Building Robust AI Infrastructure for Enterprise Success

Rohan Sharma

Chapter Chapter 20 in AI and the Boardroom, 2024, pp 247-258 from Springer

Abstract: Abstract How well is your AI infrastructure equipped to support advanced AI initiatives? In the evolving landscape of artificial intelligence, a solid foundation of architecture and infrastructure is crucial for scaling and optimizing AI capabilities. This chapter dives into the key components necessary for building a resilient AI framework—from integrating large language models (LLMs) to securing compute resources and fostering robust data management. Organizations need to invest in enterprise-level services, specialist teams, and scalable infrastructure that can accommodate the demands of large datasets and high computational requirements. The chapter also covers LLMs’ architecture, training, and security considerations, highlighting both the opportunities and challenges in deploying these systems effectively. Key takeaway: To achieve AI success, businesses must build a scalable and secure AI infrastructure, integrate seamlessly with enterprise systems, and prioritize continuous investments in talent and technology. Does your infrastructure provide the support required to realize AI’s potential, or are gaps limiting your innovation efforts?

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:979-8-8688-0796-1_20

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DOI: 10.1007/979-8-8688-0796-1_20

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