September 4, 2026
Artificial intelligence is transforming the way Latin American companies use their data. In 2026, AI-powered data analytics is evolving from experimental projects into enterprise applications focused on productivity, automation, insight generation, and decision-making.
The combination of artificial intelligence, data analytics, generative AI, machine learning, Business Intelligence, and intelligent automation is creating a new generation of tools capable of interpreting large volumes of information, detecting patterns, generating queries, and explaining results using natural language.
This report analyzes the state of AI-powered data analytics in Latin America in 2026, the main adoption trends, emerging technologies, and the challenges organizations will need to address to turn their data into a competitive advantage.
The state of Artificial Intelligence in Latin America
The adoption of artificial intelligence in Latin America and the Caribbean continues to accelerate, although the level of maturity varies significantly across countries.
The third edition of the 2025 Latin American Artificial Intelligence Index (ILIA), developed by ECLAC and CENIA, analyzes 19 countries through indicators related to AI infrastructure, talent, research, development, adoption, and governance. The edition published in March 2026 places particular emphasis on effective adoption and human capital as indicators of regional progress.
The results show a heterogeneous region. Chile, Brazil, and Uruguay emerge as pioneer countries, while Colombia, Costa Rica, Ecuador, and the Dominican Republic, among others, are at an intermediate adoption stage. A significant part of the region still has AI ecosystems under development.
However, there is one particularly relevant figure for businesses: Latin America and the Caribbean account for 14% of global visits to artificial intelligence solutions and rank third worldwide in downloads of generative AI.
This demonstrates that interest in AI is already widespread. The next challenge is to turn that interest into business adoption, productivity, and measurable value.
From traditional Business Intelligence to AI-powered analytics
One of the main trends in 2026 is the evolution of traditional Business Intelligence toward AI-powered data analytics.
For years, accessing business information required building dashboards, preparing reports, writing SQL queries, or relying on the data team. New AI analytics platforms are changing this dynamic through conversational interfaces.
With technologies such as Natural Language Query, Text-to-SQL, Natural Language Processing (NLP), Natural Language Generation (NLG), and AI copilots, users can ask questions about their data using everyday language.
Instead of asking “what was the monthly sales growth by region?” through an SQL query, users can ask the question directly and receive an answer based on the available data.
This shift is democratizing access to information and reducing reliance on technical expertise for certain analytical tasks.
The opportunity is not about replacing the data analyst. It is about enabling data analytics, BI, and business users to spend less time on repetitive tasks and more time interpreting information, identifying opportunities, and making decisions.
Generative AI connects with enterprise data
Another key trend is the integration of generative AI with databases, enterprise systems, and analytics platforms.
The report Startups x AI: An Overview of Adoption in Latin America and the Caribbean, published by IDB Lab in May 2026, shows that 85% of the startups analyzed use generative AI and 75% use predictive AI. The study also identifies AI applications in strategic areas such as marketing, product development, and decision-making.
This reflects an important transition: AI is no longer exclusively a tool for generating content and is beginning to be used to analyze information, predict scenarios, automate processes, and support business decisions.
For organizations, this means that a modern data architecture must consider not only storage and visualization, but also how AI models can query, interpret, and use corporate information.
AI Agents are transforming data analytics
In 2026, the conversation is evolving again: from copilots and assistants toward artificial intelligence agents.
While an AI assistant answers a specific question, an agent can execute a sequence of tasks to achieve a goal.
Applied to data analytics, this can mean agents capable of:
- Monitoring business indicators.
- Automatically detecting anomalies.
- Investigating variations in KPIs.
- Querying different sources of information.
- Generating executive reports.
- Explaining changes in sales, costs, or conversion.
- Identifying emerging trends.
- Sending alerts to the responsible teams in each area.
The 2026 Microsoft Work Trend Index, based on a survey of 20,000 AI-using workers across 10 markets and anonymized productivity signals from Microsoft 365, shows how AI and agents are shifting part of work execution toward automated systems, while people can focus more on directing, deciding, and supervising.
The trend points toward an evolution from augmented analytics to agentic analytics, where certain analytical processes can run continuously and autonomously.
Reliable data becomes essential
The growth of AI-powered data analytics is also increasing the importance of data quality and governance.
An AI-generated response can be technically correct while simultaneously relying on incomplete, outdated, or out-of-context data. For this reason, concepts such as data governance, data quality, data lineage, metadata management, data security, and AI governance are taking on a central role.
ILIA itself identifies talent, investment, and governance gaps as some of the region's structural challenges.
This is especially important for Text-to-SQL and conversational analytics systems. Before allowing AI to query enterprise information, organizations need to establish mechanisms to control which data each user can access, validate generated queries, and ensure that responses come from authorized sources.
The new generation of BI, therefore, is not simply about placing a chatbot on top of a database. It requires a combination of AI, data architecture, security, governance, and validation.
The digital divide limits AI's potential
The growth of artificial intelligence in LATAM is occurring alongside significant differences in infrastructure and digital access.
The World Bank estimates that between 30% and 40% of jobs in Latin America and the Caribbean are exposed in some way to generative AI. Between 8% and 12% could experience productivity improvements through its use, but up to 17 million jobs could fail to realize this potential due to digital infrastructure gaps.
The conclusion for businesses is clear: AI adoption requires more than advanced models. It also requires infrastructure, accessible data, specialized talent, and processes capable of incorporating the technology.
The economic opportunity of AI analytics in LATAM
The World Economic Forum identifies artificial intelligence as a strategic opportunity to increase productivity and create new engines of growth in Latin America. Its analysis combines research on the region's structural conditions with a business survey on AI adoption and competitiveness.
In this context, data analytics represents one of the areas with the greatest business potential.
Organizations already have enormous amounts of information coming from CRMs, ERPs, financial systems, e-commerce platforms, mobile applications, contact centers, operations, and digital channels. The challenge is to transform this data into actionable information.
AI enables this process to move closer to the end user.
What comes next for AI-powered data analytics?
The 2026 landscape points toward a transformation of the entire information analytics cycle:
Static dashboards → conversational analytics.
Manual SQL queries → Text-to-SQL and natural language.
Periodic reports → intelligent, real-time monitoring.
Isolated predictive models → augmented analytics.
Copilots → AI agents capable of executing tasks.
Scattered data → integrated data architectures.
Information → actionable insights.
Reactive analysis → continuous business intelligence.
This evolution is also changing the role of data teams. Analysts can use AI to accelerate queries and explorations; developers can automate part of solution development; and business leaders can directly access relevant information without depending on each request to the BI team.
2026: From having data to generating intelligence
The main shift in AI-powered data analytics in Latin America is not solely about technology. It is about the way organizations understand the value of their data.
During the previous stage of digital transformation, many companies focused on collecting information, building data warehouses, and developing dashboards. In 2026, the challenge is to take the next step: make data queryable, interpretable, and actionable through artificial intelligence.
The combination of AI-powered Business Intelligence, conversational analytics, Text-to-SQL, machine learning, generative AI, AI agents, and data governance is creating a new category of tools for business decision-making.
For Latin America, this evolution represents a significant opportunity. Companies that successfully connect their data with AI in a secure and governed way will be able to reduce analysis time, democratize access to information, and respond more quickly to market changes.
The question for 2026 is no longer just “how much data does my company have?”.
It is “how quickly can my company turn that data into decisions?”.
