According to a recent<\/span> survey by PWC<\/span><\/a>, AI could add as much as $15 trillion to the global economy by 2030. Even today, artificial intelligence (AI) & machine learning (ML) are being used for a wide variety of tasks like creating large language models, voice recognition, translation mechanisms, and even writing code. It has become a transformative tool that can reshape organizations and industries, and we expect it to be as prevalent in business as Word documents, Internet browsers, and mobile applications in the near future.<\/span><\/p>\n Companies that act immediately and begin developing AI-centric strategies have the opportunity to create a competitive advantage. Consequently, failing to adopt and leverage these technologies to their full potential could make you fall behind the pecking order when it comes to your industry.<\/span><\/p>\n Organizations are taking notice.<\/span><\/p>\n According to<\/span> IDC\u2019s Future Enterprise Resiliency & Spending Survey<\/span><\/a>, 41% of organizations are investing significantly in generative AI with an established spending plan, while another 37% are in the midst of initial model testing & proof of concept projects.<\/span><\/p>\n \u00a0The challenge lies in the fact that AI is advancing rapidly, with datasets expanding in size, models increasing in complexity, and demand for real-time insights becoming crucial for business survival. Large-scale AI-based computation requires intensive computing power & massive volumes of training data. For example, many companies rely on graphics processing units (GPUs) to run AI & ML code, which are extremely power-hungry and heat-generating when compared to CPUs. Through instances like this, it has become increasingly clear that traditional systems do not have the capabilities to optimize your AI & ML initiatives.<\/span><\/p>\n Cloud<\/b> has become the obvious answer for enterprise AI needs, because of benefits like on-demand resource provisioning, pay-as-you-use pricing, and ability to account for AI scalability. According to the survey by IDC, 70% of enterprises will form strategic ties to cloud providers for Gen AI platforms, development tools, and infrastructure by the end of next year. However, one must be cognizant of the evolution of the cloud.<\/span><\/p>\n What exactly is hybrid multicloud? Think of it as your enterprise’s digital ecosystem rather than a single destination. It’s an integrated environment that combines:<\/p>\n We often think of the cloud as a single location, namely the public cloud, and to be fair, the main 3 public cloud platforms have made significant advancements when it comes to accounting for AI:<\/span><\/p>\n IBM recently deemed these two technologies – AI & hybrid cloud – to be the most transformative in the present-day IT space, and the beauty of combining these two in your organizational initiatives is the <\/span>complementary<\/b> nature they both show towards each other. Hybrid multicloud enables you to run your AI & ML applications on a variety of infrastructures based on suitability and maintain their integrity to evolve with changing business needs and regulations. On the other hand, AI can simplify and fortify various components of your hybrid multicloud strategy, like automating routine tasks when it comes to your cybersecurity strategy, speeding your application development time to market and even helping your organization seamlessly with its cloud migration.<\/span><\/p>\n However, AI and ML initiatives cannot follow a<\/span> \u2018one cloud fits all\u2019<\/span><\/i> approach when it comes to the public cloud, for a variety of reasons:<\/span><\/p>\n These challenges have led many to adopt a <\/span>cloud-smart strategy<\/b> that chooses the ideal environment for each AI-based application, whether it’s public clouds, private clouds, or your existing on-premises infrastructure. This makes hybrid multicloud<\/b><\/a> the ideal option for your AI & ML initiatives. Hybrid multicloud is transformational in that it doesn\u2019t treat the cloud as a single location, but as an extensive ecosystem where organizations have the flexibility of choosing what makes sense for individual tasks, projects, or components. Hybrid cloud strategies have recently gained immense popularity in India.<\/span><\/p>\n According to the <\/span>2024 Enterprise Cloud Index (ECI) Survey by Nutanix<\/span><\/a>, hybrid multicloud is the dominant deployment model among Indian enterprises, with 44% of Indian companies using it – a figure that exceeds all other ECI countries surveyed.<\/span><\/p>\n So how does your organization create a strategy that optimizes both AI and hybrid multicloud benefits? We believe your strategy should have these key components:\u00a0<\/span><\/p>\n Following this framework will help you create a complementary hybrid multicloud for AI, leveraging its game-changing benefits like flexibility, scalability, efficiency, and hyper-convergence while having full control over your data. This will help transform your company into a truly data-driven organization that stays ahead of the curve compared to your competitors.<\/span><\/p>\n While the widespread adoption of AI & ML is breathing new life into new use cases and possibilities, it must be done with an eye on sustainability, cost management, security, and other compliances. Given the current global shortage of AI expertise and the complexity of building AI-ready infrastructure, partnering with experienced technology consultants like iValue Group can provide strategic advantages. Our team of specialists can help develop a comprehensive roadmap for your AI and machine learning initiatives, ensuring efficient implementation and sustainable growth while minimizing the challenges of in-house capability building.<\/span><\/p>\n Q: How does hybrid multicloud benefit AI implementations?<\/strong><\/p>\n A: It provides flexibility, enhanced security, cost optimization, and improved performance for AI workloads through distributed computing resources.<\/p>\n Q: What are the key considerations for AI data management in a hybrid environment?<\/strong><\/p>\n A: Organizations must focus on data governance, security, accessibility, and efficient movement across different cloud environments.<\/p>\n Q: How can enterprises ensure security in a hybrid multicloud AI environment?<\/strong><\/p>\n A: Implement comprehensive security frameworks, use AI-powered threat detection, and maintain strict compliance monitoring across all environments.<\/p>\n","protected":false},"excerpt":{"rendered":" The Rise of AI in Enterprise Computing According to a recent survey by PWC, AI could add as much as $15 trillion to the global economy by 2030. Even today, artificial intelligence (AI) & machine learning (ML) are being used for a wide variety of tasks like creating large language models, voice recognition, translation mechanisms, …<\/p>\nUnderstanding Hybrid Multicloud for AI<\/h2>\n
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Building Your AI-Ready Hybrid Infrastructure Strategy<\/h2>\n
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FAQ Section<\/h3>\n