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Latin America is seeing strong engagement with AI tools, but that does not yet add up to a uniform regional transformation in software engineering or AI analytics. ECLAC estimates that the region accounts for 14% of global visits to AI solutions but just 1.12% of global AI investment, figures reported with its 2025 Latin American Artificial Intelligence Index (ILIA). The contrast points to both real demand and a gap in the capacity to build, fund and embed AI productively.
How is AI transforming Latin America?
The clearest regional signal is growing use of AI solutions alongside sharply uneven readiness and investment. ECLAC and the Latin American Artificial Intelligence Center (CENIA) designed ILIA 2025 to assess 19 countries across more than 100 sub-indicators. Its framework covers enabling conditions, research, development and adoption, and governance. It is a measure of ecosystem conditions—not a count of software engineers, a direct measure of AI analytics productivity, or proof that firms have integrated AI into core operations.
ECLAC estimates, as reported in its ILIA 2025 release, that Latin America and the Caribbean represent 14% of global visits to AI solutions and 11% of the world’s internet users. The same release estimates the region has 6.6% of global GDP but attracts 1.12% of global AI investment. These figures describe different things: visits indicate engagement with AI solutions, while investment indicates capital allocation. Neither by itself shows how deeply businesses use AI in production.
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ECLAC characterizes much current use as concentrated in a small group of countries and oriented toward ready-made, end-user tools with relatively low technical requirements. Access to those tools can support experimentation, but it is distinct from developing local software, building tailored analytics systems, or changing processes in ways that require data infrastructure, specialist skills and ongoing governance.
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Which Latin American countries are leading in AI?
ILIA 2025 groups countries by their ecosystem conditions rather than treating the region as one market. ECLAC identifies Chile, Brazil and Uruguay as pioneers, with scores above 60 points. Eight countries—including Colombia, Ecuador, Costa Rica and the Dominican Republic—are classified as adopters. More than one-third of the countries assessed are explorers.
| ILIA category | What it indicates | Countries identified in ECLAC’s release |
|---|---|---|
| Pioneers | Stronger performance across the index’s enabling, research/development/adoption, and governance dimensions | Chile, Brazil and Uruguay; each scored above 60 points |
| Adopters | Countries developing AI capacity and adoption, but not placed in the pioneer group | Eight countries; named examples include Colombia, Ecuador, Costa Rica and the Dominican Republic |
| Explorers | Countries at an earlier stage across the index dimensions | More than one-third of the countries assessed; the release does not name them all in the summary figures |
These categories are useful for comparing national ecosystems, not for ranking every company or predicting which country will succeed in a particular project. A country can have a promising strategy or visible AI use while still facing constraints in research capacity, connectivity, funding, specialist talent, or execution. ILIA’s annual-report approach is intended to track change over time, but a category alone does not show which constraint is decisive for a specific business.
How much is Brazil investing in IT—and what does that show?
Brazil offers the most concrete software-market scale in the evidence available here, but it should not be treated as representative of every Latin American market. The Brazilian Association of Software Companies (ABES), reporting IDC data in 2025, put Brazil’s 2024 IT investment at US$58.6 billion, 13.9% higher than in 2023. Brazil accounted for 34.7% of the US$169 billion invested in Latin American IT that year.
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| Brazil market measure | Reported value | Scope and source |
|---|---|---|
| Total IT investment | US$58.6 billion in 2024; up 13.9% year over year | IDC data analyzed by ABES and reported in 2025 |
| Software spending | US$18.0 billion in 2024 | Brazil; IDC data reported by ABES in 2025 |
| IT services spending | US$12.7 billion in 2024 | Brazil; IDC data reported by ABES in 2025 |
| Software plus IT services | US$31.0 billion in 2024 | Brazil; IDC data reported by ABES in 2025 |
| Share of Latin American IT investment | 34.7% of US$169 billion | Brazil’s share, based on the regional total reported by ABES |
The spending figures establish market scale; they do not measure software engineering output, workforce size, engineering productivity, or the proportion of companies using AI analytics. ABES also reported forecasts for 2025 of 9.5% growth in Brazilian IT investment and approximately US$2.4 billion in AI projects, 30% above 2024. Those were forecasts published in 2025, not confirmed 2025 results.
