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The first in a series on integrating artificial intelligence into the research process.
AI has become one of those words that’s everywhere, a buzzword in boardrooms, a curiosity in most conversations, professional or social, and increasingly, a quiet presence in how work actually gets done. According to Google’s Our Life with AI Report, 48% people globally now use AI at work at least a few times a year, with writing and editing tools among the most common applications. Among content professionals, the numbers are even higher: over 70% use AI for outlining and ideation, and more than half use it to draft content.
The adoption curve is real. But so is the uncertainty. In Stack Overflow’s 2025 developer survey, 84% of respondents use or plan to use AI tools, yet 46% say they don’t trust the accuracy of the output. People are using AI. They’re just not sure how much to believe it.
For researchers, this tension is especially acute. Our work demands rigor. It requires accuracy, nuance, and accountability, qualities that don’t pair naturally with tools known for confident-sounding hallucinations. And yet the potential is hard to ignore: faster questionnaire development, smarter quality assurance, analysis at scales that weren’t previously practical.
So where does that leave us? Adoption. For all the attention it receives, much of the conversation remains polarized. On one end is hype: claims that AI will “replace research as we know it.” On the other is skepticism: a belief that AI is fundamentally incompatible with rigorous, ethical, human-centered inquiry.
The reality sits somewhere in between.
As our CEO, Nicholas Becker wrote in this article, AI is not changing why research is conducted. It is changing how it is conducted, and in doing so, it is forcing the research community to revisit long-held assumptions about quality, speed, scale, and responsibility.
This post and the series that follows aim to fill that gap. We will share what we have learned about where AI genuinely adds value in research, where it falls short, and how to think about integration in ways that strengthen rather than complicate your work.
The Current Landscape
AI adoption in research is uneven, and for understandable reasons.
Some organizations, such as GeoPoll, are experimenting aggressively and automating significant portions of their analysis workflows. Others are watching and waiting, uncertain whether the tools are mature enough to trust with work that demands rigor.
Both positions are reasonable. The gap between what AI can do in controlled demonstrations and what it reliably does under field conditions is real. A tool that performs impressively on clean, English-language data may struggle with the realities of multilingual surveys, low-connectivity environments, or the cultural nuance required to interpret responses from communities the model has never encountered.
This is particularly true for research in emerging markets and complex settings, exactly the contexts where good data is most needed and hardest to collect. The assumptions baked into many AI tools often reflect their training environments: high-resource languages, stable infrastructure, Western cultural frameworks. When those assumptions don’t hold, performance degrades in ways that aren’t always obvious.
None of this means AI isn’t useful. It means we need to be specific about where it works, honest about where it doesn’t, and thoughtful about how we integrate it.
Where AI Genuinely Adds Value
Let’s start with what’s working. These are applications where the technology is mature enough to deliver consistent value, and where we have seen real improvements in efficiency, quality, or both.
1. Research Design and Problem Definition
Early-stage research design has always been one of the most human-dependent phases of the process. Defining the right question, aligning objectives, and translating abstract goals into measurable constructs requires judgment, domain knowledge, and contextual awareness.
AI can support this stage by synthesizing large volumes of background material, identifying recurring themes across prior studies and stress-testing logic, assumptions and consistency in objectives.
This is one of the very few places where GeoPoll uses synthetic data – to simulate real-world possibilities and tighten the research design.
However, AI cannot determine what matters. It can help refine how a question is phrased, but it cannot decide whether the question is meaningful, relevant, or appropriate for a given context. That responsibility remains firmly human.
2. Questionnaire Development and Translation
In relation to the research design above, AI has also become a genuine accelerator in the early stages of instrument design. AI can generate initial question drafts, identify ambiguous phrasing, suggest alternative wording, and flag potential sources of bias. They are particularly useful for cognitive pretesting, helping you anticipate how respondents might misinterpret questions before you’re in the field.
Translation and back-translation workflows have also improved significantly. While human review remains essential, AI can produce working drafts faster and more consistently than traditional approaches, freeing skilled translators to focus on nuance rather than first passes.
This has been particularly useful to us as we conduct several multicountry and multilingual surveys. Using thousands of our past translated questionnaires, we have trained our own models to produce translations that are close to fine, which makes the work a lot easier and more efficient for our translation teams to only review.
3. Quality Assurance and Data Cleaning
Quality control is where AI’s pattern recognition capabilities shine. Real-time monitoring during data collection can flag anomalies. Interviews completed suspiciously fast, response patterns that suggest straightlining or satisficing, geographic inconsistencies, or interviewer behaviors that warrant review.
The value here isn’t replacing human judgment but directing it more efficiently. Instead of reviewing random samples, quality teams can focus attention where it’s most needed. Fraud detection, in particular, has become significantly more sophisticated with machine learning approaches that identify coordinated fabrication patterns humans might miss.
4. Analysis and Insight Generation
Anyone who has manually coded thousands of open-ended responses understands the appeal of automation. Natural language processing, again, with well-trained models such as the one GeoPoll Senselytic uses, can now handle initial coding, theme extraction, and sentiment analysis at scale. Work that previously consumed enormous time and introduced its own inconsistencies.
