Evaluation, Evidence and Trust in the Age of AI
- Sharon Weir
- 2 days ago
- 4 min read
Over the past three sessions during Glocal Evaluation Week 2026, we explored how AI is changing evaluation practice, where human judgment remains essential, and what organizations need to do to move from isolated AI tools to meaningful AI-enabled systems.
The conversations brought together evaluators, researchers, technologists, nonprofit practitioners, and organizational leaders. Across all three sessions, one message stood out above all others: AI can help us process information faster. It can identify patterns. It can summarize evidence. It can improve efficiency.
But evaluation is ultimately a human endeavor. It is about understanding people, contexts, relationships, values, and choices.
Here are ten things that stood out for us:
Table of Contents
1. AI Is Already Part of Evaluation Practice
The question is no longer whether evaluators will use AI. Most evaluators are already using AI to:
Review literature
Develop frameworks
Draft tools
Translate questionnaires
Digitize surveys
Clean datasets
Analyse qualitative data
Generate reports
In many cases, AI is quietly becoming part of everyday workflows. The real question is whether we are using it intentionally, critically, and responsibly.
2. AI Reduces Processing Time. It Does Not Replace Thinking
Across the discussions, evaluators repeatedly shared how AI has reduced time spent on repetitive tasks. What previously took days can often be completed in hours. However, evaluation is not simply information processing.
Evaluators still need to:
Frame the right questions
Select appropriate frameworks
Assess methodological rigor
Validate assumptions
Interpret findings
AI can generate possibilities. Human beings must decide which possibilities matter.
3. Good Prompting Does Not Replace Subject Expertise
One misconception is that AI can replace years of thematic knowledge.
It cannot. An evaluator working on education, health, livelihoods, gender, migration, agriculture, or workforce development still needs deep domain expertise.
AI may suggest frameworks. It may recommend indicators. It may identify patterns.
But it cannot determine whether those frameworks make sense in a particular political, cultural, economic, or community context. The evaluator remains responsible for judgment.
4. The Greatest Risk Is Not Wrong Answers. It Is False Confidence
One of the most important warnings raised during the sessions was that AI often produces outputs that sound convincing.
Beautiful dashboards.
Polished reports.
Confident summaries.
Clear recommendations.
Yet none of these guarantee that the underlying data is complete, representative, unbiased, or accurate. AI can create an illusion of certainty. Good evaluators remain skeptical.
5. Participation Matters More Than Ever
As AI becomes more powerful, participation becomes more important, not less.
Many communities remain underrepresented in the data that AI learns from.
Their experiences may:
Exist only in local languages
Never appear in published literature
Be poorly documented
Contradict dominant narratives
If evaluation relies only on AI-generated synthesis, important voices may disappear. Communities should not only provide data. They should help interpret it.
6. Evaluation Is Not About Generating Insights
Evaluation is not about generating insights. Evaluation is about making decisions.
Should we continue?
Should we scale?
Should we redesign?
Should we invest?
Should we stop?
AI can help us synthesize evidence. It cannot decide what should happen next.
Those decisions require values, priorities, relationships, trade-offs, and judgment.
7. Human Judgment Is Not Just Expert Judgment
When we talk about human judgment, we often imagine consultants, evaluators, researchers, or technical specialists. But human judgment is much broader than that.
It includes:
Community members
Frontline workers
Program teams
Subject experts
Leaders
People with lived experience
The people closest to the issue often provide the interpretation that data alone cannot reveal. Good evaluation systems create space for these voices.
8. Organizations Should Focus on Problems Before Tools
One of the strongest messages from technology leaders during the series was simple:
Do not start with AI. Start with the problem.
Organizations often ask: "What AI tool should we use?"
A better question is: "What problem are we trying to solve?"
Only after understanding the problem should organizations explore whether AI is an appropriate solution. Otherwise, AI becomes another technology searching for a purpose.
You May Also Read: 5 Key Takeaways from Session 2: What AI Cannot Replace in Evaluation
9. Moving from AI Tools to AI Systems Requires Governance
Many organizations are experimenting with AI. Far fewer are building AI-enabled systems.
Moving from tools to systems requires:
Connected data
Clear governance
Data privacy protocols
Quality assurance
Validation mechanisms
Ethical safeguards
Trust does not come from AI. Trust comes from the systems surrounding AI.
You May Also Read: 5 Key Takeaways from Session 3: From Tools to Systems – Rethinking AI Use in Evaluation
10. The Future Evaluator Will Spend Less Time Producing Evidence and More Time Interpreting It
The role of evaluators is changing. Tasks such as coding, transcription, translation, cleaning, and summarization are increasingly being automated. This does not reduce the importance of evaluators. It changes where they create value.
Future evaluators will spend more time:
Facilitating conversations
Interpreting evidence
Designing learning systems
Building stakeholder consensus
Supporting decision-making
Ensuring ethical use of evidence
In many ways, evaluation may become more human, not less.
Watch the Full Glocal Evaluation Week 2026 Series