Once seen as an abstract concept, artificial intelligence (AI) and generative AI (genAI) have become more normalized as organizations look for 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 a “new era for AI-driven innovation,” with a focus on practical, easy-to-use AI solutions for enterprises. Meanwhile, Foundry’s CIO Tech Poll: Tech Priorities Study 20241 found that 70% of IT decision makers will increase spending on AI-based tools by 2024. These types of events and studies show that organizations have a great interest and willingness to adopt AI. Despite the enthusiasm, actual adoption and implementation remains a challenge for many.

Key challenges for AI innovation

An eBook from Dell Technologies2 reveals that common barriers to entry for AI include 1) data science skills shortages; 2) the increasing size and complexity of data work; and 3) a lack of processing power and skills that lead to delays in recognizing the value of 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. Additionally, an Equinix survey found that 42% of IT leaders believe their existing infrastructure is not fully prepared for the demands of AI technology.3.

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

This may involve investing in high-performance computing (HPC) environments, improving data storage solutions to handle massive data sets, and upgrading networking capabilities to ensure seamless data flow. Additionally, organizations must consider the scalability of their infrastructure to meet the growing computational demands of advanced AI models.

Choose an infrastructure that suits your organization

The success of GenAI is largely determined by its large language model (LLM) capabilities, which take up a large amount of space to train. This can become an obstacle for businesses due to the perceived high costs, but organizations can realize significant savings if they choose the right solutions for their specific goals. After all, AI solutions are not one-size-fits-all due to the diverse use cases; it is crucial that organizations find a partner who understands the business and aligns with their objectives.

Dell AI Factory with NVIDIA is a good example. This is the industry’s first end-to-end enterprise AI solution, designed to meet the complex needs of enterprises looking to leverage AI technologies. As organizations operate in a world where data is increasingly distributed across multiple locations, the solution enables deployments across landscapes. Whether the data is on-premises, in colocation data centers, in the public cloud, or on the device itself, companies can easily generate content with a simple query. NVIDIA H100 GPU and Dell APEX also provide four times more cost-effective inference compared to public cloud over three years.

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

Moving to an AI-optimized cloud platform may sound like a daunting task, but it is the next step organizations must take to stay competitive in this evolving digital landscape. By laying the right foundation and equipping the workforce with relevant skills, companies can easily scale as they evolve and continue to innovate with genAI.

(1) Foundry, CIO Tech Poll: Tech Priorities Study 2024, March 2024

(2) Dell Technologies, Innovate faster with GPU-accelerated AI, 2023

(3) Equinix, Equinix 2023 Global Tech Trends Survey, H2 2023