I have been participating in several working groups where we discuss the impact of artificial intelligence (AI) on organizations and society. And I must confess: the deeper I dive into this subject, the more I realize we are living through a moment of transformation far deeper than it seems at first glance.
In recent
years, technology has evolved at an unprecedented speed. Innovations that once
seemed distant are now knocking on our doors, radically changing how companies
operate and compete. If we once spoke of isolated disruptions, we now live in a
state of continuous disruption, spanning all areas and directly challenging
corporate strategies.
And who is
at the center of this revolution? Artificial intelligence, of course. But not
AI alone. Technologies like autonomous agents, quantum computing, and
neuromorphic computing are reshaping the fabric of organizations and,
consequently, of society itself.
What
strikes me most is that, despite all this, many professionals and companies
have yet to grasp the depth and speed of these changes. And that’s concerning.
It could compromise the sustainability of organizations and even the relevance
of many professionals.
This new
scenario demands more than enthusiasm for innovation. It demands
responsibility, structure, and governance. And this is where AI governance
comes in—as a viable, necessary, and urgent path to ensure that all this
transformation is sustainable, ethical, and strategic.
We are
entering an ecosystem of emerging technologies that, when combined, have the
potential to completely reshape the fabric of society. More than just new
tools, they represent new paradigms.
Much has
been said about generative artificial intelligence, which is undoubtedly a
milestone. But it’s only the doorway. We are witnessing the convergence of
several emerging technologies that together have the power to radically change
how we live, work, and relate to one another.
I want to share some of this reflection with you, starting with understanding what is truly happening around us.
Autonomous
Virtual Agents
Autonomous
virtual agents are not just smarter chatbots. They are systems capable of
understanding objectives, making decisions, and executing tasks without
continuous human supervision.
These
agents are already being tested in financial negotiations, medical diagnostics,
and even customer service learning, interacting, and adapting in real time.
Soon we
will have our own virtual agent capable of performing simple tasks like
receiving our emails, reading, assessing, prioritizing, deciding, and
responding as if it were us even carrying out complex operations, such as
defining products based on actuarial calculations.
Their ability to operate in complex and dynamic environments raises crucial questions about control, responsibility, and social impact.
Autonomous
Robotic Systems
Industrial
automation has adopted a new meaning with autonomous robotic systems, where
robots not only follow instructions, but they also learn from their
environments, correct their paths, collaborate with each other, and make
decisions based on data.
Tesla’s Optimus is an example of this, expected to hit the market by 2026, at the price of a vehicle. In sectors such as logistics, healthcare, agriculture, defense, and space, these systems are replacing human labor in critical tasks raising significant questions about employment, ethics, and safety.
Quantum
Computing
The promise
of quantum computing is simple yet monumental: solving problems that would take
a traditional supercomputer millions of years—in just minutes or seconds.
This could
transform areas like climate modeling, molecular simulations for new drugs,
logistical optimization, and especially cybersecurity.
IBM has
already launched the Q System, a commercial quantum computer. Google has
achieved “quantum supremacy” by performing a calculation on a quantum computer
that would be impossible for classical supercomputers in a reasonable time.
Microsoft is advancing in qubit technology, having developed a new quantum chip
capable of solving large-scale complex problems.
With this power come significant risks, such as the potential to break encryption systems that underpin the modern internet, exposing sensitive data from governments, companies, and citizens.
Neuromorphic
Computing
Although
not new—Misha Mahowald and Carver Mead developed the first silicon retina and
cochlea, as well as the first silicon neurons and synapses in the 80s neuromorphic
computing gain renewed relevance with generative AI.
Inspired by
the functioning of the human brain, neuromorphic computing seeks to create
systems with learning and adaptation capabilities that closely mirror
biological cognition.
This
represents a major leap toward the creation of truly autonomous AI, capable of
reasoning with context, memory, and emotion.
But it also
represents a turning point: how do we regulate machines that think similarly to
us?
Observe
that what makes this moment unique is not the emergence of one disruptive
technology, but the convergence of several.
When
autonomous agents operate using neuromorphic neural networks, supported by
decisions optimized through quantum algorithms, within robotic ecosystems, we
are undoubtedly facing a new form of systemic intelligence—one that, if not
properly governed, could surpass our control, with unpredictable consequences.
In 2024, at
an AI event, a presenter stated that soon we would have three types of agents
operating in companies: human agents, hybrid agents, and android agents.
At the
time, I thought, “This speaker is watching too many sci-fi movies.” But today I
see how mistaken my view of technological evolution was. Not as an excuse but
understand that I’m almost a "time traveler” when I began working half a
century ago, the most advanced technology was a manual typewriter, or a
communication device called the Telex.
But let’s set aside the nostalgia and continue our reflection.
Thus, it's
clear that beyond the fascination with new technological possibilities, there’s
a reality that organizations cannot avoid: the way they operate, protect
themselves, and provide accountability is being profoundly reconfigured—so
rapidly that it’s difficult to process, adapt to, and integrate innovations
into daily operations.
