Kenyan and Nigerian workers make generative AI outputs reliable at work
Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses
Human-Computer InteractionComputers and Society
Summary
Generative AI tools can help make drafts easily, but people in small businesses in Kenya and Nigeria found the real challenge was making those AI drafts trustworthy enough to share with clients. The researchers found workers had to do extra efforts, like checking, fixing, and adapting AI outputs to local languages and traditions. These extra tasks made AI useful at work but required ongoing care. The study suggests new ways AI tools could better support these real-world needs.
What this means in practice
- •For small business software developers: Design AI writing tools that include steps for users to verify, edit, and localize outputs to fit local language and cultural contexts as shown in Kenyan and Nigerian SMBs.$Commercial implications: Allows creation of AI tools tailored to African SMBs needing reliable, localized drafts, filling gaps in existing generic AI products.
- •For enterprise ai implementation teams: Plan workflows that integrate human accountability and iterative repair to handle AI drafts before client circulation, based on observed practices in African SMB contexts.
Authors
Najeeb Gambo Abdulhamid, Stephanie Nyairo, Liz Ankrah, Jacki O'Neill
Abstract
Generative AI is widely framed as democratizing skilled work by lowering the barrier to producing professional output. Drawing on 21 semi-structured interviews with 27 workers across 21 small and medium-sized businesses (SMBs) in Kenya and Nigeria (July to October 2023), we find that availability alone did not make participation workable. In participants' accounts, drafting was rarely the hard part. The consequential work was making AI-assisted drafts defensible enough to circulate to clients. We identify five intertwined practices through which participants made generative AI workable: using it as draft labour, steering outputs through iterative interaction, switching and comparing across tools, performing release work to make outputs accountable before circulation, and repairing context misfit when outputs failed local requirements. We make two central contributions. First, we specify release work as a distinct, boundary-facing form of accountability labour, evidenced through verification gates, traceability reconstruction when sources fail, and non-delegation boundaries that decide what can ship. Second, we show how localization and representational failures, which we call context misfit, operate in everyday work as a recurring breakdown-and-repair class, including failures of local language, institutional knowledge, and the credible representation of African people and places, and we document the repair repertoire they demanded. Reframing democratization as workable participation, we argue that reported gains were conditional on this accountability and repair labour, and we translate the findings into four design directions for workplace generative AI. We treat Kenya and Nigeria as two situated cases, not as a representative sample of SMBs or the Global South.