AI and hybrid cloud promise major gains... if the underlying infrastructure can keep up. This article from Deloitte breaks down how hybrid environments are evolving, and why many organizations may be less "ready" than they think. Read the article to evaluate your infrastructure readiness and explore what's required to build a digital foundation for hybrid cloud and AI. Contact Experior I.T. to discuss your next steps in infrastructure modernization.
Is my organization ready for hybrid cloud?
Organizations need to assess their current infrastructure against the demands of AI workloads, including data storage, processing power, and cost management. As AI projects scale, many organizations may find that public cloud costs become prohibitive, prompting a reevaluation of their cloud strategy. Monitoring cloud usage and costs can help identify when it may be more economical to invest in dedicated AI infrastructure rather than continuing to rent cloud capacity.
How can AI hardware innovations impact my organization?
Innovations in AI hardware, such as neural processing units and tensor processing units, can significantly enhance processing efficiency and reduce operational costs. These advancements allow organizations to handle larger data sets and execute complex AI tasks more effectively. By integrating these technologies into their infrastructure, organizations can improve energy efficiency and overall performance, which is crucial as AI workloads continue to grow.
What are the benefits of edge computing?
Edge computing brings data processing closer to the source, which can enhance data security and reduce latency for AI tasks. As the AI edge computing market is projected to grow significantly, organizations that adopt this approach may experience improved ROI and responsiveness. By leveraging edge computing, companies can process data directly on devices, which is particularly beneficial for applications requiring low latency and high data security.
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