Congratulations IT, it's an AI!

"The business has decided to deploy an AI app to transform our customer experience. Can you provision some compute, some GPU, and 100TB of storage to get started?"

In the evolving landscape of technological innovation, the adoption of Artificial Intelligence (AI) has become a strategic imperative for businesses aiming to enhance their customer experiences and streamline operations. CIOs and VPs of Infrastructure are tasked with orchestrating the deployment of AI applications that promise transformative outcomes. However, amidst the buzz surrounding AI, it's crucial to navigate the infrastructure and platform requirements to ensure seamless integration to include your people and processes.

Reflecting on my tenure as an IT engineer, I recall the recurrent scenario of receiving requests from various business leaders across the organization for provisioning resources to support new applications. Whether it was an accounting or manufacturing line application, the process often followed a familiar pattern. A link to an online form would be dispatched, detailing the compute, storage, and networking prerequisites, and our team would then provision the required resources.

Cookies, coffee, and other treats were very effective at getting a provisioning request expedited!

"Ok, it's ready for installation by the software vendor," we'd announce, swiftly closing the ticket. Occasionally, I'd find myself shadowing a consultant during the installation process, striving to acquaint myself with the application's nuances. However, the delineation of support responsibilities remained clear-cut: software issues warranted a call to the vendor, while infrastructure performance fell squarely on our shoulders as the IT team.

Fast forward to today, and AI initiatives are being thrust upon IT and infrastructure teams akin to unexpected (and delightful!) deliveries. However, for many organizations, AI adoption predominantly translates to provisioning hardware equipped with GPUs and deploying containers capable of leveraging these resources. Whether it's embracing a conversational search solution like Pryon or integrating a private copilot for developers such as Codeium, the underlying infrastructure remains foundational.

The advent of AI underscores the imperative of leveraging operationally efficient platforms and fostering proficiency within organizational teams. Yet, beneath the veneer of cutting-edge AI applications, the underlying hardware infrastructure remains grounded in familiar principles. From server patching to firmware updates, the fundamentals of infrastructure management persist, albeit in the context of AI-driven endeavors.

For CIOs and VPs tasked with navigating the complexities of AI deployment, a strategic approach to infrastructure provisioning is indispensable. Here are key considerations to guide your AI infrastructure strategy:

1. Seamless Hardware Management: Ensure the ability to easily patch and upgrade server hardware to maintain optimal performance and security standards.
2. Integrated and Scalable Storage: Provision and safeguard storage resources to accommodate the influx of structured and unstructured data generated by AI applications.
3. Clear GPU Sizing: Understand the GPU unit of measure for a given solution (e.g. number of developers) and how that translates to a particular GPU in scope. e.g. 500 accountants per NVIDIA L40S, or 20 queries per second on an L4.
4. Optimizing GPU Utilization: Understand and monitor GPU consumption, particularly during the pilot phase of AI initiatives, to effectively allocate resources and mitigate bottlenecks.
5. Containerized Deployment: Streamline the provisioning, migration, and protection of containers hosting AI applications, machine learning frameworks, and databases to facilitate agility and scalability.
6. Integrated Troubleshooting: Establish mechanisms for seamlessly troubleshooting the AI infrastructure, enabling swift differentiation between hardware, infrastructure, and application-related issues to expedite resolution processes.
7. Operational Proficiency: Look at platforms focused on overall operational efficiency with a full lifecycle perspective, not just focused on the initial deployment.

In conclusion, as AI continues to permeate diverse facets of business operations, the role of IT in orchestrating seamless infrastructure support becomes paramount. By prioritizing operational efficiency, scalability, and proficiency, CIOs and VPs can empower their organizations to harness the full potential of AI while navigating the intricacies of infrastructure management with confidence and agility.

🔑 takeaway: Relax. For IT, AI is just another containerized application with a few unique requirements (fancy GPUs) that still requires enterprise lifecycle management of the entire stack.