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The majority (91%) of respondents agree that long-term IT infrastructure modernization is essential to support AI workloads, with 85% planning to increase investment in this area within the next 1-3 years. While early adopters lead, most enterprises understand the need for infrastructure modernization to support AI.
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.
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?
Many are reframing how to manage infrastructure, especially as demand for AI and cloud-native innovation escalates,” Carter said. While Boyd Gaming switched from VMware to Nutanix, others choose to run two hypervisors for resilience against threats and scalability, Carter explained.
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.
In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. The norm will shift towards real-time, concurrent, and collaborative development fast-tracking innovation and increasing operational agility.
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. Intel’s cloud-optimized hardware accelerates AI workloads, while SAS provides scalable, AI-driven solutions.
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. This collaboration marks a significant step in driving innovation in cloud services, particularly in the MENA region.
To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Conclusion In this post, we’ve introduced a scalable and efficient solution for automating batch inference jobs in Amazon Bedrock. This automatically deletes the deployed stack.
growth this year, with data center spending increasing by nearly 35% in 2024 in anticipation of generative AI infrastructure needs. This spending on AI infrastructure may be confusing to investors, who won’t see a direct line to increased sales because much of the hyperscaler AI investment will focus on internal uses, he says.
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. This process includes establishing core principles such as agility, scalability, security, and customer centricity.
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. Legacy infrastructure. Scalability.
As telecom executives work to navigate these challenges, finding a balance between fostering innovation and managing operating expenses is no longer optional it is a necessity for survival. This speed to market supports innovation while keeping costs in check, as telecoms quickly adapt to new opportunities.
“Then we need to bring that to the [financial services] community as a whole for evaluating [generative AI] models and solutions,” said Dayalji, who is also CEO of Kensho, S&P Global’s AI innovation hub. “I Secondly, how do you give them tools to do different work and innovate?”
The gap between emerging technological capabilities and workforce skills is widening, and traditional approaches such as hiring specialized professionals or offering occasional training are no longer sufficient as they often lack the scalability and adaptability needed for long-term success.
That’s great, because a strong IT environment is necessary to take advantage of the latest innovations and business opportunities. Start by evaluating your organization’s current infrastructure, applications, and processes to identify critical pain points, inefficiencies, and opportunities.”
However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. Ensuring effective and secure AI implementations demands continuous adaptation and investment in robust, scalable data infrastructures. To succeed, Operational AI requires a modern data architecture.
There are two main considerations associated with the fundamentals of sovereign AI: 1) Control of the algorithms and the data on the basis of which the AI is trained and developed; and 2) the sovereignty of the infrastructure on which the AI resides and operates.
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.
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.
In essence, the role of a CIO has evolved to become a nexus of innovation, leveraging technologies like AI and hybrid multicloud operations to enhance efficiency and agility and deliver customer-focused solutions. Our roadmap at Nutanix is clear: To stay ahead, we must harness these innovations. Learn more about Nutanix.
Yet, as transformative as GenAI can be, unlocking its full potential requires more than enthusiasm—it demands a strong foundation in data management, infrastructure flexibility, and governance. With the right systems in place, businesses could exponentially increase their productivity.
CIOs are responsible for much more than IT infrastructure; they must drive the adoption of innovative technology and partner closely with their data scientists and engineers to make AI a reality–all while keeping costs down and being cyber-resilient. Artificial intelligence (AI) is reshaping our world.
By ensuring consistent, high-quality product data, we enable businesses to unlock AIs full potential to drive growth, innovation, and exceptional customer experiences. From chatbots handling customer queries to algorithmic pricing strategies and automated inventory management, retailers are finding innovative ways to leverage AI capabilities.
With technology rapidly shaping business outcomes, and the tech infrastructure supporting every aspect of business, CIOs much deservedly now occupy a seat at the table. This ensures that our technology roadmap is fully aligned with our overarching business objectives and fosters a continuous cycle of innovation and efficiency.
