This blog is a joint reflection from the Evaluation Offices of IFAD, WFP and the GEF.
Early this year, the Evaluation Offices of the International Fund for Agricultural Development (IFAD), the World Food Programme (WFP) and the Global Environment Facility (GEF) came together during the UNEG Evaluation Practice Exchange to share experiences on the use of AI in evaluation practice. Using the World Café method, participants from across the UN engaged in rotating discussions to identify opportunities for AI-driven efficiency gains and actionable strategies for responsible integration centred around three areas: 1). where in the evaluation cycle AI can add the most value in low-resource contexts; 2). skills and tools needed for responsible AI; and 3). Risks, ethics and safeguards.
The following are six takeaway messages emerging from that discussion:
- AI can help evaluators do more with less by shifting effort from mechanics to meaning. It should free human capacity for the work it cannot replace, creating more space for contextual interpretation, ethical judgment, relationship-building, and critical thinking.
- The most exciting use cases are those that extend evaluators’ reach, not replace them. Enthusiasm is strongest where AI extends what evaluators can do, not where it replaces core human, ethical, or relational aspects of evaluation. Participants pointed to practical use cases such as desk reviews, evidence synthesis, identification of evidence gaps, analysis of large evaluation portfolios, geospatial analysis, report drafting, translation, communication products, and support for remote or geographically dispersed evaluations.
- Human insight becomes a defining feature of quality, not an optional add on: In an AI‑enabled environment, a “good evaluation” is timely, evidence‑informed, strategically focused, influential, and deeply human—using AI to sharpen judgment and relevance rather than automate conclusions. This also means validating AI-generated outputs against human judgment using transparent and replicable methods, so that confidence in findings is earned rather than assumed. AI may also help evaluation offices make better commissioning choices by mapping existing evidence, identifying genuine evidence gaps, and avoiding evaluations that duplicate what is already known.
- AI can deepen inequities and weaken evaluation quality if governance and context are missing: Key risks include reproducing structural bias, excluding local voices and languages, and cultural nuance, compromising privacy and data sovereignty, generating weak or unverifiable evidence, and widening gaps in capacity and access across the evaluation ecosystem.
- Equitable AI depends on local relevance, inclusive design, and sustained capacity. To reflect local perspectives, UN agencies should co-design AI-supported approaches with communities, use locally relevant data and context-sensitive prompts, and invest in local and shared capacity so that AI strengthens rather than overrides local knowledge and participation.
- Responsible AI in evaluation requires rights-based governance, transparency, and long-term safeguards. UN agencies can support responsible AI use by embedding ethical principles, strong governance, inclusive practices, and sustained capacity development across the AI lifecycle—from design and procurement to use and oversight. As technologies evolve, governance and learning systems must evolve with them. This includes clear expectations for consultants and service providers regarding disclosure, verification, and responsible AI use.
These messages reflect the practical examples, concerns and priorities raised by participants during the World Café discussions.
For additional resources:
The UNEG Ethical Principles for Harnessing AI in United Nations Evaluations outline five key ethical principles that underscore the responsible integration of AI in UN evaluation processes.