We enter 2026 with the feeling that artificial intelligence (AI) has “done (or can) do everything”. But the year 2025 serves to separate promises from practice, especially not what concerns company processes. With accelerator technology; as organizations, nor by itself. Models like ChatGPT or Gemini are common, and many businesses have (or are available) or access to generative artificial intelligence (GenAI) “for everyone”: assistants (co-pilots) write e-mails and reports, summarize documents, prepare presentations and information. Result? Growing benefits, meditation difficulties and a certain amount of discomfort: why do we end up with the minutes we have put together? That time doesn’t get diluted day by day if it automatically turns into value and increases productivity.
At the same time, let’s continue to look at many old decisions guided by intuition and habit without really drawing on the power of dice and AI. Here comes the other face of AI: not generative AI that analyzes (text, image, text, video), but analytical AI that predicts, recommends and makes decisions based on human-defined goals and large amounts of information. This allows you to reduce customer service costs, improve service levels, increase production line efficiency or reduce carbon consumption.
We’re also in 2025, when AI agents will proliferate. We are dealing with digital collaborators capable of planning and executing tasks, assisting other agents, and adapting their behavior to context. The list of expectations comes from the transition from “IA que escreve” (GenAI) to “IA que faz” (Agentic AI). But please keep in mind: many tropeçarial projects will try to automate processes designed by humans and for humans, without being redesenhar. Amara continues to apply to her: we estimate the impact without the short lunch and underestimate the impact without the long lunch. The potential transformer is still largely equalized.
In 2026, a key movement is ready to experiment with impact. These are, less than “pretty pilots”, mostly results associated with trade indicators. The big dilemma we’ve seen this year is that AI will become a mere tool that becomes a watchdog. Once you’re just reacting, move on to planning and executing with less human intervention. However, value is not one of the “ter agents”. This is the basis for the workflow that humans and AI agents play best for.
We will also know from the wardrobe of “every person with or with your co-pilot” and move to the center of AI no “process”. A simple example: no provision, instead of an associate asking to “email supplier”, the flow will be managed by an agent who consolidates needs, verifies contracts, requests details in regulations, compares proposals and makes recommendations. It is the person who really demands change: exceptions, delicate negotiations, and reputational decisions.
The game goes from “to automatiza mais” to “it decides better, mais depressa”. We will have agents in procurement to negotiate terms within limits, in RH to pre-candidate and schedule consultations, and in operations to detect performance violations and open “tickets” (pedidos) in context. In the financial function, an “organization with the help of agents” will be created. Organization and relationship processing processes will return less delay, supported by algorithms that propose scenarios, correct forecasts and provide alerts in near real time. A financial manager spends less time compiling information and more time interpreting signals and testing hypotheses. The decision-making process will be accelerated and the traditional planning and control cycles will be faster.
Multimodality will also provide the following: models that combine text, tables, sound, and images to interpret the world better than we do. For example, a factory can look at photos of components with sensors and relays to estimate component condition and recommend maintenance interventions before a fire.
It is clear that to increase the time needed to integrate into the business strategy, it needs to be treated as a combination of isolated initiatives and a purely technological theme. The basis is the capacity of the device. It’s not clear that we’re all data scientists, but almost all of us will be interested in digital and AI literature. This means knowing how to do certain things, verifying and interpreting results, and understanding work processes in light of technology.
By the end of 2026, it will not be the year when a machine replaces a human. It will be a year when one learns to liberate the value you promise and fear to enter, through your attitude, context, and responsibility.
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