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
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.
In an era where AI is becoming a cornerstone of enterprise strategy, standardization efforts are not merely technical footnotes they represent the infrastructure of our AI-powered future, says Zach Evans, CTO at healthcare AI firm Xsolis. What is MCP?
It has become a strategic cornerstone for shaping innovation, efficiency and compliance. As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. In 2025, data management is no longer a backend operation.
1] The limits of siloed AI implementations According to SS&C Blue Prism , an expert on AI and automation, the chief issue is that enterprises often implement AI in siloes. SS&C Blue Prism argues that combining AI tools with automation is essential to transforming operations and redefining how work is performed.
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. Marc offers a bold new blueprint for technology leaders navigating an era where cybersecurity must scale with innovation. A former U.S.
The Middle East is rapidly evolving into a global hub for technological innovation, with 2025 set to be a pivotal year in the regions digital landscape. AI and machine learning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance.
The first is to foster a culture of agility, collaboration, and AI-driven innovation, driven in part by our new Office of AI. And third, systems consolidation and modernization focuses on building a cloud-based, scalable infrastructure for integration speed, security, flexibility, and growth.
To capitalize on the enormous potential of artificial intelligence (AI) enterprises need systems purpose-built for industry-specific workflows. Strong domain expertise, solid data foundations and innovative AI capabilities will help organizations accelerate business outcomes and outperform their competitors.
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.
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. Its not just about performance benchmarks its about balancing cost, security, explainability, scalability, and time to value, Colisto says.
These new regions are a testament to Oracles confidence in the regions ability to drive innovation, especially as both countries ramp up their efforts to become global leaders in AI and cloud computing. A key point shared during the summit was how the Kingdoms organizations are increasingly investing in AI. Whats Next?
Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. The announcements at Next ’25 included several enhancements: Unified Enterprise Search : Employees can access Agentspace’s search, analysis, and synthesis capabilities directly from Chrome’s search box.
A sharp rise in enterprise investments in generative AI is poised to reshape business operations, with 68% of companies planning to invest between $50 million and $250 million over the next year, according to KPMGs latest AI Quarterly Pulse Survey. However, only 12% have deployed such tools to date.
Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. Scalability. Scalability. Legacy infrastructure.
The event focused on providing enterprises with an AI-optimized platform and open frameworks that make agents interoperable. Taken together, these tools aim to make enterprise AI more practical to deploy, scale, and manage, said Kaustubh K, practice director at Everest Group.
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.
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.
Technology has shifted from a back-office function to a core enabler of business growth, innovation, and competitive advantage. Senior business leaders and CIOs must navigate a complex web of competing priorities, such as managing stakeholder expectations, accelerating technological innovation, and maintaining operational efficiency.
That’s great, because a strong IT environment is necessary to take advantage of the latest innovations and business opportunities. On the other hand, there are also many cases of enterprises hanging onto obsolete systems that have long-since exceeded their original ROI. Technology continues to advance at a furious pace.
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.
“Many are reframing how to manage infrastructure, especially as demand for AI and cloud-native innovation escalates,” Carter said. operator of 28 hotel and casino properties across the US, was negotiating a fresh enterprise agreement with VMware prior to its acquisition, reported The Register.
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. Nutanix commissioned U.K.
The intersection of AI, software, and data management is set to revolutionize healthcare and will serve as a critical driver of medical innovation and improved patient outcomes. Beyond improved patient outcomes, AI integrated into site reliability engineering can help improve the scalability of software systems.
Generative AI can revolutionize organizations by enabling the creation of innovative applications that offer enhanced customer and employee experiences. This enables agile LOB innovation while providing centralized oversight on governance areas. Amazon Bedrock cost and usage will be recorded in each LOBs AWS accounts.
An agentic era needs a platform that brings AI, data, and workflows together, and that should be an open, connected, enterprise-ready platform, said ServiceNows chief innovation officer Dave Wright in a press conference last week. We look at it as distributed intelligence across the enterprise.
To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. Readers will learn the key design decisions, benefits achieved, and lessons learned from Hearst’s innovative CCoE team. This post is co-written with Steven Craig from Hearst.
While AI projects will continue beyond 2025, many organizations’ software spending will be driven more by other enterprise needs like CRM and cloud computing, Lovelock says. The company will still prioritize IT innovation, however. “This year, they did POCs, but it didn’t work out. The key message was, ‘Pace yourself.’”
A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.
Theyre rapidly experimenting with agentic AI in isolated workflows to capture quick wins but scaling enterprise-wide only after clear success metrics are met,particularly around security, observability, and human-in-the-loop validations. But the more telling pattern, he says, is based on enterprise complexity.
But this year three changes are likely to drive CIOs operating model transformations and digital strategies: In 2024, enterprise SaaS embedded AI agents to drive workflow evolutions , and leading-edge organizations began developing their own AI agents.
Cloud sovereignty is central to the European Unions quest for increased digital autonomy, with the aim of fostering innovation and supporting European businesses on their digital transformation journey. Innovation and Growth for European SMEs and Scale-Ups Of course, organizations at varying stages of digital transformation.
The answer informs how you integrate innovation into your operations and balance competing priorities to drive long-term success. Companies like Qualcomm have to plan and commit well in advance, estimating chip production cycles while simultaneously innovating at breakneck speed. A great example of this is the semiconductor industry.
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.
To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components.
Maintaining legacy systems can consume a substantial share of IT budgets up to 70% according to some analyses diverting resources that could otherwise be invested in innovation and digital transformation. A critical consideration emerges regarding enterprise AI platform implementation.
Enterprises that fail to adapt risk severe consequences, including hefty legal penalties and irreparable reputational damage. It means managing and storing data where it will bring the most value to the enterprise, without having to move it.
Driven by the development community’s desire for more capabilities and controls when deploying applications, DevOps gained momentum in 2011 in the enterprise with a positive outlook from Gartner and in 2015 when the Scaled Agile Framework (SAFe) incorporated DevOps.
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.
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 tested, interoperable, scalable, and proven.
With AI at the epicenter of innovation today, bringing AI into Industry 4.0 From plant automation and predictive maintenance in manufacturing to delivering hyper-personalized shopping experiences in retail, edge AI offers a range of possibilities and encourages innovation across industries.
AI in the enterprise has become a strategic imperative for every organization, but for it to be truly effective, CIOs need to manage the data layer in a way that can support the evolutionary breakthroughs in large language models and frameworks. Thats why there is a massive pivot toward AI powered open lakehouse architectures.
Enterprise applications have become an integral part of modern businesses, helping them simplify operations, manage data, and streamline communication. However, as more organizations rely on these applications, the need for enterprise application security and compliance measures is becoming increasingly important.
The reasons are clear: businesses need to be agile and innovative yet cost-efficient to stay competitive, and the cloud is the foundation of those objectives. This is reflected among Australias mid-sized businesses, which are on a growth trajectory driven by a strong emphasis on innovation.
We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This scalability allows for more frequent and comprehensive reviews.
We organize all of the trending information in your field so you don't have to. Join 49,000+ 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