Policy research is evolving — and Evergreen Labs is at the forefront of that transformation.

In our latest case study, we explored how AI can meaningfully enhance complex, large-scale research without replacing human expertise. The project focused on one of the most challenging policy analyses we’ve undertaken: evaluating the implementation of the Philippines’ Extended Producer Responsibility (EPR) law using more than 150 datasets from government documents, media reports, stakeholder interviews, and on-ground surveys.

The Challenge: Scale, Complexity, and Data Imbalance

Traditional research methods are powerful, but time-intensive. We faced issues common in policy work:

• Vast amounts of inconsistent and outdated data
• Geographic imbalance between urban and rural datasets
• Manual coding and cross-checking that can take months
• Significant risk of human bias or fatigue affecting analysis
  

Rather than push harder with traditional tools, we asked a different question: What if we augmented our team’s capabilities with AI — not to replace analysts, but to unlock deeper insights?

The Breakthrough: A Hybrid Research Method

Our team built a structured AI-enhanced methodology that combined:

1. Human-framed hypotheses based on government policy goals
2. Thematic segmentation of 150+ datasets to avoid AI “information overload”
3. Iterative AI analysis using consistent prompts and validation steps
4. Cross-validation between AI findings and human interpretation
5. Narrative synthesis linking qualitative and quantitative insights
   

This approach revealed patterns and contradictions that would have been nearly impossible to detect with conventional manual methods alone.

What AI Helped Us Achieve

📌 30–40% Faster Analysis

AI took on time-consuming tasks like anomaly detection, thematic grouping, and initial coding — drastically reducing processing time while increasing consistency.

📌 Deeper Qualitative Insights

AI identified unexpected themes in stakeholder interviews and media reports, surfacing perspectives that manual coding would likely miss.

📌 Stronger Narrative Integration

It connected numerical data with qualitative feedback, making it easier to identify where policy intention and on-ground experience diverged.

📌 Enhanced Reliability Through Cross-Validation

AI findings were systematically checked against multiple datasets, reducing both human bias and analytical blind spots.

Key Insight: AI Is a Partner, Not a Replacement

The research confirmed an important truth:

AI doesn’t remove the need for human expertise — it amplifies it.

The strongest insights came from the collaboration between human judgment, contextual understanding, and AI’s ability to process vast data at speed.

Successful integration depended on:

• Structured frameworks
• Clear hypotheses
• Skilled prompt engineering
• Human interpretation and verification
  

Why This Matters for Policy Research

This case study offers a roadmap for research teams exploring AI integration:

• AI can manage volume; humans provide meaning
• Breaking data into smaller thematic groups produces better results
• Cross-checking AI outputs ensures rigor
• Well-designed hybrid workflows outperform traditional methods alone

The future of research isn't human vs. AI — it's human + AI working together to produce smarter, faster, more reliable analysis.

Read the Full Case Study

For the full methodology, insights, and analytical framework, explore the complete case study here:
https://drive.google.com/file/d/1zJmr3BE9u7WqyWeOw0zTORUd-uGeGtpY/view?usp=sharing