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July 26, 2024, 6:45 a.m.
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Flexential's 2024 Report: AI Infrastructure Reliance on Colocation Data Centers

According to Flexential's 2024 State of AI Infrastructure report, organizations are increasingly relying on third-party colocation data centers for their artificial intelligence (AI) infrastructure needs. Only 24% of respondents deploy AI hardware on-premise, with 51% opting to lease rack space in third-party data centers located closer to the edge of their networks. The report also highlights that IT leaders are willing to pay a premium for data centers or cloud vendors that prioritize sustainability, with 94% stating their willingness to pay more for clean or renewable energy usage. Additionally, the report predicts that AI infrastructure will consume up to 134 TWh of power annually by 2027.

Data center operators are consequently exploring microgrid solutions to integrate renewable energy and ensure resilience. The report also touches on the increasing adoption of AI, with 64% of respondents naming automation as a key driver, and organizations leveraging AI to bridge the skills gap and improve productivity. However, Honeywell's industrial AI report warns that AI implementation can be expensive, particularly when working with existing infrastructure.



Brief news summary

According to a report by Flexential, an increasing number of businesses are turning to third-party colocation data centers for their AI infrastructure needs. The report shows that only 24% of respondents choose to deploy AI hardware on their own premises, while 51% opt to lease rack space in external data centers. This allows companies to process data closer to the network edge. The study also reveals that 94% of IT leaders are willing to pay extra for data centers that use clean or renewable energy sources, demonstrating a growing commitment to reducing carbon emissions. Data center operators are responding to this demand by exploring options such as integrating renewable energy into microgrids. At the same time, organizations are making significant investments in infrastructure and developing in-house AI applications to handle the increasing workloads driven by AI. The adoption of AI is driven by factors such as automation, improved cybersecurity, and the generation of real-time data for better decision-making. Despite the costs involved, organizations are also leveraging AI to address skills gaps, enhance worker efficiency, and boost productivity. To ensure successful AI implementation, it is essential for organizations to have access to sufficient training resources to introduce AI to their employees.
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