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Antes de que llegara cualquier política corporativa. Antes de cualquier comité de transformación digital. Antes de la primera reunión con IT.
Your employees have already started.
They are using ChatGPT or Gemini to write emails, summarize contracts, and prepare presentations. They ask a Claude agent to organize their week, analyze data, and anticipate objections in a negotiation.
Not as an experiment.
As part of their day, every day.
And the most interesting part is not that they are doing it.
It is that probably no one at the top of your organization knows for sure.
McKinsey documented it with numbers in its Superagency in the Workplace report: employees are three times more likely than their leaders expect to be using generative AI in at least 30% of their daily work.
Three times.
That is not a perception gap.
It is a management abyss.
The phenomenon nobody is governing
There is a term gaining traction across security and technology departments in large corporations: Shadow AI.
AI in the shadows.
Organizations are unaware of 89% of AI use inside their companies, despite having active security policies.
In other words: your teams already have their own artificial intelligence infrastructure.
Parallel.
Decentralized.
Without architecture, without governance, without integration into the real systems of the business.
This is not the employees’ fault.
It is a clear signal that internal demand has far exceeded the institution’s speed of response.
47% of C-suite leaders believe their companies are moving too slowly in AI development because of leadership misalignment and lack of talent.
When leadership knows it and still does not move fast enough, people below find their own way.
And in Latin America, that pattern is amplified.
The Latin American paradox: interest without urgency
Our region has a peculiar relationship with technology.
We are voracious consumers of digital innovation at a personal level — Latin America accounts for 14% of global visits to AI solutions and ranks third in the world in generative AI app downloads — but that energy rarely translates with the same speed inside organizations.
The ILIA 2025 report, published by ECLAC together with Chile’s National Center for Artificial Intelligence, summarizes it with a phrase that should make any leader in the region uncomfortable:
“Countries show great interest, but no sense of urgency. Despite overwhelming evidence of AI’s positive impact on productivity, employment, and quality of life, no country exceeds the global average in AI investment relative to GDP per capita, and the regional average remains six times below that threshold.”
Six times.
That is not a minor figure.
More than 60% of Latin American organizations identify AI talent and training as their main need, and more than 40% cite lack of technical expertise as a major barrier to adoption.
These are real gaps, with names and consequences.
But there is something else that is also real:
Human talent did not wait for the company to solve those gaps.
Talent found its own shortcuts.
And, when led properly, that is exactly the starting point many companies need.
What is an autonomous agent, really?
Let’s forget the technical definition for a moment.
An AI agent is a system that can receive an objective, make decisions to achieve it, and execute tasks without a human having to supervise every step.
It does not answer questions.
It acts.
It plans.
It chains actions together.
Gartner estimates that by 2028, 40% of enterprise applications will be integrated with task-specific AI agents, and that one-third of user experiences will shift toward agentic interfaces, driving new business models.
But perhaps the most decisive data point for leaders in customer service or collections operations is this:
Gartner predicts that by 2029, AI agents will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operating costs.
This is not a distant projection.
It is the working horizon for anyone currently in an operational leadership role.
The problem is not the technology. It is orchestration.
Here is the trap many organizations fall into:
They confuse having AI tools with having an intelligent operation.
According to Gartner, more than 40% of agentic AI projects will be canceled before the end of 2027, mainly because of escalating costs, unclear business value, or inadequate risk controls.
Not because the technology fails.
Because no one defined what it was for before implementing it.
The companies capturing real value with AI are not simply adding technology to existing work.
They are redesigning workflows, decision points, and task responsibilities from the ground up.
That is the difference between transformation and digital makeup.
And that requires something no technology provider can install:
Strategic judgment.
A system-wide view.
Leadership.
What a C-level leader should understand today, not in two years
This is not about launching an AI initiative.
It is about understanding what is already happening inside your operation and consciously deciding how to lead it.
Three concrete questions to start:
What are my teams already using on their own?
Mapping this informally reveals more about the real appetite for adoption than any internal survey.
What you find will probably surprise you — in a good way.
Where are there processes that could already be assisted by agents, but are still 100% manual?
Collections, customer service, sales management, conversation analysis: there are entire workflows today that depend on people performing repetitive tasks that an agent can execute with greater consistency, speed, and scale.
What is my position as a leader toward this?
Not to stop it.
To give it direction.
The difference between chaos and real transformation lies in whether someone takes the reins with a clear vision, not just an approved budget.
As McKinsey summarizes it: “Achieving superagency in the workplace is not simply about mastering the technology. It is equally about supporting people, creating processes, and managing governance.”
Exactly.
Autonomous agents are not the future you need to prepare for.
They are the present you need to organize.
Your organization already has AI operating inside it.
The question is not whether to adopt it.
The question is who will lead that adoption with intention: the business, with a clear strategy, or each employee on their own, using whatever tools they can find online.
The leader who understands this first will not necessarily be the one with the most technology.
It will be the one who best knows how to connect what is already happening inside the organization with a vision of where they want to take it.
That is the real work of transformation.
And it starts with a simple question:
What is your people doing with AI today, while you read this?
