AI in Climate Evaluations: The Prudent Path from Hype to High-Impact

The major climate funds from the GCF to the Adaptation Fund face a foundational challenge: the tension between rigor and speed. While their meticulous evaluation processes are the global standard for ensuring accountability, they are also perceived as slow, creating bottlenecks that delay vital capital.

For years, this was an accepted trade-off. Today, it is no longer tenable. The scale of the need is accelerating, (especially for SIDS and LDCs) and it is commendable that funds like the GCF are responding by revamping internal processes to make screening workflows far more efficient.

However, as a recent scoping study, commissioned by the GCF, GEF, AF, and CIF, highlights, Artificial Intelligence (AI) offers Funds an opportunity to take efficiency initiatives to a much higher level, making evaluations more efficient and standardized, while maintaining the needed rigor.

The Top 5 Most Compelling Findings:

1. AI Adoption is Accelerating: The study concludes that the “majority of researchers and practitioners… believe that the application of AI tools in evaluations is inevitable” and that the “speed of adoption is expected to increase at an accelerating rate.”
2. AI Can Match Manual Quality: The study cites an experiment (Sabarre, N. R. et al.) where one AI tool produced thematic coding results that were “remarkably similar to manual coding in a fraction of the time.”
3. The True Impact Will Come from Tailored Applications: The study finds that AI’s “full impact will likely come from tailored applications built on top of pre-trained LLMs…which are fine-tuned on relevant documentation.”
4. Two-Thirds of Evaluation Tasks Impacted: Researchers estimate that “as many as two-thirds of evaluation tasks could be affected by LLMs in the next five years.”
5. Failure to Engage Introduces Risks: The study highlights that the evaluation community must “proactively engage with these technologies to avoid the risk of becoming less relevant” as other data-focused professions take on evaluation work.

Empowerment Not Replacement:

The scoping study is clear that AI is not a replacement for human expertise and that the goal is not automation, but augmentation. The funds can provide their evaluators with AI tools that do the heavy lifting—instantly cross-referencing a proposal against every investment criterion, sector guide, and fund policy. This frees human experts to do what only they can: analyze strategic fit, judge project viability, and assess true, long-term impact. This approach makes reviews more precise, perfectly standardized, and dramatically faster.

Though no interviews or studies have been done on potential efficiency gains, we can get a sense of the possibilities by reviewing what other industries are achieving with AI. According to surveys conducted by Harvey AI, lawyers using its platform report efficiency gains of 60–90% on routine tasks. The company also calculates an average time savings of 13–25 hours per month for the typical user, while the most engaged users see even greater gains.

The wins don’t stop there. Standardization and precision gains are where an AI-augmented approach truly excels. Currently, ensuring standardization is a significant challenge; the quality and depth of a review can vary based on an individual expert’s workload, experience, or familiarity with a specific sector guide. It is extraordinarily difficult for any human to manually cross-reference every proposal against the fund’s entire, evolving body of policies.

A fine-tuned AI tool, however, applies the exact same “pedantic” scrutiny (as noted in the study’s research) to every submission, creating a perfectly standardized baseline primed for the next higher level of scrutiny.

Enabling a Seamless Transition:

The natural next question is how. The study itself highlights the “breathtaking pace” of AI, where findings are of “temporary value” and limitations “may become… obsolete soon.” For institutions that are not specialist tech companies, attempting to build these complex tools in-house is a high-risk, high-cost distraction.

The study’s own findings point to the most prudent path forward. It concludes that AI’s full impact will likely come from “tailored applications built on top of pre-trained LLMs” which are “fine-tuned on relevant documentation” and integrated into existing evaluation tools. (According to a recent MIT study, Enterprises that attempt to do it on their own fail at a 95% rate).

This is the solution. It is not about using generic, public-facing AI. It is about securely collaborating with specialized, domain-expert partners who have already built these fine-tuned tools. This low-risk, high-impact approach allows the funds to adopt this “inevitable” technology (as the study concludes) rapidly and safely.