As artificial intelligence (AI) increasingly holds promise for driving economic growth and solving societal challenges in developing countries, the phrase “Garbage In, Garbage Out” becomes more critical than ever. AI systems depend on the quality of the data they are trained on, and poor or biased data can lead to flawed decision-making, perpetuating inequalities. In the context of developing countries, data may be incomplete, inaccurate, or not representative of diverse populations, which can further marginalize vulnerable communities. I explore the implications of poor data quality on AI applications in areas such as healthcare, education, and agriculture, and emphasizes the need for robust data governance, community engagement, and localized AI models to ensure equitable and effective outcomes.