NSQ 2 digital - Flipbook - Page 14


Data, AI, and Compliance
What every CEO must redesign in 2026
By Alberto Ponce
For years, corporate digitalization was
largely understood as a technological
eﬃciency exercise. Automating processes,
integrating systems, reducing manual tasks.
Artiﬁcial intelligence appeared to be a natural
extension of that same path: a tool to improve
productivity and accelerate operational
decisions. That perspective is beginning to fall
short.
The rapid expansion of artiﬁcial intelligence
within organizations is transforming
something deeper than technological
productivity. It is forcing companies to rethink
how they govern data, supervise digital
processes, and ensure regulatory compliance
across their operations.
AI adoption is no longer marginal. Various
business studies indicate that more than half
of global organizations already use artiﬁcial
intelligence in at least one operational
function, and more than 70 percent plan to
integrate it into multiple processes in the
coming years. This expansion is visible across
very diﬀerent areas: ﬁnancial analysis,
customer service, document automation,
logistics, and software development.
The size of the market also reﬂects the scale
of the transformation. Recent estimates place
the global artiﬁcial intelligence market above
$300 billion when platforms, technological
infrastructure, and associated services are
taken into account.
Yet the growth of AI is revealing a less visible
r e a l i t y. C o m p a n i e s a r e n o t s i m p l y
incorporating new technological tools; they
are building new operational dependencies
based on data and algorithms. And that
introduces risks many organizations have not
fully measured.
Artiﬁcial intelligence operates on large
volumes of information. The greater the level
of automation, the greater the dependence on
the quality, security, and governance of that
data. As digital systems begin to inﬂuence
operational decisions—from ﬁnancial
processes to risk assessments—the
management of information stops being
purely technical. It becomes strategic. In this
context, the digitalization of work forces
companies to redesign several elements
within their operating structures.
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Digital Edition
MARCH 2026
One of them is data governance. Companies
must clearly establish who controls
information, how it is processed, and which
protocols guarantee its integrity. In distributed
operations—especially when teams operate
across multiple countries—data ﬂows can
quickly become complex and vulnerable
without a clearly deﬁned control architecture.
Another element is regulatory compliance.
Rules governing data protection, privacy, and
the responsible use of artiﬁcial intelligence are
evolving rapidly across multiple jurisdictions.
For organizations operating internationally,
this means technological design must
address not only operational eﬃciency but
also legal compliance.
Technological auditing is also becoming
increasingly important. As business decisions
begin to rely on algorithms and AI models,
organizations need mechanisms capable of
reviewing how those systems operate.
Transparency regarding how data is
processed—and how certain operational
decisions are generated—is becoming an
expectation for both regulators and clients.
Data, artiﬁcial intelligence, and compliance
are becoming increasingly diﬃcult to
separate.
AI requires reliable data in order to function
properly. Data requires governance and
security to avoid vulnerabilities. And both
elements must operate within regulatory
“This relationship is beginning
to reshape how companies
make strategic decisions. ”
frameworks that are becoming more
demanding over time.
This relationship is beginning to reshape
how companies make strategic decisions.
A few years ago, artiﬁcial intelligence was
primarily used as an experimental tool within
speciﬁc areas. Data management remained
largely in the hands of technical teams,
regulatory risk was perceived as limited, and
technological auditing was rare. Business
impact was measured mainly in terms of
productivity.
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