Why do AI adoption figures differ?
AI adoption percentages should be read with their source and definition attached. The World Bank’s Digital Progress and Trends Report 2025: Strengthening AI Foundations cites one dataset placing firm AI use at 13% in Brazil and 7% in Colombia. The report separately cites IBM’s Global AI Adoption Survey 2023, in which 47% of firms in Latin America reported actively deploying AI. These figures come from distinct sources and measures, so they should not be combined into a single country comparison or time series.
The World Bank also notes that adoption is concentrated among larger firms and businesses in IT, professional services and financial services. That pattern suggests why a regional or national average may not describe the experience of a smaller firm or a business in another sector. The percentage alone also does not reveal whether AI is being used for a limited task or embedded in an organization’s core workflows.
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What is holding back AI adoption in Latin America?
Specialist skills and implementation capacity
ECLAC reports that advanced AI training remains insufficient and concentrated in a small number of countries. It says the gap in advanced AI talent relative to the global average has widened since 2022 and links the shortfall to specialist brain drain. For organizations, the practical constraint is not only finding people who can use AI tools: scaling a system may also require expertise in data engineering, software development, model evaluation, security and operational change.
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ECLAC reports that many national AI strategies lack financing, implementation mechanisms and systems for evaluating impact. A published strategy is therefore not the same as a funded program, a capable delivery team or a measurable improvement in public services and business productivity. ECLAC also warns against policy that emphasizes regulation at the expense of building the technological ecosystem needed for productivity and well-being.
Responsible use and reliability
The World Bank report identifies inaccurate generative AI output as a major concern in its cited firm survey: 63% of surveyed firms considered it a relevant risk in 2024, up from 56% in 2023, and nearly one-quarter reported negative consequences from inaccuracy. These are survey results for the firms covered by that source, not Latin America-only estimates. For a deployment decision, they underline the need to evaluate outputs and define human review, data handling and accountability before relying on AI in consequential workflows.
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How should leaders compare countries and implementation plans?
A useful comparison goes beyond a headline adoption rate or national AI strategy. For a country assessment, investment decision or internal roadmap, score the conditions that determine whether a pilot can become a reliable operating capability:
- Infrastructure: Check whether connectivity, cloud or compute access, and usable data systems support the intended workload.
- Adoption and use case: Identify which organizations and sectors are using AI, what tasks it supports, and whether use is experimental or integrated into operations.
- Talent: Assess the availability of software engineering, data and advanced AI skills, along with the ability to retain and develop those teams.
- Research and development: Distinguish access to ready-made products from local capacity to adapt, build and maintain software and analytics solutions.
- Governance: Look for practical accountability, evaluation, privacy and risk controls—not just high-level policy statements.
- Funded execution: Confirm that strategies have assigned budgets, responsible institutions, implementation mechanisms and timelines.
- Measurement: Set outcome measures for the project, such as service quality, time saved or error rates, and compare them with a baseline. Do not substitute general AI usage statistics for project results.
This scorecard also helps separate a country’s ecosystem potential from a company’s readiness. A strong national environment cannot compensate for poor data or an undefined business problem, while a capable organization may still face infrastructure or talent constraints in a particular location.
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What the current evidence can—and cannot—establish
The available figures support a clear picture of uneven AI readiness, rising engagement, and a large Brazilian IT market. They do not provide a harmonized, region-wide count of software engineers, a comparable measure of engineering productivity, or a consistent estimate of AI analytics use by sector. Nor do the cited market totals show how much spending went specifically to software engineering or productive AI deployment. Those questions require measures beyond the regional index, investment totals and adoption estimates described above.
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