During my years working in monitoring, evaluation, and knowledge management, I often encountered the same challenge.
We were surrounded by evidence.
Evaluation reports, interview transcripts, focus group discussions, survey responses, lessons learned, policy documents, workshop notes, and research papers were everywhere. Teams spent months collecting valuable information, yet much of it remained buried in documents that few people had the time to revisit.
I remember working on evaluations where hundreds of pages of qualitative data had to be reviewed manually. Evaluators would spend weeks coding interviews, searching for recurring themes, comparing findings across reports, and identifying lessons that could inform future programming.
The process was important, but it was also slow.
As both an evaluator and a data scientist, I began asking a simple question:
What if we could use artificial intelligence to help us find patterns in this evidence faster, while still preserving the critical role of human judgment?
That question led me to explore Natural Language Processing (NLP), one of the most promising applications of artificial intelligence for evaluation and research.
Most evaluation data is not stored in spreadsheets.
It exists in words.
The stories shared by beneficiaries. The recommendations contained in evaluation reports. The lessons captured after a project ends. The observations documented during field visits.
These sources often contain the richest insights about what works, what does not work, and why.
Yet they are also among the most difficult forms of data to analyze at scale.
When organizations conduct multiple evaluations over several years, valuable knowledge becomes fragmented across hundreds of documents. As a result, lessons learned in one programme may never reach another team facing a similar challenge.
This is not a data collection problem.
It is a knowledge extraction problem.
Natural Language Processing, commonly known as NLP, is a field of artificial intelligence that enables computers to understand and analyze human language.
Many people interact with NLP every day without realizing it. Search engines, language translation tools, voice assistants, and AI chatbots all rely on NLP techniques.
For evaluators and researchers, NLP provides a way to analyze large volumes of text and identify patterns that would be difficult to detect manually.
Importantly, NLP should not be viewed as a replacement for evaluators. Instead, it should be viewed as a tool that augments human expertise.
The evaluator remains responsible for interpretation, validation, and judgment.
The technology simply helps process information more efficiently.
Over the last few years, I have seen growing opportunities for NLP across the evaluation lifecycle.
One common challenge is reviewing dozens or even hundreds of reports to answer a single evaluation question.
Traditionally, this requires extensive manual reading and coding.
NLP can help identify recurring themes, cluster similar findings, and highlight evidence gaps across large collections of documents.
Rather than spending weeks searching for patterns, evaluators can spend more time interpreting what those patterns mean.
Outcome harvesting often involves reviewing reports and documentation to identify evidence of change.
NLP tools can assist by extracting references to outcomes, activities, institutions, locations, and stakeholder groups from large document collections.
This can significantly reduce the time required to identify potential outcomes for validation.
Many surveys include open-ended questions that generate thousands of comments.
In practice, these responses are often underutilized because manually reviewing every comment is resource-intensive.
NLP can group similar responses, identify recurring concerns, and detect emerging themes across large populations.
This provides a richer understanding of stakeholder perspectives while maintaining analytical rigor.
Perhaps the most exciting application lies in organizational learning.
Many institutions possess decades of reports, evaluations, and learning products that are rarely revisited after publication.
Imagine being able to ask:
"What recommendations have emerged across education evaluations in East Africa over the last five years?"
Or:
"What lessons have previous evaluations identified regarding community engagement?"
With NLP-powered knowledge systems, these questions can be answered in minutes rather than days.
As enthusiasm around artificial intelligence continues to grow, it is important to recognize its limitations.
Algorithms can identify patterns.
They cannot fully understand context.
They cannot replace cultural understanding, local knowledge, ethical reasoning, or professional judgment.
Throughout my work with AI and evaluation, I have become increasingly convinced that the future is not about replacing evaluators with machines.
It is about creating better partnerships between humans and technology.
The most effective approach combines machine efficiency with human expertise.
Like any emerging technology, NLP introduces important considerations.
Organizations must address questions related to:
The goal should never be automation for its own sake.
The goal should be better evidence, better learning, and better decision-making.
The evaluation profession is entering a new era.
The volume of information available to decision-makers continues to grow, while expectations for timely and evidence-based decisions continue to increase.
Natural Language Processing offers a practical way to bridge this gap.
By helping evaluators transform large volumes of text into actionable insights, NLP can strengthen learning, improve accountability, and support more informed decision-making.
From my perspective, the question is no longer whether artificial intelligence will influence evaluation practice.
The question is how we can ensure that these technologies are applied responsibly, ethically, and in ways that strengthen—not replace—the human expertise at the heart of evaluation.
Natural Language Processing is not about replacing evaluators. It is about helping organizations transform large volumes of qualitative evidence into actionable insights while preserving the human judgment essential to evaluation, learning, and decision-making.
At Geomira, we believe the future of evaluation lies at the intersection of data, technology, and human insight. Natural Language Processing is one of the tools helping us move closer to that future.
Let's discuss how we can support your decisions with evidence-based, decision-ready intelligence.
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