If artificial intelligence is already treated as a strategic priority, why do so many companies still struggle to transform it into consistent results?
The DXi 2026, a study by INSI in partnership with the Dom Cabral Foundation, starts precisely from this question. The study shows that AI has gained legitimacy at the executive level and has taken a significant place in discussions about competitiveness, efficiency, and the future of business. At the same time, this advancement still coexists with structural weaknesses that limit its consolidation as an organizational capability. More than 80% of companies recognize the strategic importance of the topic, but the necessary maturity to sustain this movement has not yet evolved at the same pace.
Why artificial intelligence is already a strategic priority in companies
For a long time, artificial intelligence was treated as a promise. Now, the scenario is different. The study shows that the convincing phase has been overcome and that the technology is no longer seen just as support but occupies a more strategic position within organizations. AI is already seen as a priority, is perceived as a competitive advantage by a large part of companies, and already influences how leaders think about growth, productivity, and differentiation.
What the DXi reveals, however, is that recognizing value is not the same as building capability. In many companies, AI has already reached leadership discourse but has not yet achieved the same degree of integration in operations, governance, and structural business decisions. It is in this gap between intention and execution that the main bottlenecks arise.
The main challenges of artificial intelligence in companies today
One of the most relevant points of the study is the shift in the debate. The central difficulty no longer seems to be in accessing the technology itself but in the ability to coordinate its use with strategic clarity, internal repertoire, and executive responsibility. The main obstacle pointed out by executives is the lack of specialized knowledge, indicating that the discussion about AI in companies is less about the existence of tools and more about the ability to make consistent choices about where, how, and why to apply them.
This perspective gains strength when the study shows that the technological base is already treated as a priority by a good part of the leadership and that technology decisions are, in many cases, perceived as aligned with business objectives. Even so, most companies continue to operate with insufficient alignment between the AI strategy and business areas. This helps explain why the topic advances in relevance but not always in cross-functional capability.
How Artificial Intelligence is Currently Being Used in Companies
The DXi also shows that artificial intelligence already produces practical effects within companies. Many organizations associate AI with productivity gains and primarily use it in areas related to operational efficiency, automation, and task acceleration. This path is understandable. Generally, it is through these applications that the technology begins to demonstrate a clearer and faster return.
However, the study itself suggests that this can also be a limitation. When AI remains focused on specific applications, aimed only at optimizing operations, it improves existing workflows without necessarily reconfiguring processes, expanding analytical capacity, or supporting more sophisticated decisions. The problem is not with these uses, which are valid and important, but in stopping there. The next cycle, according to the study, tends to favor companies that manage to integrate AI into the business model more consistently.
AI Governance: Why Many Companies Still Struggle to Advance
If there is one axis that helps explain the difficulty of turning priority into results, that axis is AI governance. The study shows that 80% of organizations do not regularly monitor AI maturity and 74% do not have structured risk management practices. These data speak not only of control. They reveal the absence of mechanisms to sustain scale, learning, and continuous evolution.
Without this type of monitoring, progress tends to be fragmented. Projects are tested, pilots gain visibility, and some gains appear, but the company does not necessarily build a decision architecture capable of expanding these uses safely. In this scenario, governance ceases to be an operational detail and becomes a condition for AI to stop being an experiment and become a real strategy.
How Data and Training Impact AI Maturity in Companies
Another important point brought by the DXi is the relationship between data, people, and the real capacity for evolution. The study indicates that half of the companies have already adopted architectures such as datalakes, but only a small portion can use this infrastructure as a more advanced basis for analysis and artificial intelligence. This suggests that the foundation exists but is still underutilized when the goal is to transform information into applied intelligence.
On the people front, the signal is equally clear. AI is still not a priority in the training and development agendas of 55.8% of companies. This data may be one of the most revealing of the study because it exposes a recurring contradiction: technology rises on the strategic agenda before the organization develops, with the same intensity, the necessary human repertoire to operate it well.
Why Adopting AI Is Not the Same as Generating Results
The most interesting point of DXi 2026 might be this: the market will no longer be differentiated solely by who adopts artificial intelligence, but by who manages to institutionalize it. This means connecting strategy, governance, data, metrics, training, and operation into a coherent system. The study emphasizes this idea by showing that the current challenge is no longer just prioritizing technology, but transforming the installed base into better decisions, with scale and coordination.
As summarized by Telmo Costa, CEO of INSI, “companies that truly capture value treat technology differently: they connect AI directly to business strategy, establish clear governance, define impact metrics on the P&L, and develop internal competencies based on data and team literacy.”
Therefore, the question that opens this article remains the most important. If AI is already a strategic priority, why is it still so difficult to generate consistent results? The DXi suggests a straightforward answer: because competitive advantage does not arise solely from technology adoption, but from the organizational capacity to sustain it. In other words, what separates intention from result is not the tool, it is the structure.