Why the cloud is the natural path for enterprise AI

As AI gains ground in companies, the cloud becomes essential to support this evolution with efficiency and scale.

By Frankllin Nunes, Global Head of Architecture at Teltec Data.

For many years, cloud computing was treated as a step in digital transformation. Today, I see that it has ceased to be just a technological alternative and has become the infrastructure that supports the next great revolution in companies: artificial intelligence.

For effective operation, AI relies on large volumes of data and intense processing, something that the cloud delivers easily. In the cloud, a company can scale resources up or down as needed, without depending on investments in physical infrastructure.

I have been observing companies from various sectors accelerating AI initiatives. This movement is natural. Technology has ceased to be a topic restricted to innovation labs to support strategic decisions, automate processes, improve customer relationships, and increase team productivity.

The success of artificial intelligence does not depend solely on the quality of models or algorithms. It primarily depends on the infrastructure capable of sustaining this operation at scale.

It is common to find companies that start AI projects using local servers, believing that this choice offers greater control over data and environments. In some cases, this makes sense for very specific applications. However, as artificial intelligence becomes part of business operations, the limitations of traditional infrastructure quickly become evident.

AI consumes increasingly larger volumes of data, requires enormous processing capacity, and demands constant updates. This means continuously investing in new equipment, expanding computational capacity, ensuring availability, and maintaining specialized teams to manage all this infrastructure. Many companies end up putting energy into keeping the infrastructure running when they should be focused on generating value with artificial intelligence itself.

According to the FutureScape: Worldwide Cloud 2025 Predictions report by IDC, over 90% of new digital initiatives will need to use cloud-native architectures in the coming years, primarily driven by the expansion of artificial intelligence and advanced data analytics. Gartner projects that global investments in public cloud services will exceed $1 trillion by 2027, reflecting the strategic role that the cloud has assumed in companies. The discussion has shifted from “to migrate or not to the cloud” to how to build an architecture ready to leverage the full potential of artificial intelligence.

Computational resources can be scaled up or down in just a few minutes, following application demand without requiring large upfront investments in physical infrastructure. This allows companies to experiment with new AI models, develop pilots quickly, and scale projects according to the results obtained.

The leading cloud providers continuously incorporate new services related to artificial intelligence, machine learning, data analytics, automation, and security. Instead of rebuilding all this capacity internally, companies start consuming these technologies as services, accelerating their innovation capacity.

As AI agents begin to access corporate information, execute processes, and support critical decisions, the need to control identities, protect data, ensure regulatory compliance, and continuously monitor the entire environment grows. Cloud platforms have evolved precisely to offer these mechanisms in an integrated manner, allowing security and innovation to go hand in hand.

Artificial intelligence will continue to evolve at a rapid pace. Therefore, the cloud is not just a technological choice; it is the foundation that makes AI viable, scalable, and sustainable in companies. However, adopting artificial intelligence in the cloud requires more than just choosing a platform. It is necessary to have strategy, technical knowledge, and business vision to build an efficient, secure architecture aligned with business objectives.