From Evidence to Insight: Using AI to Strengthen Synthesis, Trust, and Learning in Climate Adaptation Evaluation
Webinar | Online
About the Event
As artificial intelligence rapidly transforms how evidence is generated, analyzed, and communicated, the evaluation community faces a critical challenge: how to harness AI’s potential while safeguarding rigor, credibility, and trust.
This session presents insights from a pilot application of AI in synthesis evaluation of forestry interventions across major climate funds, analyzing over 11,000 findings from 191 reports. It demonstrates how AI can expand evaluators’ ability to identify patterns and generate actionable insights at scale.
The session combines practical demonstration, methodological reflection, and interactive discussion, offering participants concrete use cases and a structured framework for responsible AI adoption.
This session presents insights from a pilot application of AI in synthesis evaluation of forestry interventions across major climate funds, analyzing over 11,000 findings from 191 reports. It demonstrates how AI can expand evaluators’ ability to identify patterns and generate actionable insights at scale.
The session combines practical demonstration, methodological reflection, and interactive discussion, offering participants concrete use cases and a structured framework for responsible AI adoption.
Speakers
| Name | Title | Biography |
|---|---|---|
| Vladislav Arnaoudov | AF-TERG Secretariat Coordinator | Vladislav Arnaoudov is AF-TERG Secretariat Coordinator and Senior Evaluator with 21+ years’ experience in climate finance and evaluation. He has led M&E, results management, and policy design at the Green Climate Fund and Deloitte, and has taught carbon finance at KAIST. |
| Anupam Anand | Senior Evaluation Officer, GEF IEO | Anupam Anand, Senior Evaluation Officer at the GEF IEO, has 15+ years’ experience in evaluation and development. He applies mixed methods including GIS, satellite data, and machine learning, and has contributed to 30+ publications on remote sensing and environmental policy. |
| Michael Ward | Senior Sustainability Specialist, CIF | Michael Ward is a Senior Sustainability Specialist with the Climate Investment Funds Evaluation and Learning (E&L) Initiative, with over 25 years of experience in sustainable development, learning, and program evaluation. He currently leads work on the Transformational Change Learning Partnership and contributes to just transitions and strategic evaluations. He has worked across NGOs and international consultancies, and holds degrees from Lund University and Henley Business School. |
| Yeonji Kim | Evaluation Uptake Specialist | Yeonji Kim is an Evaluation Uptake Specialist at GCF IEU, leading strategic communications, knowledge dissemination, and stakeholder engagement. She has over 15 years of experience across GCF, UNFCCC, IFC, and UNESCAP. She has a Master’s in Public Policy from Duke University and a B.A. in International Development from the University of California Los Angeles. |
Moderators
| Name | Title | Biography |
|---|---|---|
| Aneesh Kotru | Evaluation Analyst, AF-TERG | Aneesh is a development professional with experience across IT, manufacturing, and finance, now focused on climate change adaptation and natural resource management. He holds a Master’s in Sustainable Resource Management from the Technical University of Munich, with specializations in climate change and environmental economics. As a consultant, Aneesh works on natural resource management projects, the most recent one being the development of a compendium of best practices in forest management with a focus on the Forest-Water Nexus for policy makers in India. |
Summary
The webinar highlighted the potential of AI to strengthen evidence synthesis, learning, and knowledge use in climate change evaluation. Findings from the joint forestry synthesis demonstrated that AI can efficiently analyze large volumes of evaluation evidence while helping identify trends, gaps, and lessons across climate finance portfolios. The discussion emphasized, however, that AI complements rather than replaces evaluators. Human expertise remains essential for framing questions, validating findings, ensuring methodological rigor, and addressing ethical considerations. Continued collaboration, capacity building, and responsible governance will be critical to realizing AI’s potential while maintaining the credibility and integrity of evaluation practice.
Participants identified several priorities moving forward: continue piloting AI across evaluation functions; finalize shared ethical guidelines and protocols for responsible AI use; strengthen AI literacy and capacity among evaluators; maintain human oversight and validation throughout AI-assisted processes; improve data quality and standardization to support AI-enabled analysis; and continue collaboration among climate funds to share lessons and good practices. The findings also point to the need for stronger attention to sustainability, systems-level change, long-term impact measurement, and the integration of evaluation lessons into future climate finance programming and decision-making.