Artificial Intelligence in Evaluation

Artificial Intelligence (AI) is transforming the field of evaluation by enhancing efficiency, analytical capacity, and evidence-based decision-making.

AI Adoption at a Glance

• Approximately 78% of organizations worldwide use AI in at least one business function.

• Around 71% regularly use Generative AI.

• More than 90% are either using AI or actively exploring its use.

• AI is increasingly applied to improve productivity, automate routine tasks, support decision-making, and generate insights from large datasets.

Despite this rapid adoption, only 22% of organizations have formal AI policies in place, while 61% report receiving little or no organizational AI training. This highlights a significant gap between AI adoption and the governance, oversight, and capacity-building measures required to ensure its responsible, ethical, and effective use.

 

Use of AI in Evaluation

AI can support evaluators throughout the evaluation cycle - from planning and data collection to analysis, reporting, and learning.

 

Design and Planning

• Develop Terms of Reference (ToRs) in minutes.

• Support the formulation of evaluation questions and indicator selection.

• Assist in developing evaluation frameworks and methodologies.

 

Data Collection

• Support desk reviews by identifying and synthesizing information from secondary sources.

• Automate surveys, interviews, transcription, document reviews, and online information gathering.

• Improve the speed and efficiency of data collection processes.

 

Data Analysis

• Process and analyze large and complex datasets efficiently.

• Automate data cleaning by identifying missing values, duplicates, inconsistencies, and outliers.

• Detect patterns, trends, and anomalies that may be overlooked through manual analysis.

• Analyze qualitative data by coding text, identifying themes, and assessing stakeholder sentiment.

• Support quantitative analysis through statistical testing, predictive modelling, and forecasting.

• Triangulate findings across multiple data sources to strengthen evidence and conclusions.

 

Reporting and Data Visualization

• Generate reports, executive summaries, and presentation materials.

• Create charts, dashboards, and visualizations.

• Tailor findings for different audiences and stakeholders.

• Highlight key trends, insights, and emerging issues.

• Accelerate reporting and communication of evaluation results.

  
  

Benefits of AI in Evaluation

• Increased efficiency through automation of repetitive tasks.

• Enhanced analytical capacity for large and complex datasets.

• Improved timeliness through faster analysis and reporting.

• Reduced costs associated with data collection, analysis, and reporting.

• Better decision-making through predictive insights and scenario modelling.

• Improved learning through rapid synthesis of evidence and lessons learned.

• Greater scalability across multiple projects and programmes.

 

Risks and Challenges

• Bias and Fairness: AI may reinforce biases present in historical data.

• Limited Transparency: Some AI systems operate as 'black boxes,' making results difficult to explain.

• Data Quality Issues: Poor-quality data can lead to inaccurate findings.

• Privacy and Data Protection: Large datasets may expose sensitive information if not properly managed.

• Ethical Concerns: Challenges related to consent, accountability, fairness, and responsible use.

• Over-Reliance on Technology: Excessive dependence on AI may weaken critical human judgment.

• Loss of Context: AI may overlook cultural, political, and social factors that influence evaluation findings.

• Capacity Constraints: Organizations may lack the skills and infrastructure needed to effectively use AI. A global report by KPMG found that 61% of respondents reported having no AI training, indicating a substantial skills and capacity gap.

• Methodological Limitations: AI often identifies correlations rather than causal relationships.

  
  

Recommendations for Responsible AI Use

• Adopt a Human-in-the-Loop Approach: Ensure human oversight of key decisions and interpretations.

• Establish Ethical Guidelines: Promote transparency, accountability, fairness, and privacy.

• Ensure Data Quality: Implement robust quality assurance processes.

• Strengthen Data Protection: Apply anonymization, secure storage, and informed consent practices.

• Promote Transparency: Document AI tools, data sources, assumptions, and limitations.

• Build Capacity: Strengthen evaluator skills in AI, data literacy, and ethics.

• Maintain Methodological Rigor: Use AI to complement not replace established evaluation methods.

• Audit AI Systems Regularly: Monitor accuracy, bias, and performance.

• Prioritize Equity and Inclusion: Minimize bias and protect vulnerable groups.

• Develop AI Governance Policies: Establish clear standards, roles, and accountability mechanisms.

AI has the potential to significantly improve efficiency, quality, and timeliness of evaluations. However, realizing these benefits requires strong governance, ethical safeguards, high-quality data, capacity development, and continuous human oversight. Responsible adoption of AI will be essential to ensuring that evaluation findings remain credible, transparent, and contextually relevant.

References/Source Notes:

Alex Singla Alexander Sukharevsky Lareina Yee Michael Chui Bryce Hall: “The state of AI - How organizations are rewiring to capture value,” McKinsey Global Survey on AI, McKinsey Global Survey on AI, March 2025

Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, et al, “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025