And since
companies are made up of people, all this deeply and continuously affects
individuals’ lives, requiring them to break paradigms constantly. This
contributes significantly to professional burnout and rising depression levels.
Corporate
structures are being redefined, and this doesn’t only affect IT it impacts the
entire operational ecosystem. Every department is being affected, without
exception.
Compliance, risk management, and internal audit—traditionally pillars of corporate governance are directly impacted by this new disruptive ecosystem. Let’s examine:
Compliance
With the
rise of autonomous agents and real-time decision-making systems, ensuring legal
and ethical compliance is no longer a matter of simply “checking processes.” It
now demands continuous monitoring, a deep understanding of the technologies
involved, and the ability to respond to unforeseen events.
What happens, for instance, when an autonomous AI makes a biased or unethical decision? How can we ensure systems comply with regulations that are still being formulated?
Risk
Management
In today’s
landscape of exponential innovation, corporate risk management faces one of its
greatest challenges: anticipating the unpredictable.
Technologies
like autonomous agents, quantum computing, and neuromorphic systems introduce
variables that didn’t exist a few years ago—and often aren’t even recognized as
risks until they’ve already materialized.
The
traditional risk management model—based on static cycles of identification,
analysis, response, and monitoring—was already showing signs of exhaustion and
now must be completely reimagined. It lacks the agility and adaptability to
handle emerging risks that evolve in a matter of weeks, days, or even hours.
Risk is no
longer a possibility—it’s a certainty at some point in the
journey. The real differentiator now is the speed and intelligence of the
response. This demands new organizational capabilities.
Internal
Audit
Internal
audit, long the guardian of compliance and efficiency, must now also serve as
an interpreter of technological complexity.
With
increasingly automated processes and decisions made by autonomous systems,
auditing the "who did what" requires understanding algorithms, data
flows, and machine learning logic.
More than identifying failures, auditing now requires anticipating risks, evaluating efficiency considering new innovations, assessing ethical impacts, and verifying whether digital governance principles are being upheld.
So, the
central question is: How will we, and our organizations deal with all this?
In my view,
there is no single answer. But one thing is certain: the first step involves a
structured approach to effectively manage this technological disruption in a
sustainable way, which we can call artificial intelligence governance.
AI
governance is not just a control strategy, it is a foundational approach to
ensure digital transformation occurs in alignment with corporate, societal, and
ethical interests.
In times of rapid and unpredictable innovation, it serves as the backbone for managing disruption sustainably creating a framework that guides organizations not just to innovate, but to innovate with responsibility and long-term vision.
For our reflection, I believe AI governance must address the following key areas:
Defining
Responsibilities
AI
governance sets clear responsibilities within the organization. Who is
accountable for the ethical and safe use of technology? How do we ensure
automated or AI-assisted decisions follow company guidelines and legal
standards?
Creating an
AI governance committee, for instance with executives from IT, compliance,
legal, and ethics, ensure decisions are made in a coordinated and informed way,
without overwhelming any single department.
Additionally,
governance determines how responsibilities align with strategic objectives.
Every new AI project should be evaluated not only for its innovative potential,
but also for its strategic, ethical, and regulatory impact.
Committing
to AI governance means that, while the organization explores new technological
frontiers, it also maintains control over the consequences of innovation.
Defining
Security Standards
As
technologies advance, security becomes a critical issue—not just in terms of
data protection, but also regarding the integrity of automated decisions and
system reliability.
AI
governance establishes the necessary security standards to protect both
sensitive data and autonomous systems. This involves implementing advanced
cybersecurity mechanisms and protocols to ensure AI makes decisions that are
secure and aligned with the organization’s values.
Preventing
bias, ensuring algorithm transparency, and auditing automated decisions are all
essential governance practices to ensure technologies are not only effective
but also safe and fair.
Operational
Formats and Monitoring
Governance
also defines the operational structure of AI within the organization—creating
frameworks for the development, integration, and management of intelligent
systems.
AI
implementation must be transparent, continuously monitored, and adjusted as
technology evolves.
AI
monitoring systems are essential to ensure that even when systems make
autonomous decisions they remain within established boundaries.
In
addition, AI governance demands ongoing monitoring to detect failures, errors,
or unwanted behavioral changes, ensuring that unexpected risks don’t arise.
This monitoring must be integrated into strategic corporate management, aligning technological innovation with organizational goals and culture, so that AI contributes effectively to sustainable and ethical growth.
Note that
this topic is much broader and far from exhausted here. Organizations through
their Boards of Directors and/or Executive Management must address it quickly,
seriously, and assertively.
They must
take a leading role in structuring robust governance that permeates the entire
organization and this is now urgent and non-negotiable.
I leave you
with a thought provoking question for reflection:
How are you
and your company addressing this issue?
The journey
is just beginning. And despite all the challenges, what truly matters in the
end is that we continue to find meaning, build together, and be happy
with
ethics, awareness, and purpose.
Comments
are welcome! Be happy.
This article was written with the help of human intelligence!
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