By Eddie Arenas, CEO of Quarks Data
Most companies in Latin America already use artificial intelligence. Almost none make money with it. That is, in one line, the uncomfortable conclusion of the Latin America in the Intelligent Age report published by the World Economic Forum with McKinsey in early 2026.
The numbers are stark. Only 23% of organizations in the region report generating any economic value from AI, and only 6% report significant value creation, defined as an impact greater than 5% on operating results. The rest invest, experiment, launch pilots and do not move the needle. Among small and medium companies, where regional employment is concentrated, 59% report no impact at all.
The easy reading is that LATAM is behind. And it is: according to ECLAC, the region represents about 6% of the global economy but only 1.6% of global AI investment. But that reading hides what matters. The underlying problem is not how much AI is bought. It is that most of it is bought without the foundation that makes it work.
The 6% do not have better models. They have better infrastructure.
Here is the finding almost no one quoted from the report. Companies that capture high value do not differ by using more advanced models. They all have access to the same models. They differ in three concrete things: they integrate AI into their core processes instead of treating it as an isolated experiment, they define explicit financial metrics to measure its impact, and they operate on a scalable data architecture.
Translated: the bottleneck is not the algorithm. It is the data. A frontier language model connected to fragmented, ungoverned data with no business model behind it produces impressive demos and zero impact on EBIT. The same model connected to a reliable, governed data layer that understands the business produces decisions that hold over time.
This explains why so many AI pilots in the region die pretty. The company hires, sees a dazzling demo, deploys, and three months later the system hallucinates, contradicts official reports, or nobody knows if it is making or losing money. AI did not fail. What failed was what was underneath the AI, which was nothing.
Why in Venezuela the problem is even sharper
Venezuela's operational realities amplify the gap. A generic data layer does not model multiple simultaneous exchange rates, informal distribution networks, WhatsApp as a primary commercial channel, or the regulatory surface of SUDEBAN in banking or health authorities in healthcare. An AI trained to assume stable infrastructure, a single jurisdiction and optional compliance simply does not apply.
That is why the idea of buying a global solution and expecting it to work locally is an expensive trap. Value is not in the imported model. It is in modeling the business, in its real context, so the AI can reason over it without inventing. There is a difference between surviving this continent and learning to design from within it. AI that creates value in LATAM is designed from within it, not despite it.
What companies that do capture value do differently
If the report is right and the differentiator is the foundation, not the model, the roadmap becomes clear. Three moves, in order:
First, a governed data layer. Before any AI agent, systems that do not talk to each other today (ERP, POS, clinical systems, banking cores) must be integrated into a single reliable base. Without this, everything else is decoration.
Second, a business ontology. Concepts like important customer, critical SKU or acceptable exposure are not universal. They are specific to each company and each regulatory context. Encoding them in a versioned, governed way is what allows an AI agent to execute instead of hallucinate.
Third, metrics with owners. Every view connects to an action and every metric has a responsible owner. AI that is not measured against EBIT ends up in the 94% that does not capture value.
None of these three steps are glamorous. None make it into a demo. But they are exactly what separates the 6% from the rest.
The conclusion
The WEF and McKinsey report projects that AI could add between 1.1 and 1.7 trillion dollars annually to the Latin American economy, and that around 60% of that value will come from analytical AI applied to operations. That value will not be captured by companies buying the newest tool. It will be captured by those building the foundation that sustains it.
At Quarks Data we build that foundation. We start with Essential, the layer that ingests, governs and models a company's data into a single ontology, and only then deploy AI agents that reason over explicit rules. It is the difference between being in the 94% that experiments and the 6% that executes.
If you want to understand what this looks like in your operation, learn more about our work or about Essential, our foundational layer.