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Introduction
Today, 70% of companies say they have no full visibility of what happens in the cloud , even having migrated part or all the operation there . The data is from Forrester and exposes a contradiction that grows every year.
In practice, this means that flaws, bottlenecks or risks can go unnoticed for weeks, costing much more than hours set: they cost confidence in the business . This is where the combination of Cloud Support with AI (Artificial Intelligence) can change the game, joining automation, predictability and speed to keep the cloud as it should be: invisible when everything works, present when you need to act .
In this article, we will explore what changes when support evolves from reactive to smart , how this turn connects to the business strategy, and why preparing your business can now save many alerts tomorrow .
Let's understand how to prepare for this new support?
What is and how the cloud support with ia
Cloud Support was born along with the cloud's own mass adoption . In the early 2000s, when providers such as Amazon Web Services (AWS) and Microsoft Azure began to offer under demand infrastructure, companies gained speed to scale, but also became more dependent on outdoor environments that needed constant maintenance.
In this first moment, the support followed the traditional model: IT teams resolved manually called, often when the problem had already become a headache. As the business became more digital and the most complex environments , Cloud Support had to evolve to ensure not only availability, but also security, compliance and performance, without locking the day to day.
This is where AI changes everything. Automation began to take on repetitive tasks, while algorithms help detect failures even before they impact the end user. The result is more proactive, smarter and more aligned with business strategy, not just a "help" when something goes wrong.
But to understand how Cloud Support fits into practice, it is important to know the service models that shape this structure, and how each defines what is under the responsibility of the customer and the provider.
Service Models: Iaa, Paas, SaaS
When we talk about cloud, we talk about Yaa, Paas and SaaS, acronyms that define how companies consume technology today . Even though they are different, they all have a point in common: they need support to work well.
In IAAS ( infrastructure as a service ), the company hires servers, network and storage as required . It's like renting the structure without buying anything physical, but still, you need to ensure that everything is configured, monitored and protected.
In PAAS ( Platform as Service ), the provider delivers a ready platform to create, test and launch applications . Here, support takes care to keep the environment stable, updated and safe so that developers don't waste time with infrastructure tasks.
SAAS ( Software as A Service ) is the software Ready for use , such as CRM, ERP, collaboration tools. Support guarantees user
onboarding Regardless of the model, one thing is certain: without a well -defined (now enhanced AI) cloud support structure, operations are exposed and the risk of bottlenecks increases . This is the turn: out of traditional support to intelligent support, which 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 working when something failed. The model was mostly reactive: a call was opened, the problem was identified, the team corrected. However, there was not much room to anticipate situations or optimize resources before the impact arrived.
This reality began to change as the cloud has consolidated itself as a pillar of critical business operations of all sizes. Today, a failure does not only affect an isolated system: it can paralyze sales, service, logistics or even delivery of services to the end customer. That's when I was found room to do what humans can't do on the same scale and speed.
Intelligent automation has entered as a natural response to increasing complexity . Once manual tasks have come to be monitored and performed by algorithms, while real -time analyzes identify patterns, divert bottlenecks and correct failures even before the user notices.
Companies that adopt this approach turn support into a business intelligence layer , no longer just a fixed maintenance cost and generating value by protecting revenue, reputation and productivity.
Automation and AI: aiops, chatbots , auto-healing
But this key turns does not happen alone: it rests on automation fronts that together support much faster, proactive and smart support . Three of them are already reality in large operations and have everything to gain even more space:
