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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. However, its only when combined with automation and orchestration that the technologies full potential can be unlocked.
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.
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. Enterprisetechnology 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.
In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. The foundation of the solution is also important.
Technology continues to advance at a furious pace. 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.
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. Scalability. Cost forecasting. Vendor lock-in.
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.
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.
“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.”
Artificial Intelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. Nutanix commissioned U.K.
Generative AI is likely to confuse the capital investor as much as any technology ever has,” he adds. In many cases, CIOs and other IT leaders have moved past the peak expectations about what gen AI can do for their organizations and are headed into more realistic ideas about the future of the technology, Lovelock adds.
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.
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.
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.
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.
A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. But this scenario is avoidable.
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. Looking ahead to 2025, Lalchandani identifies several technological trends that will define the Middle Easts digital landscape.
The AI revolution is rewriting the CIO playbook across industries, creating unprecedented opportunities for technology leaders to elevate their strategic impact. And third, systems consolidation and modernization focuses on building a cloud-based, scalable infrastructure for integration speed, security, flexibility, and growth.
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.
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.
Regardless of the driver of transformation, your companys culture, leadership, and operating practices must continuously improve to meet the demands of a globally competitive, faster-paced, and technology-enabled world with increasing security and other operational risks.
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.
At the same time, however, the business may have so much riding on legacy technology that it cant afford not to maintain and update it. On the contrary, vendors like IBM, Oracle and SAP remain very committed to continuing to support enterprise offerings that they first introduced decades ago.
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. AI can be assistive technology,” Dyer says.
As new technologies and strategies emerge, modern mainframes need to be flexible and resilient enough to support those changes. 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.
Generative AI has seen faster and more widespread adoption than any other technology today, with many companies already seeing ROI and scaling up use cases into wide adoption. Vendors are adding gen AI across the board to enterprise software products, and AI developers havent been idle this year either.
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. For instance, organizations can implement ideal code examples and preferred processes into code-writing models.
It also supports the newly announced Agent 2 Agent (A2A) protocol which Google is positioning as an open, secure standard for agent-agent collaboration, driven by a large community of Technology, Platform and Service partners. Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy.
To thrive in todays business environment, companies must align their technological and cultural foundations with their ultimate goals. At Brown & Brown, we constantly focus on articulating the value of technology in terms of business outcomes. To us, its not just about using technology its about thinking like a tech company.
With our longstanding technology and go-to-market partnership, we are yet again innovating to deliver value in the space of cyber and disaster recovery. VMware Live Recovery on GCVE enables customers to benefit from a consistent VMware experience, with the elasticity and scalability of the cloud.
However, enterprise cloud computing still faces similar challenges in achieving efficiency and simplicity, particularly in managing diverse cloud resources and optimizing data management. Enterprise IT struggles to keep up with siloed technologies while ensuring security, compliance, and cost management.
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.
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.
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.
They are using the considerable power of this fast-evolving technology to tackle the common challenges of cloud modernization, particularly in projects that involve the migration and modernization of legacy applications a key enabler of digital and business transformation. The result is a more cybersecure enterprise.
Commercial enterprises are increasingly leveraging technology to drive sustainable growth and optimize operations, all while minimizing environmental impact. Through scalable processes, real-time data, and advanced analytics, companies are reinventing their business models to achieve efficiency and reduce waste.
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.
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.
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. Green computing contributes to GreenOps by emphasizing energy-efficient design, resource optimization and the use of sustainable technologies and platforms.
An operating model defines the organizational design, core processes, technologies, roles and responsibilities, governance structures, and financial models that drive a businesss operations. This centralized operating model promotes consistency, governance, and scalability of generative AI solutions across the organization.
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