5 Key Takeaways from Session 2: What AI Cannot Replace in Evaluation

The conversation around AI in evaluation often focuses on efficiency, automation, and productivity. But during Session 2 of our GLOCAL Evaluation Week 2026 series, the discussion moved beyond what AI can do and focused on a more important question:

What should remain fundamentally human in evaluation?

Meet the Speakers

• Aditi Chatterjee – Independent Research and MEL Specialist with extensive experience in evaluation, learning, and systems thinking across the development sector.

  
  

• Rahul Shah – Non-Profit Consultant and Organizational Development Expert, working with organizations on strategy, leadership, and institutional effectiveness.

  
  

• Payal Mulchandani – Co-founder & Business Lead, The 4th Wheel; works at the intersection of impact evaluation, impact strategy, and systems change.

  
  

• Sharon Weir – Co-founder & Strategy Lead, The 4th Wheel; specializes in organizational strategy, learning, and evidence-informed decision-making.

  
  

• Aivya Jain – Founders' Office, The 4th Wheel; moderator of the session.

Key Takeaways from the Session

Here are five key takeaways from the discussion.

1. Evaluation Is Not About Data. It Is About Decisions.

AI can summarize documents, identify patterns, and generate insights. However, evaluation is ultimately not about producing information. It is about making decisions.

Determining which findings matter, what trade-offs should be considered, whose voices should be prioritized, and what actions should follow requires human judgment.

"The purpose of evaluation is not analysis. The purpose of evaluation is better decisions."

AI can support decision-making, but it cannot be accountable for those decisions.

2. Lived Experience Cannot Be Replaced by Training Data

One of the most powerful discussions centered around whose voices are represented in AI systems. Most AI models are trained on information that is documented, published, and often available in dominant languages. Yet many of the communities evaluators work with exist outside these datasets.

Whether working with survivors of trafficking, marginalized populations, or vulnerable communities, lived experience often provides insights that never appear in reports, academic papers, or institutional documents.

“People closest to the problem must continue to shape how that problem is understood”

3. Context Is What Makes Evaluation Meaningful

A recurring theme throughout the session was that context cannot be automated.

Programs operate within social, cultural, political, and institutional realities that AI cannot fully understand. An intervention that works in one geography may fail in another. A successful model on paper may be inappropriate in practice.

Field realities, local relationships, power dynamics, and community perspectives continue to require human interpretation and engagement.

“AI can process information.Humans must make sense of it”

4. Validation Is as Important as Analysis

The panel emphasized that collecting and analyzing data is only part of the evaluation process.

The real value comes from validating findings with stakeholders and communities.

Before conclusions become recommendations, evaluators must ask:

• Does this reflect reality?

• Does this interpretation make sense?

• Are we missing something important?

Validation helps ensure that evidence remains grounded in lived experience rather than becoming disconnected through layers of automated analysis.

“Communities should not simply be sources of data. They should be partners in making sense of it”

5. Strong Fundamentals Matter More Than Ever

One of the most interesting conclusions from the session was that the rise of AI actually increases the importance of core evaluation skills.

• Critical thinking.

• Facilitation.

• Ethical reasoning.

• Contextual understanding.

• Interpretation.

• Communication.

The better an evaluator understands these fundamentals, the more effectively they can use AI.

"AI is not going to do a good job for you unless you are good at your job."

Technology can amplify expertise, but it cannot substitute for it.

Final Reflection

Throughout the session, the speakers returned to a common message:

• AI can make evaluation faster.

• It can help organize information.

• It can improve efficiency.

  
  

But evaluation has always been about people, perspectives, and judgment. As AI becomes increasingly integrated into evaluation practice, the challenge is not deciding whether to use it. The challenge is ensuring that human judgment, accountability, and lived experience remain at the center of the decisions we make.

Because while AI can process information, only humans can determine what truly matters. Session 2 reminded us that while AI can accelerate analysis, it cannot replace judgment, context, relationships, or lived experience. If these discussions resonated with you, we invite you to continue exploring the questions shaping the future of evaluation.

Visit the 4th Wheel Blog for reflections on AI in evaluation, systems thinking, impact measurement, organizational learning, Theory of Change, and evidence-informed decision-making. Our articles build on many of the themes discussed during this session and offer practical insights for evaluators, practitioners, funders, and social impact leaders.

Watch the full webinar recording to hear the complete discussion, audience questions, and real-world examples shared by the panelists.

Important Time Stamps

• 04:15 – Why human judgment remains central to evaluation

• 10:32 – Can AI truly understand context and lived experience?

• 18:47 – The risk of excluding marginalized voices from AI-generated insights

• 26:05 – Why validation matters as much as analysis

• 33:12 – Community participation and sense-making in evaluation

• 40:28 – Context, culture, and the limits of pattern recognition

• 49:10 – AI, organizational learning, and decision-making

• 58:36 – Building evaluator competencies in an AI-enabled world

• 1:07:20 – What should remain fundamentally human in evaluation?

• 1:15:45 – Final reflections and audience Q&A

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At 4th Wheel, we believe that the future of evaluation is not about choosing between human intelligence and artificial intelligence. It is about ensuring that technology strengthens, rather than replaces, the critical thinking, empathy, accountability, and contextual understanding that make evaluation meaningful. Because in the end, data can inform decisions. But people must decide what matters.