Raw technology acquisition costs are just a small part of the equation as businesses move from proof of concept to enterprise AI integration. The rise of vertical AI To address that issue, many enterprise AI applications have started to incorporate vertical AI models.
A Zero Trust platform ensures applications and data are not visible to the public internet and users are only provided least privilege access, preventing lateral movement and protecting against ransomware attacks. Zero Trust architecture was created to solve the limitations of legacy security architectures.
S/4HANA is SAPs latest iteration of its flagship enterprise resource planning (ERP) system. The SAP Business Technology Platform offers in-memory processing, agile services for data integration and application extension, as well as embedded analytics and intelligent technologies. What is S/4HANA?
Well also examine strategies CIOs can use to address these challenges, ensuring their organizations can recognize the rewards of GenAI without compromising financial stability. This emphasizes the difficulty in justifying new technology investments without clear, tangible financial returns. million in 2025 to $7.45
AI, and gen AI in particular, are continuing to bombard the enterprise, but the gains to date havent been as big, nor come as quickly, as many business leaders hoped. Thats according to the fourth quarterly edition of Deloitte AI Institutes State of Generative AI in the Enterprise report released on Tuesday.
The average organization adds or updates some 300 services every month, creating a significant challenge for security teams charged with protecting enterprise cloud-based resources, notes Unit 42. Application layer protocols such as SNMP, NetBIOS and PPTP are most often susceptible.
And theres no shortage of applications for new discovery of magnetic materials, says Alan Baratz, CEO at D-Wave Quantum, a company that makes annealing-style quantum computers. And we should see enterprise adoption as the breadth and richness of these simulations evolves. There is growing and rich research interest, he said.
CIOs often have a love-hate relationship with enterprise architecture. On the one hand, enterprise architects play a key role in selecting platforms, developing technical capabilities, and driving standards.
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?
The key areas we see are having an enterprise AI strategy, a unified governance model and managing the technology costs associated with genAI to present a compelling business case to the executive team. Our research indicates a scramble to identify and experiment with use cases in most business functions within an enterprise.
Emmelibri Group, a subsidy of Italian publishing holding company Messaggerie Italiane, is moving applications to the cloud as part of a complete digital transformation with a centralized IT department. We’re an IT company that’s very integrated into the business in terms of applications, and we put innovation at the center.
We organize all of the trending information in your field so you don't have to. Join 83,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content