Artificial intelligence is not here to do the same things better. It is here to challenge the very logic on which those processes were designed. It is no longer about improving how we work, but about questioning whether the way we work still makes sense. It is not enough to add AI to what already exists. The challenge is to rebuild those processes entirely.
This shift is already visible across multiple industries. In banking, tasks that once required entire teams—such as credit risk analysis—can now be completed in minutes using models that process thousands of variables simultaneously. In legal departments, contract reviews that used to take hours or days are now automated with systems that detect inconsistencies, risks, and opportunities for improvement in seconds. In marketing, entire campaigns—from segmentation to content generation and real-time optimization—can be designed and executed with the assistance of artificial intelligence.
But the most interesting point is not automation itself—it is what comes next. When those tasks stop being bottlenecks, a new question emerges: what do we do with that freed-up time, capacity, and knowledge?
That is where artificial intelligence stops being a tool for efficiency and becomes a new frontier for creation. The creation of new products, new experiences, and new ways of interacting with customers and markets.
Increasingly, work is being organized around a different logic: humans collaborating with AI agents. Not as support tools, but as true partners in problem-solving. Teams that were once structured by function are now evolving into hybrid systems, where part of the work is done by people and part by models that analyze, propose, and execute.
As a result, some roles as we know them today will become obsolete. At the same time, entirely new roles are emerging—ones that would have been unthinkable just a few years ago. From prompt designers to AI process architects, to professionals capable of coordinating multiple intelligent agents within a single operation.
This is not a linear replacement. It is a transformation of work.
From copilots to intelligent agents
In this context, what is happening is closer to a transition than an evolution. A migration from processes designed for an analog world to those built for a digital, dynamic, and continuously learning environment.
Many companies are undergoing a deep and silent transformation: moving from using AI tools individually—so-called copilots—to integrating intelligent agents into business processes. The difference is substantial. A copilot improves an individual’s productivity. An agent redesigns how an entire process works.
This can already be seen, for example, in customer experience, where AI has moved from being an isolated chatbot to becoming a system that supports operators, suggests responses, prioritizes cases, and anticipates needs. Or in software development, where automation not only accelerates code writing but also testing, error detection, and continuous product improvement. Even in automated testing, where systems learn from patterns and adjust test cases in real time.
In many cases, learning does not come from perfect implementations, but from well-leveraged mistakes. For example, a service company decided to fully automate its customer support using artificial intelligence. The initial result was a drop in satisfaction levels: the responses were correct but lacked context, empathy, and judgment. The solution was not to roll back, but to redesign the process. AI shifted from replacing the operator to assisting them—suggesting responses, anticipating issues, and accelerating resolution. The impact changed completely. It was not replacement, it was amplification.
Rethinking the business through the lens of AI
Argentina holds a unique position in this landscape. On one hand, it has globally competitive talent and a knowledge-based industry that exports billions of dollars annually. Argentine professionals are involved in developing artificial intelligence solutions used worldwide.
However, internally, adoption is slower. Many companies are still in early stages, using AI to improve individual productivity—writing faster, analyzing data more efficiently, automating specific tasks—or, at best, to optimize existing processes.
There are still few cases where organizations are taking the most challenging step: rebuilding their business from the logic of artificial intelligence.
This gap between capability and adoption is both a risk and an opportunity. A risk, because entire sectors may continue operating with models that quickly become outdated. And an opportunity, because it enables significant leaps in productivity and competitiveness if this transformation can be accelerated.
Ultimately, the conversation around artificial intelligence is no longer about whether it should be adopted. That stage is over. The real question is different: which companies will use it to do what they already do a little better—and which will dare to redesign and build something entirely new.
Because in this new phase, the advantage is not in having access to technology, but in understanding how to use it to push the boundaries of what is possible.
By Ángel Pérez Puletti, CEO of Baufest.
