After intense days of conversations, panels and exchanges with CIOs, CEOs and technology leaders during the IT Forum Trancoso, a feeling was difficult to ignore: the technology industry has definitely entered a change of logic and not a change of cycle.
For decades, the technology industry has been organized around the logic of delivering hours, scaling the number of people and growing revenue. This model, which moved the market and supported global information technology service giants, is beginning to show clear signs of exhaustion.
Not because the demand disappeared. On the contrary. Never has so much technology been required. But now, what is at stake is no longer an effort and has become a result delivered.
Artificial intelligence doesn't just speed up deliveries. It changes the nature of what is delivered. When algorithms start to perform tasks previously performed by entire teams, the value stops being in “how much is done” and migrates to “what is solved”. And the market already demands this logic. Increasingly, he refuses to pay for hours when he can hire impact on the business.
This shift seems subtle, but it is transformative in its essence. It breaks the historical link between growth and headcount. And, with that, it dismantles the economic logic that has sustained much of the sector until now.
What emerges at this moment is still timid, but some signs that change will happen faster than imagined are evident. The first is the consolidation of a platform-oriented model, in which knowledge, data and analytical intelligence are packaged as scalable assets, rather than as bespoke projects that start over from scratch with each contract.
The second is time compression. AI reduces cycles, shortens decisions and exposes organizational inefficiencies with unprecedented clarity. Companies are no longer just behind technologically, they are out of line in speed. Digital-native companies are in a race against the threat of disruption from AI-native companies, whether because they will drastically reduce costs or because they will redesign value capture models.
This mismatch creates a phenomenon that is beginning to gain a name, the “AI speed gap”. Professionals adopt tools and increase their individual productivity, while organizations remain stuck in structures, processes and decision models inherited from another era.
There has never been so much production — and it has never been more difficult to capture value from this production.
It is at this point that the discussion stops being technological and becomes strategic. In this context, it is not enough to incorporate AI. It is necessary to reconfigure the way value is created, delivered and captured.
This requires a more profound change than many companies are willing to admit. Because it involves gradually abandoning the comfort of predictable models, based on measurable effort, to assume the complexity of operating for results.
Furthermore, it requires a redefinition of the role of technology in organizations. Infrastructure stops being just support and becomes a condition for competitiveness. Data stops being an input and becomes a strategic asset. And intelligence, previously restricted to specific analyses, now operates as a continuous layer of decision-making, no longer isolated.
In practice, this means moving away from the "digital transformation" discourse to a more mature stage, that of applied intelligence. Therefore, it is no longer about digitizing existing processes, but about redesigning business logic based on the ability to learn, decide and act in real time. In this new context, companies that insist on protecting old models tend to face a well-known dilemma, that is, the more successful they were in the past, the greater the difficulty in reinventing themselves.
Those that are able to translate their expertise into scalable assets, combining data, AI and domain knowledge, will be closer to capturing the value of this new phase.
The transition will not be linear. There will be adjustments, short-term losses and zones of uncertainty. But one thing is clear: the discussion is no longer about technology. It is, increasingly, about the business model. And, mainly, about who is willing to transform it before it stops making sense.