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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.
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. These platforms also seamlessly integrate with enterprise data fabric, enabling a unified approach to securing sensitive data across silos.
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.
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.
Enterprise features. Flexibility and scalability. The following checklist is built to help you evaluate the scope of services offered by various encryption solutions on the market and covers questions on the following topics: Encryption. Key management system. User authentication and advanced security factors.
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.
To capitalize on the enormous potential of artificial intelligence (AI) enterprises need systems purpose-built for industry-specific workflows. Enterprise technology leaders discussed these issues and more while sharing real-world examples during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI.
Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. DAMA-DMBOK 2.
After a dark few years in the wake of the FTX collapse and the digital currency winter that followed, theres light at the end of the tunnel, and optimism is returning to the enterprise blockchain industry. This time, the focus is squarely on utility and scalable ledgers.
AI practitioners and industry leaders discussed these trends, shared best practices, and provided real-world use cases during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
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.
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. Cost forecasting. The results?
On the other hand, there are also many cases of enterprises hanging onto obsolete systems that have long-since exceeded their original ROI. Understandably, many enterprises may want to ‘sweat the assets’ in order to get the most out of their systems, but this tactic comes with risk,” he warns.
“AI deployment will also allow for enhanced productivity and increased span of control by automating and scheduling tasks, reporting and performance monitoring for the remaining workforce which allows remaining managers to focus on more strategic, scalable and value-added activities.”
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.
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 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.
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. While Boyd Gaming switched from VMware to Nutanix, others choose to run two hypervisors for resilience against threats and scalability, Carter explained.
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.
Looking beyond existing infrastructures For a start, enterprises can leverage new technologies purpose-built for GenAI. This layer serves as the foundation for enterprises to elevate their GenAI strategy. They help companies deploy the tool with ease, reducing the time spent on designing, planning, and testing digital assistants.
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.
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. We’re consistently evaluating our technology needs to ensure our platforms are efficient, secure, and scalable,” he says. The key message was, ‘Pace yourself.’”
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. These metrics might include operational cost savings, improved system reliability, or enhanced scalability.
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.
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. It may surprise you, but DevOps has been around for nearly two decades.
As enterprise CIOs seek to find the ideal balance between the cloud and on-prem for their IT workloads, they may find themselves dealing with surprises they did not anticipate — ones where the promise of the cloud, and cloud vendors, fall short versus the realities of enterprise IT. How long do they retain these logs?” Levine says.
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.
In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. AI agents , powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses.
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.
To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. With Amazon Cognito , you can authenticate and authorize users from the built-in user directory, from your enterprise directory, and from other consumer identity providers.
Today, a startup called Forward Networks — which has built digital twin modeling software geared specifically at helping to operate and maintain enterprise networks — is announcing some funding on the heels of strong growth. Capital and Andreessen Horowitz.
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.
However, even in a decentralized model, often LOBs must align with central governance controls and obtain approvals from the CCoE team for production deployment, adhering to global enterprise standards for areas such as access policies, model risk management, data privacy, and compliance posture, which can introduce governance complexities.
And third, systems consolidation and modernization focuses on building a cloud-based, scalable infrastructure for integration speed, security, flexibility, and growth. With AI, we can now deliver the wow factor, which increases momentum and shows the power of the wheel to the entire enterprise.
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.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. It enhances scalability, flexibility, and cost-effectiveness, while maximizing existing infrastructure investments.
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.
Beyond improved patient outcomes, AI integrated into site reliability engineering can help improve the scalability of software systems. Enterprises should use ethical frameworks to ensure that AI applications undergo rigorous testing and validation before being deployed in order to safeguard patient safety and data privacy.
Because data management is a key variable for overcoming these challenges, carriers are turning to hybrid cloud solutions, which provide the flexibility and scalability needed to adapt to the evolving landscape 5G enables. However, the complexity of managing workloads across different environments can be daunting.
Running AI on mainframes as a trend is still in its infancy, but the survey suggests many companies do not plan to give up their mainframes even as AI creates new computing needs, says Petra Goude, global practice leader for core enterprise and zCloud at global managed IT services company Kyndryl.
Bringing mainframe data into a cloud environment has the ability to unlock a new level of real-time analysis and insight from data that can reshape the way operations are managed across the enterprise. This integration enhances the overall efficiency of IT operations.
It enables confident, secure cyber recovery through a dedicated, step-by-step workflow that integrates guided restore point selection, safe validation of recovery points in an isolated recovery environment (IRE) with live behavioral analysis, network isolation to prevent reinfection, and recovery orchestration at enterprise scale.
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