Artificial intelligence is no longer a promise and has become the center of corporate decisions. Companies that two years ago tested generative models are now rewriting entire processes based on technology. And experts from AWS and Oracle say that 2026 will be the year that AI officially leaves the experimental phase and becomes business critical infrastructure.
The movement follows global estimates that indicate that the AI market should surpass US$300 billion in 2026according to the Worldwide Artificial Intelligence Spending Guidedriven by the expansion of autonomous agents, the customization of models and the growing need for governance.
To understand what will really shape the technological and professional landscape in the next year, Neilson CrepaldeAWS specialist and professor at XP Educação, and João Fariaexecutive at Oracle and Coordinator of postgraduate courses in technology at XP Educação, highlight that the turn to 2026 will not be marked by specific news, but by maturity and strategic efficiency.
AI Agents
After the advancement of copilots in 2024 and expanded automation in 2025, the year 2026 should mark the dissemination of autonomous agents — systems capable of performing end-to-end tasks, interacting with tools, making decisions based on rules and reviewing their own performance.
Crepalde highlights that this is one of the most visible inflection points: “we are entering the phase in which models stop being just systems that answer questions and become digital collaborators. AI agents are able to plan, execute and review tasks from end to end — and this changes the logic of efficiency within companies.”
The expectation is that these agents will appear in areas such as service, logistics, auditing, compliance, data analysis and software development, reorganizing operational flows.
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GraphRAG
Another strong trend for 2026 is the advancement of GraphRAGa technique that integrates natural language and graph structures to allow deeper and more contextual searches.
The approach enables AI to retrieve documents and understand how data connects — something essential for companies with large internal bases.
The expert explains: “the big limitation of generative models has always been the difficulty of understanding deep relationships between information. GraphRAG solves this. It allows AI to navigate complex structures, connect dots and deliver much more accurate answers. For large corporate bases, it is a game changer.”
The trend is expected to gain traction in sectors such as finance, insurance, retail and public administration, which depend on sensitive and interconnected information.
Fine tuning, model distillation and model specialization
For 2026, the expectation is that companies will prioritize smaller and specialized models, trained for specific needs through fine tuning and model distillation.
The adoption of this format is associated with cost reduction, increased efficiency and easier auditing, an increasingly critical factor in regulated environments. In this context, compact, fast and customized models tend to replace generalist and large-scale solutions.
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The movement also opens space for an area on the rise: With IA audit.
AI regulation is a matter of attention
With the expansion of autonomous agents and greater regulatory pressure, the demand for tools and processes capable of monitoring decisions, metrics and risks associated with models is growing. This set of practices is known as AI observability.
Continuous oversight should drive advancement in the AI Governance area, which includes internal policies, security protocols, risk review, and compliance frameworks.
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Companies are now demanding trackable and auditable AIs, especially in segments such as finance, healthcare and critical infrastructure.
The careers that should stand out in 2026
The consolidation of artificial intelligence as a business infrastructure not only changes processes and technologies, but also the remuneration logic in the labor market. As companies begin to depend on more complex systems, the competition for professionals capable of building, supervising and governing the use of AI grows.
Among the most valued profiles is AI Engineerprofessional responsible for developing, integrating and monitoring systems based on artificial intelligence, combining software engineering, data science and model architecture.
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According to data from Glassdoor, the average salary for this position in Brazil is R$9,792 per montha value that tends to grow as autonomous agents and specialized models become a central part of company operations.
Another rising career is experts in security and compliance applied to AI. With the advancement of regulations and growing concern about legal risks, data privacy and responsible use of technology, companies are looking for professionals capable of balancing innovation and control.
According to Glassdoor, the average salary for a Compliance Specialist in the country is R$ 15,850 monthlyreflecting the strategic weight of the function in highly regulated environments, such as the financial sector.
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Also gaining space are AI governance professionalsresponsible for defining internal policies, supervising the life cycle of models, ensuring auditing and mitigating operational and reputational risks. Although it is still a relatively new role, it builds on existing IT governance structures.
Data from Glassdoor indicates that the average salary of a IT Governance Analyst in Brazil it is R$7,792 per monthwith a tendency to increase in value as AI governance becomes mandatory.
For the coordinator of postgraduate courses in technology at XP Educação, these numbers reflect a structural change in the profile of talent most sought after by the market.
“The professional of the future will not be just technical or just strategic. He will be hybrid. And this combination of product vision, analytical skills and mastery of how models work is what will separate those who lead from the functions that will disappear.”
Why does 2026 indicate a new phase of AI?
After years marked by experimentation, enthusiasm and specific tests, artificial intelligence enters 2026 under a new logic: return on investment. Companies continue to invest heavily in AI, but the central question is no longer “what can be done” to become “what generates measurable value”.
For Crepalde, this is one of the technology’s differences in relation to other waves of digital innovation. “The great advantage of AI solutions is that the return tends to appear faster. Unlike long structural projects, it is possible to measure efficiency gains, cost reduction and increased productivity in much shorter cycles.”
This ability to measure quickly changes how executives make decisions. Instead of broad and poorly defined projects, companies start to prioritize specific use cases, with clear impact metrics, such as saving operational hours, reducing errors, gaining scale or improving service.
In practice, this means that 2026 will be less about wholesale adoption and more about strategic efficiency. Projects that do not deliver clear results tend to lose ground, while solutions that are well integrated into business processes gain priority.
