AI has been a buzzword these days, and almost everything is becoming supported by AI. From business operations to communication and decision-making, AI is entering many parts of our lives. But for Market Systems Development (MSD) programs, the key question is not about the hype, it is about how AI can be used in a practical way to improve how market systems function for disadvantaged communities.
MSD is not about introducing new technology for the sake of it. It is about identifying constraints in the market system and supporting actors to solve them in a sustainable and scalable way. So, when we talk about AI in the market systems programs, we need to ask: where are the real system gaps, and how can AI help to address them?
Employment system
In the employment system, one of the major constraints is the lack of reliable labor market information and weak connections between job seekers, employers, and training providers. Many young people struggle to find jobs not only because opportunities are limited, but also because information does not flow well across the system. AI can help improve this by analyzing data from different sources and making it easier to match job seekers with relevant opportunities based on their skills and potential. At the same time, it can help employers identify suitable candidates more efficiently and support training providers to better understand what skills are in demand. In Ethiopia, this can be done by strengthening existing job matching platforms and integrating AI into services that already use call centers or SMS, so that even those without smartphones can benefit.
Financial Inclusion System
In the financial inclusion system, the main challenge is that many small and micro businesses are excluded from formal financial services due to lack of collateral and formal records. From a market systems perspective, this is a systemic issue that limits growth and job creation. AI can support financial service providers to better understand and serve these businesses by using alternative data, such as transaction patterns or business activity, to assess creditworthiness. This creates an opportunity to expand access to finance, especially for underserved groups. However, the role of MSD programs is not to build these systems directly, but to facilitate partnerships between financial institutions, fintech companies, and other actors so that these solutions are developed and sustained by the market. At the same time, attention must be given to risks such as data privacy and potential bias, ensuring that AI does not unintentionally exclude certain groups.
Agricultural System
In the agriculture system, which remains central to Ethiopia’s economy, one of the key constraints is the lack of timely and reliable information for farmers and other actors. Farmers often make decisions without accurate knowledge of weather conditions, pest risks, or market prices, which affects productivity and income. AI can help address this by providing more accurate and localized information that supports better decision-making. It can also help other actors in the system, such as traders and service providers, to better plan and respond to market needs. From an MSD perspective, the focus should be on strengthening existing agricultural advisory and information systems by integrating AI into them, rather than creating entirely new solutions. This also requires ensuring that the information reaches farmers in accessible ways, such as through basic mobile phones or local intermediaries.
Applying AI in Market Systems Development programs requires a practical and context-driven approach. The focus should remain on strengthening how systems work, improving relationships between actors, and ensuring that solutions are inclusive and sustainable. AI should be seen as a tool that can support these objectives, not as an end in itself. When applied carefully and in alignment with market realities, it has the potential to improve efficiency, expand access, and create more opportunities for women, youth, and small businesses across Ethiopia.
