Many research and programme teams in Uganda are asking the same question: should we be using AI, and if so, how? Some are already experimenting. Others are worried about getting it wrong, or about sending sensitive data somewhere it should not go.
Both instincts are reasonable. AI can save a great deal of time on some data tasks. On others it adds risk without much benefit. This article sets out how we think about it, from our work on research data and on FieldPulse, our automated data-quality service.
Where AI and automation genuinely help
Catching problems while data is still being collected
The most valuable use we have found is not glamorous: checking incoming survey data automatically, every day. Software can scan each day’s interviews for outliers, missing answers, inconsistencies, duplicates and unusual enumerator patterns far faster than a person can. FieldPulse runs these checks nightly on data from SurveyCTO, ODK, KoboToolbox or Google Forms and delivers a report before the next morning’s fieldwork, while problems can still be fixed.
Much of this is classic statistics rather than “AI” in the popular sense. The point is the same: let the machine do the repetitive checking, and let people decide what to do about it.
Making sense of open-ended answers
Open-ended questions often produce the most interesting data and the least analysed. Language models can suggest codes or themes for hundreds of free-text responses, which a researcher then reviews, corrects and finalises. Used this way, AI speeds up the first pass without replacing judgement.
Drafting, transcribing and translating
AI tools can produce first drafts of transcripts, translations and summaries. For widely spoken languages the results can be useful. For many Ugandan languages they are still unreliable, so human checking is essential, especially for anything that will be quoted or used in analysis.
Supporting decisions
Dashboards that combine several data sources and highlight what has changed can help managers focus on the right questions. The goal is to support a decision, not to make it.
Where to be careful
Personal data
Research data often contains names, phone numbers, locations and sensitive answers. Under Uganda’s Data Protection and Privacy Act, 2019, organisations are responsible for how that data is processed, and research ethics approvals usually limit who may see it.
Before using any AI tool, ask: where will this data be sent, who can access it, and is it kept or used for training? If you cannot answer clearly, do not upload identifiable data. Remove names and contact details first, or use tools that run within your own controlled environment.
Confident mistakes
AI models can produce answers that sound right and are wrong. They can invent references, misread a table or mistranslate a key word. Anything that will inform a decision or appear in a report needs a person to check it.
Hidden bias
Models trained mostly on data from elsewhere may not reflect local language, culture or context. A sentiment score or a translation that works well for one group may be misleading for another. Test on your own data before relying on the output.
Using AI where a simpler method works
Sometimes a clear rule, a well-designed form or a basic statistical check does the job better and more transparently. If a simpler method works, use it.
Four questions before you start
- What problem are we solving? Be specific: time saved, errors caught, decisions improved.
- What data does it need, and is it allowed to go there? Check consent, ethics approvals and the law.
- Who checks the output? Name the person and the standard they check against.
- How will we know it worked? Compare against your current process on a small test first.
A practical path
The organisations that get the most from AI start small: one well-defined task, tested on real data, with a person reviewing the results. They write down what they did so others can check it. And they build their staff’s understanding alongside the tools.
That is the approach we take in our artificial intelligence services, from automated quality monitoring to AI adoption and training for teams. If you would like to explore where AI could help your work, and where it should not, get in touch.