AI's Demand: Shifting Cloud Strategies!

One fact will change everything around the status quo of IT/cloud deployments today. If short of time, scroll to the bottom and read.

Introduction

Cloud computing revolutionized the traditional IT landscape. Previously, IT infrastructure demanded manual installation and provisioning before deploying applications. The advent of cloud computing introduced the ability to provision IT infrastructure via APIs. This innovation meant that infrastructure requests and provisioning could be done either locally, like with VMWare, or remotely, as with AWS.

This shift to cloud computing marked a significant change: provisioning became instantaneous, and in the case of AWS, it appeared as if capacity was limitless.

This transformation sparked immediate experimentation and minimized the cost of failure. Rapid success became manageable, as additional capacity could be easily integrated, though not without challenges such as steep cloud bills and the difficulty of breaking away from the proprietary tools and data gravity of the chosen cloud provider.

This shift was a catalyst for the explosion of Web 2.0 applications, reshaping the world into what we predominantly experience today through our smartphones.

The AI-driven transformation

We are now entering a new phase, an AI-powered reality. The focus has shifted from rapid provisioning and scaling of infrastructure to ensuring the availability, transport, and security of the data essential for powering the AI models. These models are the key to unlocking new insights and actions. This transition is driving a fundamental change in how IT infrastructure will be designed and implemented in the future.

A new Perspective on cloud's future role

Dave McCarthy, VP of research at IDC, emphasizes this transition.

“Cloud’s next phase requires a shift in how developers and enterprises think about getting applications and data closer to their customers ... The biggest change in attitude I’ve seen about cloud is a shift from thinking of it as a location — a remote data center managed by someone else — to an operating model that can be deployed anywhere,” [link ]

The inherent value of the cloud, with its remote access and instantaneous creation and management of infrastructure and services, remains as vital as ever. As artificial intelligence algorithms become more widespread, enterprises are compelled to redesign their IT platforms from highly centralized systems to highly distributed ones. This shift is essential for keeping applications and data in closer proximity.

Independence and automation in cloud management

Enterprises aim to avoid being tethered to a single cloud provider. Instead, they are increasingly seeking an automation abstraction layer that unifies all cloud locations, allowing them to select the most suitable application execution sites based on the application's needs, rather than being limited by each cloud provider’s proprietary management systems.

Managing distributed systems is inherently complex. It requires abstracting away the underlying ownership of infrastructure and enabling operations to focus on the application layer. This approach encompasses everything from day 0 planning and day 1 launch to day 2 ongoing operations. For more education see here .

The imperative of automation in application lifecycle management

To thrive in this digitally-driven future, automating all aspects of application lifecycle management is crucial. The goal is to execute faster, with fewer errors, and at a lower cost. This approach is becoming a fundamental success factor for all digitally enabled businesses.

In conclusion: a paradigm shift

The proximity of apps and data is more critical than ever. This evolution is set to change everything in the IT and cloud deployment landscape. As a call to action, businesses must adapt and embrace these changes to stay competitive in this rapidly evolving digital era.

And the one fact that will change everything

Apps and data need to be closer together.