Infrastructure for AI: Why the cloud is the foundation of digital transformation

The phase of doubt regarding the disruptive potential of technology is a thing of the past. Currently, in the corporate environment, almost no one questions whether Artificial Intelligence (AI) will redesign work structures. The real debate has shifted to the field of execution: how to transform this strategic conviction into real, operational capability?

A special edition of MIT Technology Review Brazil, developed in partnership with Skyone, shed light on this scenario by investigating the impact of AI on Brazilian organizations and the formation of new "hybrid teams" (composed of humans and intelligent systems). The central diagnosis reveals a clear mismatch between the top of the strategic pyramid and the technical reality on the digital factory floor: companies have very high ambitions, but an infrastructure capacity that is still progressing slowly.

If your company wants to stop accumulating "permanent pilots" and start reaping structural results with AI, you need to understand why the cloud is the non-negotiable foundation for this transition.

The Brazilian Disparity: Consensual Desire vs. Scarce Budget

To understand the magnitude of the challenge, one only needs to look at the data gathered by Skyone's research, "AI at Work: 20 Insights into Hybrid Teams".

Expectations surrounding intelligent agents are virtually unanimous: 99% of companies believe that AI agents will be central to their business within the next three years. Furthermore, 53% of professionals cite artificial intelligence as the technology with the greatest recent impact on their work, even surpassing the internet itself (30%).

However, when we analyze the organizational conditions for making this ecosystem work, the numbers don't add up:

• 74% of organizations are still in the early or intermediate of technology adoption.
• 57% of companies do not have a dedicated budget for Artificial Intelligence initiatives.
• 59% of leaders admit that their organizations are not prepared to operate with hybrid teams in the short term.

MIT's diagnosis: this international disparity is not unique to us. The MIT NANDA report, The GenAI Divide: State of AI in Business , analyzed more than 300 initiatives and found that 95% of organizations are still not capturing measurable returns from generative AI . The reason? The extreme difficulty of integrating algorithms into real workflows.

The technical bottleneck: why are 59% of companies stuck?

The corporate market bought into the AI ​​narrative, believing it could be integrated like a technological "add-on," simply requiring the purchase of an off-the-shelf tool and anticipating a surge in productivity. However, practice has shown that when the algorithm attempts to leave the laboratory, it encounters fragmented systems, scattered data, and outdated local databases.

According to a study by MIT Technology Review and Skyone, only 41% of Brazilian companies use the cloud as a foundation for data and AI. The other 59% still operate with partial cloud or predominantly on-premises infrastructure, a condition categorically classified by the study as inadequate to support serious and scalable projects.

Attempting to run complex intelligent agents or robust language models on legacy local servers generates three fatal problems:

1. Data fragmentation and silos

AI thrives on clean, governed, and integrated data. If your company's information is scattered across isolated spreadsheets and systems that don't communicate with each other, the algorithm will generate flawed, inaccurate, or irrelevant answers. For 40% of companies, integration between departments and systems is the biggest challenge today.

2. Lack of elasticity and scalability

AI models require brutal processing spikes at specific times (such as model training or real-time predictive analytics). An on-premises infrastructure lacks the capacity to expand and contract resources on demand, leading to slowdowns in the ERP system and operational bottlenecks.

3. Cybersecurity vulnerability and traceability

Putting strategic corporate data or sensitive customer information to work in AI engines without a proprietary layer of protection and encryption poses an extremely high risk of non-compliance and data breaches.

Less tool fetishism, more data architecture

As Luiz Pecci , IT and Digital Director at Mundo do Cabeleireiro, aptly defined in his statement to the publication, AI is not a magic off-the-shelf product. "It's a journey of construction, in which the organization needs to teach the AI ​​its business rules, its decision criteria, and its operational context . "

For this mechanism to function and for the investment to yield a Return on Investment (ROI), companies need to focus less on the "tool fetish" and more on backend architecture. This is where integrated platforms like Skyone Studio.

Technology shouldn't be an obstacle to your business growth. Skyone Studio was designed precisely to eliminate organizational silos and pave the way for digital maturity through four essential pillars:

• iPaaS (Integration Platform as a Service): centralizes and connects data from over 400 systems (such as the leading ERPs, CRMs, and e-commerce platforms), ensuring automated workflow without the need to rewrite complex code.
• Cloud Lakehouse: a modern cloud infrastructure that unifies storage and enables high-performance, optimized queries for Business Intelligence (BI) and Machine Learning.
• Customized AI Agents: allows you to build AI robots that truly understand the specific rules of your business and its operational context, securely automating bureaucratic routines.

The optimized human: the future of hybrid work

The consolidation of the cloud as a foundation for artificial intelligence is not intended to diminish the role of people in companies, but rather to elevate it. When the technical foundation is solid, mechanical, repetitive tasks based on predictable patterns migrate to computational agents.

The result? Professionals stop spending hours filling out spreadsheets or searching for scattered data and assume the role of strategic supervisor. Essentially human skills, such as critical thinking, contextual judgment, emotional intelligence, and the ability to ask the right questions, become the true competitive differentiator in the market.

The winner in the age of intelligence will not be the one who merely repeats enthusiastic speeches at innovation events. It will be the one who manages, in a practical and structured way, to connect the algorithm to the real workflow. And this journey necessarily begins in the cloud.

Do you want to transform experimentation into real results in your company?

Companies that want to extract real value from Artificial Intelligence need to start by organizing and governing their data. Download the Special Edition and discover the paths that technology leaders are taking to accelerate innovation and achieve concrete results with AI.

Written by Skyone