3.2 Methodology The methodology of this study was guided by the vision to map AI startups in African countries with a focus on wide and diverse coverage in both rural and urban areas, and inclusivity in the AI innovation ecosystem dedicated to understanding their status in terms of their funding mechanisms, fundraising journeys, products, traction, challenges and opportunities that will lead to the promotion of excellence in the adoption, development, implementation, and use of AI that will lead to sustainable social-economic development in Africa. The mapping for AI startups in Africa utilized proprietary vocabulary to discover and extract qualified startups from the literature, online resources, and AfriLabs’ enormous datasets of African startups, investors, hubs, corporates, public sectors, and humanitarian and development organizations with the in- tegration of snowballing approach to have wide and diverse coverage of the data. Throughout the process, iterative methods were used in each stage to review and incorporate missing and new required information leading to a comprehensive final report. The following Figure 3 briefly describes the methods applied toward fulfilling the vision of this project;
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Primary Data Collection E ngage African AI stakeholders through the use of key informant interviews (KIIs), Focus Group Discussions (FGCs), case studies, Startups and Hubs Referral.
Secondary Data Collection
Initial Preparation
Conduct thematic desk research, verify data authentically across various AI, publish articles, and tech-focused online sources.
Setup ethics and confidentiality recruiting team, prepre research tool, and identify participants.
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Data Reporting and Dissemination
Report data using tools such as PowerBI and Tableau and Data dissemination to AI-based Afrilabs Gatherings such as Clean Tech Conferences, Africa Tech Summits, TechCrunch Disrupt, and Africa Tech Festival, and also, African and Global AI conferences and journals to mention a few
AI Stakeholders Engage- ment: Conduct in-depth KIIs and FGDs with key stakeholders
Case Studies: Conduct outreach to short- listed AI startups and create case studies that highlight their status, challenges and opportunities
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Data Analysis ans Synthesis
Use snowballing technique to identify existing AI start- ups and its AI stakeholders
Conduct qualitative and quanti- tative analysis using tools such as PowerBI, Python, and DAX to analyse and synthesize the clean collected data
Surveys: Deploy online surveys to AI stakeholders in AfriLabs community and AI stakehold- ers in Africa.
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Iterative Review of Collected Data Follow an iterative strategy to review and refine findings and primary data for final reporting
Data cleaning and Annotation
Webinars: Host identified thought- ful-leaders within AI and techh community in Africa.
Data cleaning and annotation through the use of tools such as Python and Excel.
Figure 3: Description of Methodology for Mapping AI Startups in Africa
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