IT and business: the secret to scaling AI in companies

The phase of doubt is definitely behind us. In 2026, no serious corporate leader will question whether or not Artificial Intelligence (AI) will reshape the job market. The real question that keeps executives awake at night has shifted to the operational field: how to transform strategic conviction into real execution capability?

Many organizations have incorporated AI into their institutional discourse, gaining space in boardrooms and transformation plans. However, when we look at the day-to-day operations, the reality is far less glamorous. Instead of robust scaling, what predominates in the market are so-called "permanent pilots," experiments that impress in internal demonstrations but fail to translate into continuous organizational capabilities.

If your company is facing this barrier, know that the problem isn't a lack of access to the algorithm or technology. The real bottleneck is internal organization.

A special edition of MIT Technology Review Brazil, developed in partnership with Skyone, shed light on this transition and revealed the real reason why AI projects often stall: the lack of alignment between IT and Business areas.

Below, we analyze the data from this diagnosis and show how to break through these barriers.

The mismatch between ambition and actual ability

Market expectations regarding AI agents are almost unanimous. According to the study "AI at Work: 20 Insights on Hybrid Teams," conducted by Skyone, an impressive 99% of companies believe that AI agents will be central to their business within the next three years.

However, there is a chasm between what is desired and what the current structure can support. The same survey indicates that:

• 74% Many organizations are still in the early or intermediate stages of technology adoption.
• 57% They don't even have a dedicated budget for Artificial Intelligence initiatives.
• 59% They claim they are not prepared to operate with hybrid teams (humans + AI systems) in the short term.

This pattern of "much enthusiasm and little structural result" is not unique to Brazil. The report The GenAI Divide: State of AI in Business, published by MIT NANDA, analyzed more than 300 initiatives and identified that 95% of organizations still do not capture measurable returns from generative AI. The root of the problem? The immense difficulty of integrating the technology into real workflows.

MIT Technology Review's diagnosis: the silo is the bottleneck itself

When AI projects attempt to move from the innovation lab to the corporate world, they clash head-on with legacy infrastructure and culture. Fragmented systems, scattered data, and nascent governance are the most common symptoms of an old corporate ailment: operational silos.

Skyone's research revealed compelling data about this isolation:

• 40% of companies cite integration between departments as the main challenge to including AI in daily processes.
• In 46% of organizations, business and IT still operate in isolated silos, either because they work completely separately or because there is no clearly defined collaboration dynamic.

Historically, companies were designed to coordinate people and distribute responsibilities in a linear fashion. With the arrival of the hybrid workforce, IT began to buy or develop tools based on generic promises of efficiency. On the other hand, the Business area often doesn't know how to translate its pain points and operational rules into computer systems.

As Luiz Pecci, IT and Digital director interviewed in this edition, points out, AI is not an off-the-shelf product that you install and magically solve operational problems. It's a continuous construction journey where the company needs to actively teach the algorithm its market context and decision criteria. And this becomes impossible if those who possess business knowledge don't communicate with those who manage the technology.

How to unify IT and business to scale AI

For Artificial Intelligence to move beyond being a mere peripheral layer of mechanical automation and start generating real strategic value, the company's technical architecture needs to keep pace with the ambition of the business.

We need to stop treating AI solely as an IT project and start viewing it as a structural reorganization of workflows. This requires three fundamental steps:

1. Literacy and common repertoire

Training in AI is not a whim or a cultural detail; it's an operational prerequisite. Promoting meetings between technical and business leaders to align on what the technology can do, where it requires human supervision, and how it alters routines mitigates defenses and creates a unified language across departments.

2. Data governance and preparation

One of the major technical problems highlighted in the study is that only 41% of companies have the cloud as a consolidated foundation for data and AI. Trying to run intelligent assistants and automated decision-makers on scattered spreadsheets and systems that don't communicate with each other generates flawed and dangerous responses. Organizing, integrating, and centralizing data is the first step.

3. Focus on process redesign (and not just marginal gain)

Investing in AI solely to speed up repetitive tasks yields immediate but insufficient gains. The true competitive leap occurs when technology is integrated to support high-level decisions, reshaping occupations. Functions cease to be isolated boxes and begin to incorporate capabilities shared between humans and systems.

Skyone Studio: the platform that eliminates technological silos

Understanding that corporate complexity lies not in "having" the technology, but in integrating it, Skyone developed Skyone Studio. It's a unified and comprehensive solution specifically designed to connect the digital ecosystem end-to-end and pave the way for true AI.

Skyone Studio works by eliminating silos and unifying infrastructure through four essential pillars:

• iPaaS (Integration Platform as a Service): centralizes and connects information from over 400 market systems and applications (such as ERPs, CRMs, and external databases) in an intuitive way, allowing corporate data to flow seamlessly.
• Lakehouse: a robust cloud layer that organizes, standardizes, and transforms large volumes of historical and real-time data, providing a single, reliable foundation for advanced analytics.
• AI Agents: Enables the development and deployment of intelligent, customized virtual agents for specific business rules, automating complex operational workflows.
• Conversational Platform with BI: an interface that enables business users to interact with complex databases using natural language, democratizing access to strategic insights without relying on time-consuming technical requests.

By unifying systems integration, data intelligence, and process automation under a single interface, Skyone Studio restores agility to IT and strategic control to business areas. It's technology working to break down operational silos and transform experimentation into scalable results.

Conclusion: Bridge the gap between intention and scale

The greatest risk for organizations today is not being left out of the conversation about Artificial Intelligence. The real danger is remaining enthusiastic about it, reporting on it, and holding events, but never crossing the chasm that separates intention from real-world scaling.

Technology has matured enough to expose the structural weaknesses that companies have been putting off for years. Less fetish for isolated tools, more integration architecture. Less talk of innovation, more operational discipline.

Download the Special Edition and gain access to insights, trends, and analyses that help technology and business leaders overcome adoption barriers and accelerate value creation with AI.

Written by Skyone