The print infrastructure is not immune to security risks – on average, paper documents represent 27% of IT security incidents. It has a long heritage in end-user computing and continues to drive security innovation across its personal systems and print business.
AI cloud infrastructure startup Vultr raised $333 million in growth financing at a $3.5 The deal was co-led by AMD Ventures , the venture arm of semiconductor company AMD underscoring the fierce competition between chipmakers to provide AI infrastructure for enterprises.
Despite these opportunities, Tencent Cloud faces challenges from competitors, requiring a careful balancing act between innovation and market adaptability. While this demonstrates Tencent Cloud’s technical capabilities, the real challenge lies in ensuring the scalability and consistency of these solutions across multiple industries.
With our enterprise know-how and industry expertise, HP Professional Services [2] can help you simplify the complexity of migrating to Windows 11 and modern management with Microsoft Intune by offering a dedicated portfolio of services to ensure your applications [3] , devices and infrastructure are Windows 11 ready.
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.
Ever since Steve Jobs stood on stage to unveil the first iPhone in 2007, the focus of the global technology industry has been on innovation in the software, mobile and cloud markets. Suddenly, infrastructure appears to be king again. Organizations are appreciating anew just how important this foundational infrastructure really is.
In todays digital economy, business objectives like becoming a trusted financial partner or protecting customer data while driving innovation require more than technical controls and documentation. 2025 Banking Regulatory Outlook, Deloitte The stakes are clear.
To address these challenges, Atento turned to Avaya, leveraging the flexibility and innovation of Avaya’s solutions to scale its environment seamlessly as the business grew. However, this rapid expansion presented significant challenges in maintaining consistency and efficiency across its global operations.
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.
But by doing so, developers are sl owed down by the complexity of managing pipelines, automation, tests, and infrastructure. Dependencies Modern software systems increasingly rely on various external services, APIs, cloud infrastructures, and third-party tools, creating complex dependencies. But DevOps is just one of many examples.
Protecting industrial setups, especially those with legacy systems, distributed operations, and remote workforces, requires an innovative approach that prioritizes both uptime and safety. This approach not only reduces risks but also enhances the overall resilience of OT infrastructures. –
First, the misalignment of technical strategies of the central infrastructure organization and the individual business units was not only inefficient but created internal friction and unhealthy behaviors, the CIO says. I want to provide an easy and secure outlet that’s genuinely production-ready and scalable.
Cost Savings: Hybrid and multi-cloud setups allow organizations to optimize workloads by selecting cost-effective platforms, reducing overall infrastructure costs while meeting performance needs. This modernization also provided a future-proof platform for advanced analytics and AI-driven insights, ensuring continued innovation.
Of late, innovative data integration tools are revolutionising how organisations approach data management, unlocking new opportunities for growth, efficiency, and strategic decision-making by leveraging technical advancements in Artificial Intelligence, Machine Learning, and Natural Language Processing.
However, their existing infrastructure posed significant limitations. Critical data – including leads, forms, and campaign information – was stored in a legacy CRM (Customer Relationship Management) system that lacked the scalability needed to support their growth ambitions. Ready to Transform Your Healthcare Organization?
The role of CVCs across the global venture funding landscape has continued to rise in importance this past year, as these corporate strategic investment arms serve as one key cornerstone of the global innovation economy.
To accelerate iteration and innovation in this field, sufficient computing resources and a scalable platform are essential. These challenges underscore the importance of robust infrastructure and management systems in supporting advanced AI research and development.
EGA’s digital transformation is driven by a dual-track strategy, designed to deliver both short-term impact and long-term scalability. This empowers the workforce to leverage technology, ensuring scalability and success in the digital age. Carlo points to two major initiatives in this area.
This surge is driven by the rapid expansion of cloud computing and artificial intelligence, both of which are reshaping industries and enabling unprecedented scalability and innovation. Leaders encourage questioning assumptions, exploring innovative ideas and pursuing groundbreaking solutions. Short-term focus.
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