From legacy to future: Bringing order to enable innovation
Across many Latin American companies, the enthusiasm to “do something with Artificial Intelligence” coexists with a fragmented technology foundation—legacy systems, incomplete integrations, scattered data, and teams operating at different levels of digital maturity.
This is not an isolated perception. According to a Gartner report, fewer than half of organizations have sufficiently mature data management processes before launching AI projects, increasing investment risk.
The primary challenge, then, is not implementing AI itself, but preparing the conditions that allow AI to deliver real value. In Latin America, where legacy environments coexist with modern platforms, this preparation requires putting the legacy landscape in order. In line with the clean core approach, the goal is to maintain a stable, standardized system core, avoiding direct modifications while moving customizations and extensions to external layers.
Achieving this requires defining data models, cleansing and consolidating sources, modernizing architectures, and establishing governance processes that reduce inconsistencies.
This is where the CIO’s role takes center stage—evolving from a predominantly technical function into a strategic vision that transforms data into a reliable, scalable, business-oriented infrastructure.
A continuous feedback loop
Innovation can be understood as a virtuous cycle in which data enables AI, AI transforms data, and both reshape processes and decision models. When well managed, this transformation enhances competitiveness, improves services, and generates tangible business value.
Within this cycle, the CIO becomes the orchestrator—driving a data strategy that bridges both the technical and the strategic components. This means establishing a comprehensive roadmap that guides how data is collected, managed, stored, and analyzed to support business objectives. To succeed, today’s CIO must translate AI’s potential into decisions, align investments with business goals, set priorities, and mitigate risks.
The true value of the CIO in the AI era lies precisely in the ability to connect decisions, data, processes, culture, and strategy.
Risks and challenges: From promise to reality
Latin America’s reality demands a tailored approach. The region combines large enterprises with SMEs that sustain employment, informal economies, deep cultural and geographic diversity, and highly specific legal frameworks. From extended supply chains to regulatory differences, from labor informality to cultural diversity, each country presents unique conditions that impact digital maturity.
CIOs must redesign their function around collaborative models that unite governance, strategy, and outcomes—ensuring data is consistent, interoperable, and usable by AI models scaling across the business. In this context, AI stops being merely an IT tool and becomes a cross-enterprise asset that reshapes responsibilities, processes, and expectations.
In practice, the role is about creating the necessary conditions for every business area to evolve toward smarter, measurable, data-driven models. The notion of simply “delivering technology” is long behind us.
The new role
For transformation to take place, the CIO must operate collaboratively. Their mission includes redefining responsibilities across key innovation stakeholders—acting as a strategic integrator who reshapes how CFOs, COOs, and CEOs approach AI adoption, automation, and data integration.
Driving these standardized, harmonized practices is essential to consolidate dispersed information and generate a shared, trusted view that supports both advanced analytics and new technological capabilities.
Likewise, fragmented information remains a recurring challenge for CFOs and finance teams, often preventing a single, unified view of the business. The ability to project future scenarios and make decisions based on trusted data is critical. Implementing data governance models enables consistent cross-functional analysis across commercial, financial, and operational areas. This was the case at Natura in Brazil, where an end-to-end data flow model was established to ensure cross-functional coherence.
At the same time, CIOs must resist the temptation to simply “deploy” AI without rethinking processes—especially in Latin America, where supply chains can be complex and geographically dispersed. End-to-end operational automation and full process visibility are essential for COOs seeking to map critical workflows with multidisciplinary teams.
It is also important to remember that AI adoption depends as much on talent as on technology. This co-creation is a direct area of impact for the CIO, enabling the development of use cases aligned with real business needs—reducing time to adoption and increasing system trust. In this context, hybrid roles that connect operations and technology act as powerful accelerators of new-tool adoption.
Expectation alignment is another critical factor CIOs should be aware of. The introduction of new technologies can create tension, particularly in organizations operating with mixed analog-digital structures. Without shared indicators—such as processing time, error reduction, or satisfaction improvements—progress may be interpreted differently across the business, as seen, for example, at the Brazilian company São Salvador Alimentos. Establishing a common measurement framework is essential to communicate value consistently across the enterprise.
A forward-looking perspective
Artificial Intelligence is no longer a promise—and Latin America has already proven it is ready. Global results reinforce this momentum: according to SAP’s latest financial report, SAP Business AI was included in two-thirds of cloud orders in the fourth quarter, alongside sustained AI adoption across the ERP suite. The conversation looking ahead is how to integrate AI into companies in ways that generate real business value.
In our region, this challenge comes with a unique advantage: the coexistence of legacy structures and emerging dynamics. And precisely there lies the opportunity—there is no rigid model to be bound by. We can choose how to build alongside AI.
The next step is more demanding: scale with consistency, sustain governance, and turn AI into a structural—not situational—advantage. Now is the time to step forward and bring that ambition to every organization. In this journey, the CIO’s mission is clear: ensure that investment becomes strategy, strategy becomes execution across every function, and technology becomes embedded in the culture of the business.
