In Latin America, the role of the Chief Data Officer is evolving rapidly as artificial intelligence accelerates across industries. Technology has taken center stage in corporate strategy and is no longer viewed as a standalone function.

So, what is expected of a modern CDO in Latin America? Nothing less than leading the large-scale adoption of data and AI across such a diverse and volatile region bringing together leadership, talent, and governance while connecting business, applications, data, and AI in a continuous virtuous cycle.

By 2025, nearly 99 out of 100 global companies consider investing in data and AI initiatives a top priority an 82.2% increase from the previous year according to the AI and Data Leadership 2025 report by DataIQ. In other words, investing in this space is no longer optional if the goal is to remain competitive.

Yet despite the strategic urgency, real adoption, the kind that generates measurable value is still far from widespread. The same report reveals that 76.1% of organizations remain in experimentation or limited-production stages with AI, while only 23.9% have achieved AI deployments at scale.

In Latin America, the picture is similar. Artificial intelligence is no longer a future promise; it is a present-day paradox. While 47% of Latin American companies have already integrated AI into their operations, five percentage points above the global average, only 23% report generating economic value from it, and just 6% capture a meaningful impact on profitability. These findings come from the report Latin America in the Intelligent Age, published in January 2026 by the World Economic Forum in collaboration with McKinsey & Company.

The gap is not about intent or budget. It is about leadership.

Closing that gap requires leadership, governance, and talent a triumvirate embodied by the Chief Data Officer (CDO), a role that today often expands into Chief Data & Analytics Officer or incorporates artificial intelligence responsibilities.

The modern CDO does not operate with a single playbook or universal answers. Instead, the role demands the ability to read the context, connect stakeholders, and sustain the innovation cycle over time. In that capability lies much of the region’s potential to turn data into development.

At its core, this is a deeper conversation about who decides which data matters, how it is used, how AI transforms it, and how that process ultimately feeds back into business and society through better decisions.

Data and AI leadership: A role in transition

Data alone is not enough. What truly matters is the ability to align investments, processes, and decisions with business objectives.

The same DataIQ report shows that data leadership roles have grown exponentially over the past decade. While only 12% of organizations had a CDO or equivalent role in 2012, that number has climbed to 84.3% today. In addition, 33.1% of companies have already introduced a Chief Artificial Intelligence Officer (CAIO), and 43.9% believe they should.

Even so, the role continues to face challenges related to clarity and sustainability, with high turnover rates and ongoing ambiguity around its objectives.

The modern CDO agenda: leadership, talent, and governance

1. Leadership: the CDO as an internal political actor

When a CDO fails to scale the data agenda within an organization, the reason is rarely a poorly designed architecture or a model that failed to converge. More often, it comes down to a meeting that never happened, a budget that was never approved, or a business unit that chose to continue managing its data the same way it always has. In Latin America, this pattern occurs more frequently than the market tends to acknowledge.

The CDO role is, at its core, a mandate without its own territory. CDOs do not control the data they must transform, they do not own the infrastructure they rely on, and they rarely manage the budgets that determine what gets built and what gets postponed. Their only real capital is the ability to persuade business units to share what they view as their competitive advantage, persuading IT to relinquish some ownership over the architecture, and persuading the CFO to fund investments whose results may not appear in the next quarter’s financial statements.

This is the political dimension of the role and the one that receives the least training and discussion. It cannot be solved with a governance framework or a data management certification. It is built through difficult conversations, early wins that create credibility, and the ability to make every key stakeholder feel that the data agenda is also their agenda.

2. Governance: accelerating without bureaucracy

AI and data governance remain among the most frequently discussed and least understood concepts within organizations. Governance is still often associated with control, bureaucracy, or regulatory compliance. Yet it can be the very factor that determines whether AI scales or not.

When no one can explain where the data comes from, how models are trained, or who is accountable for their outcomes, initiatives quickly lose both internal and external legitimacy.

In Latin America, where public and private systems coexist in highly heterogeneous environments, data governance becomes even more critical: not only to protect data, but also to enable its productive use across diverse legal and cultural landscapes.

The region’s data governance environment is far from a regulatory vacuum; it operates within a mosaic. Brazil has consolidated its LGPD (Lei Geral de Proteção de Dados Pessoais) with significant updates in 2024, including standard clauses for international data transfers and stricter sanctions. Chile approved a new data protection bill in August 2024 aligned with the European GDPR. Meanwhile, Colombia is advancing reforms to its Law 1581 of 2012 to introduce new legal bases for data processing and the right not to be subject to fully automated decisions. For CDOs, navigating this mosaic becomes a competitive advantage for organizations that can anticipate regulatory change and mobilize the resources needed both to comply with the rules and to unlock new business opportunities.

Effective governance is not built as an abstract framework but as a practice embedded within the business. When governance rules are disconnected from operations, they become irrelevant. When they are integrated into processes, they enable speed. The difference lies in design.

3. Talent: hybrid skills that connect business, applications, data, and AI

One of the biggest barriers to large-scale AI adoption is the lack of strategic talent capable of bridging business and technology. More than 40% of organizations still face significant skills gaps when it comes to implementing AI responsibly. The challenge lies in building teams with hybrid capabilities: business knowledge, data literacy, and the ability to translate insights into executive decisions.

The profile the region needs is not simply a data architect with executive authority. It is an organizational translator; someone capable of converting technical capabilities into business decisions, and cultural resistance into real adoption.

This challenge also has a distinctly regional dimension. CDOs in Latin America operate in markets where specialized talent is scarce and highly competitive, where regulatory frameworks evolve at different speeds depending on the country, and where most organizations including SMEs, lack the mature infrastructure needed to scale data initiatives. In this environment, the most valuable skills are not those learned in certification courses. They are the ones that allow leaders to move forward with what they have at hand, negotiate priorities with limited resources, and build internal coalitions that sustain the data agenda beyond cycles of hype and enthusiasm.

Sustaining the cycle

Today’s environment demands architectures capable of supporting data in real-world conditions often unequal, regulated, fragmented, and constantly evolving.

The challenge for the CDO or whichever role concentrates data and AI leadership is to turn data into a sustained organizational capability that goes beyond individuals, isolated projects, or specific technological breakthroughs.

In this sense, the goals of data leadership are shifting from technical outcomes to structural transformation. Some potential OKRs (Objectives and Key Results) for this new leadership model focus on measuring tangible organizational change:

• Break down fragmented data environments by integrating critical data domains, preventing episodic AI initiatives. Data without context has limited value.
• Improve data quality, especially in a region where public and private data intersect frequently. Representativeness becomes not just a technical concept but a strategic variable.
• Move AI from the lab into operations, reducing dependence on isolated pilots and building reusable capabilities that outlast the initial excitement.
• Establish data-driven decision-making as an organizational norm rather than an exception. When data does not enter executive conversations, AI cannot transform anything.
• Build trust at scale through governance frameworks that accelerate innovation without eroding legitimacy, enabling compliance without paralysis and automation, and without losing accountability. This may be the most complex challenge, but also the one that ensures long-term sustainability.

Data alone does not transform organizations. Transformation happens when someone decides which questions to ask of that data, who can access it, how it is translated into action, and who is accountable for the outcomes. In Latin America, that chain of decisions is still interrupted too often by talent shortages, architecture that cannot be scaled, or organizations that have yet to find the leadership capable of guiding them. The modern CDO is precisely the one who closes that chain not as a gatekeeper of data, but as a catalyst for value across strategy, technology, and business. That is the complexity of the role, and also its greatest value.