How Governments Are Using AI
How Governments Are Using AI — and What M&E Professionals Can Learn
From real government AI applications to practical lessons for Monitoring & Evaluation.
The Case
How are governments actually using artificial intelligence?
And what can M&E professionals learn from those applications?
The World Bank study How Is Government Using Artificial Intelligence?
examines how governments are adopting and integrating AI into public administration.
Its AI and Data for Better Governance Survey covered 60 economies and collected
information from central digital agencies, sectoral ministries and management information
system (MIS) leads.
The study looks beyond the question of whether governments have adopted AI.
It examines the data infrastructure, analytical capabilities, institutional capacity
and governance arrangements that surround AI adoption.
The key lesson for M&E:
AI is not simply a technology to be added to an M&E system.
Its value depends on the problem being addressed, the quality of the data,
the capabilities of the people using it, governance arrangements and the
continued role of professional judgment.
The AI Use Cases
The most useful way to understand government AI is to look at
what governments are actually trying to do with it.
The World Bank study identifies different applications across taxation,
procurement, health, education, human resources, public finance and other
government functions. The applications differ, but many follow a similar
logic:
DATA
→
AI ANALYSIS
→
SIGNAL / INSIGHT
→
HUMAN JUDGMENT
→
DECISION
AI for Fraud and Risk Detection
| THE PROBLEM | Tax administrations need to identify taxpayers or invoices that may present a higher risk of fraud. |
| HOW AI IS USED | AI tools can analyze administrative data and flag taxpayers or invoices with a high risk of fraud. |
| HUMAN ROLE | The AI produces a risk signal. Tax officials investigate and apply professional judgment. |
| M&E TRANSLATION | An M&E system could identify projects, indicators, partners or activities that require additional verification. |
AI can become a risk-detection layer within monitoring.
It does not become the evaluator.
AI for Decision Support
| THE PROBLEM | Procurement officers need to understand prices, transactions and supplier patterns in order to support procurement decisions. |
| HOW AI IS USED | Existing administrative data and analytics can be made more accessible through AI-supported decision tools. |
| HUMAN ROLE | Procurement professionals interpret the information and make decisions. |
| M&E TRANSLATION | AI could analyze historical project costs, implementation delays, partner performance, budgets and indicator patterns to identify areas requiring further examination. |
AI for Diagnostic and Decision Support
The World Bank uses health as an example of how AI may support professional
decision-making. AI can help process information and support diagnostic
tasks, particularly where specialist expertise is scarce.
AI cannot compensate for inaccurate, incomplete or disconnected underlying
data.
M&E translation
Imagine an M&E system identifying an unexpected change in an outcome indicator.
AI might identify the pattern quickly.
The evaluator must still ask:
- Is the indicator measuring what we think it is measuring?
- Is the data complete?
- Could there be another explanation?
- What does the program context tell us?
- What additional evidence is required?
Better analytics cannot substitute for better measurement.
AI for Identifying Risk
The World Bank study describes the problem of identifying students who may
be at greater risk of dropping out of school.
This illustrates a broader predictive use case:
using existing data to identify cases that may require additional attention.
M&E translation
An M&E system could potentially use historical and current data to identify:
- projects showing elevated implementation risk;
- sites with unusual performance patterns;
- indicators requiring verification;
- activities showing signs of delay;
- program areas requiring closer monitoring.
A predictive signal should trigger investigation — not automatically determine
the conclusion.
AI for Organizational Analytics
Government HR systems can provide data that helps managers understand
organizational performance and identify areas requiring attention.
M&E translation
Similar approaches could be applied to organizational M&E data:
- staff workload;
- turnover;
- training participation;
- implementation capacity;
- organizational performance;
- patterns across teams or locations.
AI can surface patterns, but managers still need to interpret organizational
and contextual factors before acting.
AI for Communication, Documents and Knowledge
The World Bank survey’s definition of AI includes virtual assistants and
chatbots, document analysis and translation tools, diagnostic or
decision-support tools, risk-scoring, forecasting and optimization models.
The study also identifies GenAI applications such as chatbots in tax
administration.
M&E translation
For M&E teams, generative AI can potentially assist with:
- searching large evidence repositories;
- classifying evaluation reports;
- extracting findings;
- identifying recurring themes;
- drafting evidence summaries;
- comparing findings across evaluations;
- supporting knowledge management.
AI can increase analytical capacity without transferring final
interpretive responsibility from the evaluator.
From Government AI to M&E
The government examples can be translated into familiar M&E processes:
| Government AI use | M&E equivalent | Potential AI contribution |
|---|---|---|
| Tax fraud detection | Risk-based monitoring | Flag cases requiring verification. |
| Audit prioritization | M&E verification | Prioritize limited monitoring resources. |
| Procurement analytics | Performance analysis | Identify unusual historical patterns. |
| Health decision support | Evidence-supported decisions | Surface relevant information. |
| Education risk analysis | Predictive monitoring | Identify emerging risks. |
| HR analytics | Organizational M&E | Identify performance patterns. |
| Document analysis / GenAI | Evidence synthesis | Search, classify, extract and synthesize evidence. |
The Human-AI Interface
Across these use cases, one principle keeps appearing:
AI produces information or signals; people remain responsible
for interpretation and decisions.
