Making the shift from computation to cognition

Making the shift from computation to cognition

Once perceived as an abstract concept, Artificial Intelligence (AI) and generative AI (genAI) have become more normalized as organizations look at ways to implement them into their tech stack. From improving the customer experience to enabling intelligence-driven business decisions, organizations recognize the need for AI to thrive in today’s digital economy.

The annual Google Cloud Next event declared 2024 as a “New era for AI-driven innovation,” with a focus on practical, user-friendly AI solutions for enterprises. Meanwhile, the CIO Tech Poll: Tech Priorities Study 2024 by Foundry1 found that 70% of IT decision makers are increasing spend on AI-enabled tools in 2024. Events and studies like these demonstrate a keen interest and willingness for organizations to adopt AI. Despite the enthusiasm, actual adoption and implementation remains a challenge for many.

Key challenges for AI innovation

An eBook by Dell Technologies2 reveals that the common barriers to entry for AI include 1) skills shortages in data science; 2) the increasing volume and complexities of data work; and 3) lack of processing power and skills that lead to delays in recognizing value from data.

The common denominator here is ultimately the lack of an AI-ready infrastructure. In the eBook, 86% of organizations identify at least one technology roadblock to AI success. In addition, a survey by Equinix found that 42% of IT leaders believe that their existing infrastructure is not fully prepared to meet the demands of AI technology3.

Powerful genAI models often require substantial bandwidth for training and development. For that reason, organizations cannot simply adopt new AI capabilities and implement them into their existing networks. Instead, they need to take a step back and revisit their overall infrastructure, perhaps even take a new approach to computing.

This might involve investing in high-performance computing (HPC) environments, enhancing data storage solutions to handle vast datasets, and upgrading network capabilities to ensure seamless data flow. Additionally, organizations must consider the scalability of their infrastructure to accommodate the growing computational demands of advanced AI models.

Choosing an infrastructure that’s right for your organization

GenAI’s success is largely driven by its large language model (LLM) capabilities, which takes up a large amount of space to train. This can become a roadblock for businesses due to the perceived high costs, but organizations can enjoy significant savings if they choose the right solutions for their specific goals. After all, AI solutions are not a one-size-fits-all due to the varying use cases; it is crucial that organizations find a partner that understands the business and are aligned with their objectives.

Dell AI Factory with NVIDIA is a prime example. This is the industry’s first end-to-end enterprise AI solution designed to address the complex needs of enterprises seeking to leverage AI technologies. As organizations operate in a world where data is increasingly distributed across multiple locations, the solution enables deployments across various landscapes. Whether the data resides on premises, at colocation data centers, the public cloud, or on the device itself, businesses can facilitate easy content generation with a simple query. NVIDIA H100 GPU and Dell APEX also provide 4x more cost-effective inferencing compared to the public cloud over a three-year period.

And especially given the current economic uncertainty, having pay-as-you-go flexibility is advantageous for organizations to rapidly adopt AI solutions without the hefty upfront investments. Solutions like Dell APEX let users pay only for what they use so they can closely align their financial and operational needs as the technology evolves.

Moving to an AI-optimized cloud platform may sound like a daunting task, but it’s the next step that organizations need to take to remain competitive in this evolving digital landscape. By laying the right foundations and equipping the workforce with relevant skill sets, enterprises can easily scale as they evolve and continue innovating with genAI.

[1] Foundry, CIO Tech Poll: Tech Priorities Study 2024, Mar 2024

[2] Dell Technologies, Innovate Faster with GPU-accelerated AI, 2023

[3] Equinix, Equinix 2023 Global Tech Trends Survey, H2 2023

“Once perceived as an abstract concept, Artificial Intelligence (AI) and generative AI (genAI) have become more normalized as organizations look at ways to implement them into their tech stack. From…”

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Making the shift from computation to cognition

Once perceived as an abstrac…