The artificial intelligence market is expected to surpass US$ 300 billion in 2026. It is a striking number — but what it represents in practice is more interesting than the figure itself: AI stopped being a trend and became infrastructure.
The last three years were marked by the pilot phase. Companies tested AI in isolated projects, with small teams, experimental budgets, and expectations calibrated for "learning." In 2026, that model is being replaced by another: large-scale implementation, integration with critical systems, and real business metrics.
Deloitte mapped eight major AI trends for 2026, and the common thread among all of them is the same: operational maturity. It is no longer about what AI can do — it is about how you operate, govern, and scale what you already know works.
67% of Brazilian companies consider AI a strategic priority in 2026. The stated focus is operations optimization, cost reduction, and new revenue sources — in that order.
The federal government has a Brazilian Artificial Intelligence Plan with planned investments of R$ 23 billion through 2028. Part of that amount goes to research and training, part to infrastructure, and part to adoption by the public sector.
For comparison: Brazil represents about 2.5% of global GDP. An investment of R$ 23 billion in AI over four years is modest compared with the amounts the US and China are putting on the table — but it is a sign that the topic has entered state planning, which has implications for regulation, public procurement, and workforce training.
One of the most concrete changes of 2026 is that multimodality — the ability to process text, image, audio, and structured data in a single model — stopped being a differentiator and became an expectation.
Gemini 2.5 Pro has native audio output. GPT-5.4 and Claude Opus 4.7 process high-resolution images. The next frontier is already real-time video and industrial sensors as input.
For manufacturing, healthcare, and logistics companies, this means the use cases that previously required separate systems for each type of data can be consolidated. Reduced integration complexity is a direct gain.
The autonomous agents market is growing from US$ 8.5 billion to US$ 35 billion by 2030. More than the size, what matters is the multiplier effect: a well-implemented agent can carry out the work that previously required hours of human operation in minutes, with greater consistency and a marginal cost close to zero.
The companies reporting ROI of 200% to 400% in the first year of agent adoption are not exaggerating — they are measuring the automation of high-volume repetitive processes, where the impact is immediate and measurable.
In companies that are managing to convert AI investment into results, three factors appear consistently: organized and accessible data, clear governance over the models' use and outputs, and teams that know how to ask the right questions — not just operate the tools.
The barrier in 2026 is no longer technological. It is organizational. The technology is available, cheap, and works. What separates those who capture value from those who merely follow the news is execution.
