A strong temptation in the "AI for social good" space is a bias towards more efficient solutions through automation or a machine-only intervention. In this talk I give several examples where going in with this assumption results in sub-optimal solutions and the need for a human-machine collaboration becomes evident. A side effect of this is usually a move from simple automation to a more context-aware design of a potential solution. I highlight the need for considering context as an integral factor in human-machine collaboration through examples from practical deployments of AI solutions in the developing world context.