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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.
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.
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. No one wants to be Blockbuster when Netflix is on the horizon, he says.
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.
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.
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.
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.
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.
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.
Always on the cusp of technology innovation, the financial services industry (FSI) is once again poised for wholesale transformation, this time with Generative AI. GenAI-powered financial services use cases Across the sector, GenAI is empowering innovation and enabling new work patterns.
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.
While useful, these tools offer diminishing value due to a lack of innovation or differentiation. This allows companies to benefit from powerful models without having to worry about the underlying infrastructure. Alternatively, several models can be operated on-premises if there are specific security or data protection requirements.
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.
This involves the integration of digital technologies into its planning and operations like adopting cloud computing to sustain and scale infrastructure seamlessly, using AI to improve user experience through natural language communication, enhancing data analytics for data-driven decision making and building closed-loop automated systems using IoT.
Ambitious businesses are already eyeing the next leap forward in AI technology fuelled by the growing imperative to deliver business success driven by digital innovation. 1] The next horizon for savvy enterprises seeking to automate at hitherto unseen levels of scale in 2025 is agentic AI. Its about every component working together.
On a good day, this disconnect can lead to missed opportunities, slower decision-making and limited innovation. They are instrumental in navigating the complex intersection of technology and business, driving innovation, and accelerating decision-making in ways traditional leadership roles have struggled to do.
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.
Global professional services firm Marsh McLennan has roughly 40 gen AI applications in production , and CIO Paul Beswick expects the number to soar as demonstrated efficiencies and profit-making innovations sell the C-suite. Enterprises are also choosing cloud for AI to leverage the ecosystem of partnerships,” McCarthy notes.
Sevita is dedicated to providing adults, children, and their families innovative services and support designed to lead to growth and independence despite physical, intellectual, or behavioral challenges. You ’re building an enterprise data platform for the first time in Sevita’s history. What’s driving this investment?
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.
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. Whats Next?
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.
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.
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. Thats 100% accurate, says Patrick Buell, chief innovation officer at Hakkoda, an IBM company. Microsofts Phi, and Googles Gemma SLMs.
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. To succeed, Operational AI requires a modern data architecture.
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.
The world must reshape its technology infrastructure to ensure artificial intelligence makes good on its potential as a transformative moment in digital innovation. John Gallant, CIO.coms Enterprise Consulting Director and Vito Mabrucco, NTT Corp. John Gallant, CIO.coms Enterprise Consulting Director and Vito Mabrucco, NTT Corp.
With the right AI investments marking the difference between laggards and innovative companies, deploying AI at scale has become an essential strategy in today’s business landscape. This development is due to traditional IT infrastructures being increasingly unable to meet the ever-demanding requirements of AI.
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.
At the Mobile World Congress (MWC) 2025, Huawei has positioned itself at the forefront of technological innovation, showcasing its latest advancements in 5G, artificial intelligence, and cloud computing. The company unveiled its next-generation 5G infrastructure, promising faster speeds, lower latency, and greater efficiency.
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.
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. This improves system reliability and ensures that healthcare infrastructure remains robust and efficient.
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.
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.
With AI agents poised to take over significant portions of enterprise workflows, IT leaders will be faced with an increasingly complex challenge: managing them. If I am a large enterprise, I probably will not build all of my agents in one place and be vendor-locked, but I probably dont want 30 platforms.
As enterprises continue to grow their applications, environments, and infrastructure, it has become difficult to keep pace with technology trends, best practices, and programming standards. This post covers how to integrate Amazon Q Business into your enterprise setup.
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.
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.
Broadcom has once again been recognized with a prestigious 2025 Google Cloud Infrastructure Modernization Partner of the Year for virtualization. This powerful combination empowers enterprises to seamlessly extend their on-prem environments to the cloud.
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. Maintaining and upgrading outdated systems can be resource-intensive and hinder innovation.
By eliminating time-consuming tasks such as data entry, document processing, and report generation, AI allows teams to focus on higher-value, strategic initiatives that fuel innovation. As AI usage spreads, data frequently moves between different infrastructures, making it harder to keep track of and protect.
The 2024 Enterprise AI Readiness Radar report from Infosys , a digital services and consulting firm, found that only 2% of companies were fully prepared to implement AI at scale and that, despite the hype , AI is three to five years away from becoming a reality for most firms. Is our AI strategy enterprise-wide?
These efforts dont just bridge the skills gapthey create a culture of innovation around the mainframe. That means taking a measured, iterative approach to modernization, and finding the infrastructure that works best for the unique needs of your enterprise. As one CIO put it at SHARE: The goal isnt to move off the mainframe.
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