DATA
→
AI
→
INSIGHT / SIGNAL
→
HUMAN REVIEW
→
DECISION
What Does an AI-Ready M&E System Require?
01 · DATA
Reliable, accessible and sufficiently documented evidence.
02 · USE CASE
A clearly defined M&E problem where AI may add value.
03 · CAPABILITY
Professionals able to use and challenge AI outputs.
04 · GOVERNANCE
Rules, safeguards, accountability and controlled workflows.
05 · HUMAN JUDGMENT
Context, interpretation, methodology and professional responsibility.
06 · EVALUATION
Measure whether AI actually improves the M&E process.
The Missing Layer: Evaluating AI
The World Bank study reports that monitoring and evaluation of government
AI applications is lacking. More than half of governments in lower-income
economies reported that they do not monitor and evaluate AI performance at all.
Where evaluation exists, the report notes that it often focuses on implementation
and usage statistics rather than more advanced approaches such as cost-benefit
analysis, service-quality assessment and independent audits.
For M&E professionals, this creates an important opportunity:
the people who know how to evaluate programs also need to know how
to evaluate the AI systems supporting those programs.
How Should We Evaluate an AI-Enabled M&E System?
| EFFICIENCY | Did AI reduce time or workload without simply transferring work into validation and correction? |
| QUALITY | Did the quality, consistency and traceability of evidence improve? |
| ACCURACY | How frequently does the system produce errors or unsupported conclusions? |
| RISK | What new risks emerge, including bias, hallucinations, confidentiality or misuse? |
| DECISION USEFULNESS | Does AI-supported evidence actually improve decision-making? |
| HUMAN OVERSIGHT | Do M&E professionals understand, challenge and appropriately validate AI outputs? |
You Are the M&E Manager
Your organization has 600 evaluation reports,
approximately 20 years of historical evidence, limited
analytical capacity and increasing demand for rapid insights.
Leadership asks:
Where can AI add value — and where must professional judgment remain central?
Decision 1 — Where should AI enter the M&E process?
Your team spends substantial time reviewing hundreds of evaluation reports.
What is the most appropriate starting point?
A. Use AI to classify, search and extract information from reports.
B. Ask AI to conduct evaluations automatically.
C. Keep the process completely manual.
D. Give everyone unrestricted access to AI and allow staff to decide how to use it.
A strong starting point is generally a clearly defined, repetitive analytical
task where AI can assist while human reviewers retain responsibility for
interpretation.
Decision 2 — What happens before AI analyzes the evidence?
The reports were produced over many years using different methodologies
and formats.
Should you first establish document and data-quality procedures?
AI does not eliminate the need for evidence management. Poorly structured,
incomplete or inconsistent source material can undermine AI-supported analysis.
Decision 3 — What remains human-led?
The AI produces a thematic synthesis of the evidence.
Which activities should remain under professional responsibility?
- methodological judgment;
- contextual interpretation;
- triangulation;
- assessment of evidence limitations;
- final conclusions.
Decision 4 — How should AI use be governed?
Team members have already started using public AI tools.
An AI-ready M&E organization should establish clear rules concerning
approved uses, data protection, confidentiality, accountability,
human oversight and validation.
Decision 5 — How should AI outputs be validated?
AI produces a surprising finding that could influence a major program decision.
The finding should be traced back to the source evidence and examined for
data quality, methodological limitations, alternative explanations and context.
Decision 6 — How will you know whether AI is working?
Do not evaluate AI only by the number of users or prompts.
Evaluate whether it changes the M&E process in meaningful ways.
- time saved;
- analytical quality;
- accuracy;
- errors;
- bias;
- cost;
- decision usefulness;
- user capability and confidence.
Apply This to Your Organization
Ask your M&E team:
The Bigger Lesson for M&E
Do not ask only: “How can we use AI in M&E?”
Ask: “What would it take to make our M&E system AI-ready?”
Related EvalCommunity Cases
Source & Methodology
Primary source:
World Bank,
How Is Government Using Artificial Intelligence?,
background paper for the World Development Report 2026.
The AI and Data for Better Governance Survey covered 60 economies.
The survey examined AI adoption, data infrastructure, analytical capabilities,
institutional readiness and governance across government.
Data were collected between December 2025 and May 2026. The study uses
purposive expert-informant sampling and relies on government-reported
information. The World Bank notes that the study is cross-sectional and
therefore does not establish causal effects.
Confidentiality:
This case study is intended for professional learning and educational use.
Do not enter confidential, personal or otherwise sensitive organizational
information into public AI systems. Examples and scenarios are designed
for learning and should not be interpreted as organizational, legal,
methodological or policy advice.
AI in Monitoring & Evaluation