The keyword is “initial.” AI-generated codes require human review, and the categories need refinement based on contextual understanding the model might lack. But as a first pass that analysts then validate and adjust, the efficiency gains are substantial. Also, analysis is not insight. AI can surface patterns, but it may not fully understand causality, significance, or implication in the way decision-makers require. Without human interpretation, there is a real risk of over-fitting narratives to statistically convenient patterns.
Then feed the results back into the model and continuously improve its capabilities for next time.
5. Reporting, Visualization, and Storytelling
Beyond analysis, AI streamlines the communication of findings: drafting report sections, generating visualization options, summarizing results for different audiences, and adapting technical findings into plain narratives.
For organizations producing high volumes of research, this represents significant time savings. First drafts that once took days can be generated in hours, freeing researchers to focus on refinement, interpretation, and strategic recommendations.
6. Operational Efficiency
Beyond the research process itself, AI streamlines the operational work that surrounds it: drafting reports, cleaning and restructuring data, generating documentation, and summarizing findings for different audiences. These applications are less glamorous but often deliver the most immediate time savings.
But Human Judgment Remains Essential
Listing AI’s capabilities without acknowledging its limitations would be both incomplete and misleading. There are aspects of research where human judgment isn’t just preferable, it’s irreplaceable.
1. The Foundation
Deciding to conduct research does not begin at the research design stage. It starts with a real problem an organization needs to solve. AI can help refine questions, but it can’t tell you which questions matter. The strategic decisions that shape a study – what to measure, why it matters, how findings will be used – require understanding of context, stakeholders, and objectives that models don’t possess. This is where research value is created or lost, and it remains fundamentally human work.
2. Contextual Interpretation
Data doesn’t interpret itself. Understanding what a response pattern means requires knowledge of local context – political dynamics, cultural norms, recent events, historical relationships – that AI tools lack. A model might identify that responses in a particular region differ from the national average; understanding why they differ, and what that implies for the research question, requires human insight.
This is especially critical in cross-cultural research, where the same words can carry different meanings, and where what’s left unsaid is often as important as what’s captured in the data.
3. Ethical Judgment
Research involves ongoing ethical decisions: how to handle sensitive disclosures, when informed consent requires additional explanation, how to protect vulnerable respondents, whether certain questions should be asked at all in particular contexts. These judgments require moral reasoning, empathy, and accountability that can’t be delegated to algorithms.
4. Stakeholder Relationships
Research happens within relationships – with communities, partners, clients, and institutions. Building trust, navigating sensitive topics, communicating findings in ways that lead to action rather than defensiveness: these are human skills that no AI will replicate. The credibility of research ultimately rests on the people behind it.
5. Final Analytical Decisions
AI can surface patterns and generate hypotheses, but the final interpretive decisions – what the data means, how confident we should be, what recommendations follow – belong to researchers. The stakes of getting this wrong are too high, and the accountability too important, to outsource.
The Integration Question
Based on all this, the question isn’t whether to use AI but how to integrate it without breaking what already works.
The most sustainable approach treats AI as an augmentation rather than a replacement. The goal isn’t to automate researchers out of the process but to free them from tasks where their judgment adds less value, so they can focus where it adds more. AI handles the volume while humans handle the judgment.
This requires what’s often called “human-in-the-loop” workflows: processes designed so that AI outputs are reviewed, validated, and refined by people before they influence decisions. It’s slower than full automation, but it’s also more reliable and more accountable.
It also requires building internal capacity. Organizations that outsource AI entirely to vendors risk losing understanding of how their research is actually being conducted. The teams that will use AI most effectively are those that understand it well enough to know when it’s helping and when it’s not.
In our work at GeoPoll, we see AI as a tool that strengthens research when it is embedded thoughtfully, not when it is layered on top as a shortcut. The most effective applications combine automation with clear methodological guardrails and continuous human oversight.
What This Series Will Cover
This article sets the foundation for a deeper exploration of AI across the research lifecycle. In the coming pieces, we will go into each stage in detail, looking closely at what works, what doesn’t, and what responsible use looks like in practice:
- Research design and questionnaire development: From hypothesis to instrument
- Sampling and recruitment: Reaching the right respondents
- Data collection: Fieldwork in the age of AI
- Quality assurance: Detection, monitoring, and validation
- Analysis and interpretation: From data to insight
- Reporting and visualization: Communicating findings effectively
- Ethics and limitations: What AI can’t do, and why it matters
Each post will be practical and specific, drawing on real-world applications and our experience rather than theoretical possibilities.
GeoPoll’s Perspective
At GeoPoll, we have spent over a decade conducting research in some of the world’s most challenging environments—conflict zones, low-connectivity regions, rapidly evolving political contexts. We complete millions of interviews annually across more than 100 countries, in dozens of languages, using mobile-first methodologies designed for conditions where traditional approaches don’t work.
That experience has shaped how we think about and work with AI. We have seen what works when assumptions break down, when infrastructure isn’t reliable, and when the cultural context is unfamiliar to the models. We have learned through iteration, testing tools in the field, finding their limits, and building workflows that account for them. As a technology research company, we have built AI platforms and processes into our research and are actively employing AI to make our work easier and deliver greater value to our clients and partners.
This is the knowledge we are sharing in this series.
If you are thinking about how AI might strengthen your research, we would welcome the conversation. Contact us to discuss what’s working, what’s not, and where the opportunities might be.