This week I worked on research for programme implementation at the Cherie Blair Foundation for Women. I researched payment and financing options for participants in Egypt, including digital wallets, cards, and emerging payment platforms. Additionally, I tested the Road to Finance course and checked materials for accuracy, timing, and errors. I was proud of seeing how research and data analysis can directly inform the participant experience.

I also made progress on the qualitative AI data project. I looked at differences between AI users and non-users, examined challenges that were not captured by the survey's answer choices, and investigated how trust barriers appears in the qualitative data. I was proud of moving from organizing data toward interpreting what the findings meant.

As I have worked with data across countries, I have become more aware of the danger of generalizing solutions in different context. Payment systems, financial access, and user behavior differ significantly across markets. I could have been more intentional from the beginning about separating country-specific findings from broader patterns, as well as using other control factors when analyzing the data.

The value of determined were particularly important. The work required attention to detail because small errors in a course, dataset, or payment recommendation could affect real participants.

Testing programme materials made the relationship between research and participants feel more tangible. An inaccurate lesson can create a barrier for someone trying to access the programme. This emphasized the notion that research has to be accountable to the people it is intended to serve.

In the future, I will introduce more structured quality-control checks into my work, particularly when outputs will be used directly by participants or partners. I want to not only ask whether something is technically correct, but whether someone will actually use it.