Overview
In a strategic effort to democratize artificial intelligence and promote regional innovation, Colombia’s Ministry of Information and Communications Technologies (MinTIC) launched the Microcenters of AI program. These Microcenters are designed to bridge the digital gap by fostering applied research, localized innovation, and inclusive development in sectors such as education, health, and the environment. Powered by advanced AWS cloud services and machine learning technologies, the initiative is reshaping how Colombia applies AI to real-world problems.
Challenge
MinTIC faced multiple national-level challenges prior to the launch of the Microcenters:
- Low Interoperability and Open Data Adoption: There was a limited supply of high-quality, secure, and accessible open data—essential for building AI-driven solutions that are both inclusive and trustworthy.
- Regional Gaps in Digital Infrastructure: Many territories lacked the technological foundation and human capital required to develop or benefit from AI-based solutions.
- Fragmented Collaboration: Cross-sector collaboration was minimal, reducing the ability to co-create solutions with shared value.
- Insufficient Technical Capacity: Local professionals had limited access to the tools, training, and platforms necessary to develop or deploy AI responsibly.
- Regional Inequity in AI Advancement: Colombia ranked below its potential in the Latin American AI Index, driven by disparities in education, innovation, and access to digital tools.
Solution
To address these barriers, MinTIC created the Microcenters of AI program, virtual and physical hubs designed to accelerate AI adoption across the country’s regions.
Key solution components included:
- AI Virtual Microcenters: Collaborative platforms that bring together public, private, academic, and civil society actors to co-create applied AI projects in key sectors.
- Cloud-Based Data Processing and Analytics: Leveraging Amazon Athena, AWS Glue, and Amazon SageMaker, the Microcenters process large volumes of sectoral data to develop predictive models for education, health, and climate resilience.
- Predictive Modeling with Machine Learning: Solutions are developed using Amazon SageMaker to forecast educational dropouts, food safety risks, disease outbreaks, and climate events.
- Visual Decision Tools: Geovisualization and dashboards, powered by Amazon QuickSight, enable stakeholders to map vulnerabilities and act preventively.
- Data Governance and Security: The platforms ensure responsible AI development with secure, compliant, and interoperable data environments.
Impact
The initiative has driven meaningful transformation across multiple public sectors through the application of artificial intelligence and data-driven decision-making.
- Improved Educational Continuity: Predictive models are enabling earlier identification of students at risk of dropping out, helping institutions design more personalized and effective support strategies to improve retention.
- Enhanced Public Health Oversight: Health authorities are now equipped with tools to detect potential sanitary risks in food establishments early, enabling more timely inspections and preventive interventions.
- Better Climate Preparedness: With access to climate forecasting models, local governments can anticipate and prepare for extreme weather events, reducing the impact on communities and infrastructure.
- Proactive Animal Health Monitoring: Advanced geovisualization tools allow authorities to map and assess sanitary vulnerabilities, supporting better planning and control measures to prevent disease outbreaks.
- Accelerated Digital Inclusion and Innovation: The initiative is fostering a more inclusive digital transformation by empowering local actors to participate in the design and deployment of AI solutions, strengthening Colombia’s innovation capacity and competitiveness.
Key Data Points
5–10%
estimated reduction in projected school dropout rates
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10–15%
projected improvement in food safety through risk identification and early action
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Forecasting models
deployed to anticipate environmental events (e.g., floods, droughts)
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