IFAD used AI to organise, search and triangulate a very large evidence base
EvalCommunity Academy · Case Study
Generative AI in Large-Scale Evaluation: Inside IFAD’s Corporate-Level Evaluation of IFAD11 and IFAD12
How the Independent Office of Evaluation of IFAD used AI to organise, search and triangulate a very large evidence base, while keeping evaluators in charge of every finding.
Organisation
IOE, IFAD
Evaluation type
Corporate-level
Launched
2024
Focus
AI-assisted analysis
What you will learn
- Where generative AI adds real value in a complex, multi-source evaluation
- How to design AI workflows around evaluation questions rather than around the tool
- Which safeguards protect rigour, privacy and traceability
- What AI could not do, and where expert judgement remained essential
The evidence base at a glance
35
country strategy and programme evaluations
62
country strategic opportunities programmes
350+
interviews across 10 country case studies
90+
key informant interviews with Management, stakeholders and Board
486
e-survey respondents
11
prior evaluations synthesised (9 corporate/thematic, 2 MOPAN)
1. Context
In 2024, the Independent Office of Evaluation of IFAD (IOE) began a corporate-level evaluation of IFAD’s institutional and operational performance across two replenishment cycles, IFAD11 and IFAD12. The evaluation looked at core parts of IFAD’s business model: its financial architecture, its operations and its management of human resources.
Evidence came from many streams at once: headquarters interviews, country case studies, earlier corporate and thematic evaluations, country strategy and programme evaluations, and a steadily growing body of strategic and operational reports. IOE introduced generative AI into the process, in line with its own AI Strategy and IFAD’s data governance policy, to help the team structure this material and analyse it efficiently.
2. The challenge: scale without losing rigour
The core problem was volume. Alongside the documents and interviews counted above, the team also ran portfolio analysis, thematic deep dives and an impact assessment. Reading, coding and cross-referencing all of it manually would have consumed weeks of evaluator time and made consistent treatment across countries and themes difficult.
Design principle: AI was never meant to replace evaluator judgement. It was used to organise qualitative data, locate evidence quickly, apply a consistent analytical approach across sources, and make every finding traceable back to where it came from.
3. The approach: three AI workflows
Interview processing and a searchable chatbot
Audio recordings were transcribed and turned into structured text. Key points, timestamped quotations and stakeholder attributions were mapped to each sub-evaluation question.
The team also built a chatbot on anonymised interview minutes. Evaluators asked questions in plain language and got answers grounded in the data, with quotes and links to the source transcripts. The same method was applied to country case study interviews with governments, IFAD staff, project units, donors and private sector actors.
Document classification
AI sorted paragraphs from public IFAD documents (country evaluations and strategies, MOPAN assessments, country strategy notes, Board papers) into predefined categories.
For non-lending activities alone, more than 95 reports were screened across 33 sub-dimensions, with accuracy generally between 80% and 95%. Each paragraph was tagged by country, year and document type and linked to its source, so evidence could be filtered and compared. The same method covered procurement, disbursement, timeliness, budget management and transformational change.
Triangulation across sources
With interviews, case studies and documents all structured the same way, the team could compare evidence across streams and check that each finding was supported by several sources before it entered the analysis.
4. Safeguards that protected credibility
IOE treated every AI output as an analytical input, never as a finding. The safeguards included:
Human review of all outputs, checked against the original transcripts and documents before use
Anonymisation of all data before processing, with analysis run in secure environments
Spot checks against human coding, using standard accuracy metrics
Documented prompts and coding rules, so the process can be audited and repeated
Full traceability from every output back to its original source
Ethical alignment with IOE policies and the UNEG Ethical Principles for Harnessing AI in UN Evaluations
5. Results and limitations
What worked
- Time: document screening that used to take weeks was done in days, and locating specific evidence became much faster
- Consistency: similar evidence was treated the same way across countries and themes
- Breadth: the team could work with a far wider evidence base while keeping the analysis structured
- Reuse: the workflows can be applied to future evaluations
What did not
- Complex concepts: ideas such as transformational change or policy engagement needed contextual, expert interpretation
- Transcription errors: specialised terminology and accents caused mistakes that had to be corrected by hand
- Scanned documents: required manual fixes
- Hidden costs: prompt design took iteration, and validation (spot checks and cross-checks) took real time
6. Three lessons for evaluators
Start from the questions, not the tool. AI is most useful inside workflows built around evaluation questions, with evaluators firmly in control.
Validate at every stage. Human checking is needed from model selection through coding to synthesis.
Document everything. Recording prompts, coding rules and decision protocols makes the work transparent, replicable and useful for institutional learning.
Questions for reflection
- Classification accuracy here ranged from 80% to 95%. What accuracy threshold would you accept in your own evaluation, and how would you decide?
- Which parts of your current evaluation work are high-volume and rule-based enough to benefit from AI, and which depend on context and judgement?
- How would you explain the role of AI in your methodology section so that commissioners and stakeholders trust the findings?
- What data privacy steps would your organisation require before interview data could be processed by an AI tool?
Key takeaway
Generative AI is not a silver bullet. In this evaluation it worked as a practical assistant that made analysis faster, more consistent and more traceable, because human judgement and rigorous validation stayed at the centre of the process.
Source: Independent Office of Evaluation of IFAD, “Beyond the Hype: How IOE Used Generative AI for the Corporate-Level Evaluation of IFAD’s Replenishment Commitments (IFAD11 and IFAD12)”, 30 April 2026. Case study adapted by EvalCommunity Academy for learning purposes.
