In the face of escalating climate risks, the Adaptation Fund sought to strengthen evidence-based decision-making for climate resilience in Colombia’s Mojana region. To achieve this, the Fund launched a cutting-edge platform powered by Retrieval-Augmented Generation (RAG), artificial intelligence, and scalable cloud infrastructure. The solution allows decision-makers to instantly analyze thousands of documents, integrate geospatial and historical data, and identify optimal interventions in real time. The result is a transformative leap in climate governance—one that prioritizes speed, accuracy, and local relevance.

Prior to the development of the AI-driven decision-support platform, the Adaptation Fund faced significant barriers that limited the speed, consistency, and effectiveness of decision-making for climate change mitigation initiatives:

• Fragmented and Dispersed Information: Critical data—ranging from past interventions, scientific studies, and climate models—was scattered across multiple systems, formats, and institutions. This made it difficult to access, correlate, or synthesize insights in a timely manner.

• Manual Document Review: Decision-making processes relied heavily on human-led reviews of large volumes of documentation. This not only consumed considerable time and resources but also introduced subjectivity and a higher likelihood of oversight or inconsistency.

• Limited Analytical Capacity: Without unified, real-time data processing or AI-powered tools, it was challenging to analyze complex, multi-layered problems (e.g., identifying risk factors across environmental, economic, and social domains).

• Slow Response to Emerging Risks: The time required to gather, validate, and interpret data significantly delayed the formulation of mitigation strategies—particularly problematic in climate-sensitive contexts that demand rapid, evidence-based action.

• Lack of Contextual Integration: While local knowledge existed, there were few mechanisms to formally integrate community insights, regional priorities, and historical interventions into structured decision models.

• Inconsistent Decision Frameworks: Without a centralized knowledge system, each decision cycle had to start from scratch, leading to repeated efforts, variability in analytical depth, and missed opportunities for learning from past programs.

These challenges collectively resulted in slower, less scalable, and more reactive decision-making, limiting the Fund’s ability to effectively prioritize resources and interventions in the high-risk region La Mojana, Colombia.

The new platform significantly improved the Fund’s ability to make informed, timely, and context-aware decisions:

• Real-Time Insight Generation: Cognitive agents can process more than 2 million pages of documentation in seconds—180 million times faster than manual review.

• 90% faster knowledge retrieval: AI search reduced project and regulation lookup time from 6 hours to 10–30 minutes.

• Decision Acceleration: Decision-making cycles that once took weeks can now be completed within minutes, with cross-referenced historical context and geospatial analysis.

• Comprehensive Knowledge Use: Local data, expert knowledge, and external sources are now seamlessly integrated to inform multidimensional decisions.

• Clarity and Depth of Insights: Integration with eight external data sources increased the completeness and accuracy of outputs by up to 800%.

• Operational Efficiency: Storage systems were optimized, reducing redundancy and achieving a 17% decrease in data volume while improving usability.