With AI adoption accelerating, particularly in generative AI (GenAI), many organizations are scaling beyond isolated pilots to fully integrate AI in their core systems. In 2024, about 82.7% of worldwide organizations are expected to deploy GenAI applications. However, with this shift comes the challenge of managing infrastructure to support these advancements effectively. A recent IDC survey indicates that nearly half of organizations cite inadequate data management, infrastructure and workforce skills as top challenges in achieving AI goals.

GenAI offers a range of applications, from boosting productivity to driving revenue and innovation. However, selecting the right use cases and determining the optimal infrastructure model—whether to "build or buy"—remain critical. Larger enterprises may opt to develop private AI infrastructure for control and scalability, while small-to-medium businesses often leverage cloud-based models for cost efficiency and flexibility.

Modular AI infrastructure is essential for scalability, enabling organizations to integrate new components seamlessly and optimize performance. IDC research also highlights that cloud AI platforms offer compelling options for companies that lack extensive internal infrastructure, with access to specialized AI hardware. Building AI infrastructure isn’t solely about powerful GPUs or CPUs; networking and data management are equally critical to ensure real-time, actionable insights and interoperability across cloud environments.

Key Takeaways:

• Infrastructure Complexity: AI deployment success hinges on modular, scalable infrastructure that accommodates various data types and workloads.
• Data Management: Effective AI strategies require robust data pipelines that handle structured and unstructured data.
• Flexibility in AI Models: Organizations should balance between open-source and commercial models based on their unique applications and infrastructure.

Download this whitepaper to learn how a strategic approach to AI infrastructure can enhance your organization’s readiness for next-generation AI deployments.