Cloud Support with AI: the future of support starts now!

Today, 70% of companies say they lack complete visibility into what's happening in the cloud, even after migrating part or all of their operations there. This data, from Forrester, exposes a contradiction that grows every year.

Cloud July 25, 2025 5 min read By: Skyone

Introduction

Today, 70% of companies say they lack complete visibility into what's happening in the cloud , even after migrating part or all of their operations there . This data, from Forrester , exposes a contradiction that grows every year.

In practice, this means that failures, bottlenecks, or risks can go unnoticed for weeks, costing much more than downtime: they cost trust in the business . This is where the combination of Cloud Support with AI (artificial intelligence) can be a game-changer, uniting automation, predictability, and speed to keep the cloud as it should be: invisible when everything is working, present when action is needed .

In this article, we'll explore what changes when support evolves from reactive to intelligent , how this shift connects to business strategy, and why preparing your company now can save you a lot of warnings tomorrow .

Let's work together to understand how to prepare for this new support?

What is AI-powered Cloud Support

Cloud Support was born alongside the mass adoption of the cloud itself . In the early 2000s, when providers like Amazon Web Services (AWS) and Microsoft Azure began offering on-demand infrastructure, companies gained speed to scale, but also became more dependent on external environments that required constant maintenance.

Initially, support followed the traditional model: IT teams resolved tickets manually, often only when the problem had already become a major headache. As businesses became more digital and environments more complex , Cloud Support needed to evolve to ensure not only availability, but also security, compliance , and performance, without disrupting daily operations.

This is where AI changes everything. Automation has taken over repetitive tasks, while algorithms help detect failures before they even impact the end user. The result is more proactive, smarter support that is more aligned with business strategy, and not just a "rescue" when something goes wrong.

But to understand how Cloud Support fits into practice, it's important to know the service models that shape this structure, and how each one defines what is the responsibility of the client and the provider.

Service models: IaaS, PaaS, SaaS

When we talk about the cloud, we're talking about IaaS, PaaS, and SaaS— acronyms that define how companies consume technology today . Even though they are different, they all have one thing in common: they need support to function properly.

In IaaS ( Infrastructure as a Service ), the company contracts servers, network, and storage as needed . It's like renting the infrastructure without buying anything physical, but it's still necessary to ensure that everything is configured, monitored, and protected.

In PaaS ( Platform as a Service ), the provider delivers a ready-to-use platform for creating, testing, and launching applications . Here, support takes care of maintaining a stable, up-to-date, and secure environment so that developers don't waste time on infrastructure tasks.

SaaS ( Software as a Service ) is ready-to-use software and collaboration tools. Support ensures onboarding , automatic updates, integrations with other systems, and data protection.

Regardless of the model, one thing is certain: without a well-defined Cloud Support structure (now enhanced by AI), operations are exposed and the risk of bottlenecks increases . This is the turning point: moving from traditional support to intelligent support that anticipates problems and creates value continuously.

Evolution of Cloud Support : From Maintenance to Intelligent Support

For a long time, cloud support was limited to a basic function : keeping everything running when something failed. The model was mostly reactive: a ticket was opened, the problem was identified, the team fixed it. However, there wasn't much room to anticipate situations or optimize resources before the impact arrived.

This reality began to change as the cloud became established as a cornerstone of critical business operations for businesses of all sizes. Today, a failure doesn't just affect an isolated system: it can paralyze sales, customer service, logistics, or even the delivery of services to the end customer. That's where AI found an opportunity to do what humans can't do on the same scale and speed.

Intelligent automation emerged as a natural response to increasing complexity . Tasks that were once manual are now monitored and executed by algorithms, while real-time analytics identify patterns, bypass bottlenecks, and correct errors before the user even notices.

Companies that adopt this approach transform support into a layer of business intelligence , moving beyond simply being a fixed maintenance cost and instead generating value by protecting revenue, reputation, and productivity.

Automation and AI: AIOps, chatbots , auto-healing

But this shift doesn't happen on its own: it relies on practical automation strategies that, together, support much faster, more proactive, and smarter support . Three of them are already a reality in large operations and have the potential to gain even more traction: