Practical applications of AI for rural credit assessment, agricultural advisory and last-mile inclusion — what works, what doesn't.
AI in development conversation tends to oscillate between two extremes — utopian transformation and dismissive scepticism. The field reality is more granular: a few use cases are genuinely transformative, many are useful, and a long tail is hype.
Three categories work today. Credit assessment models that combine bureau data with alternative signals to underwrite the thin-file rural borrower. Agricultural advisory systems that turn local language voice queries into season-specific guidance. And programme-level decision support that lets a district manager see what is working and what is not.
Three categories are still over-sold. Chatbots that try to replace human field staff. Predictive models built on data that simply does not exist in rural contexts. And anything that asks the end user to have a smartphone, data plan, English literacy and trust in the screen — all at once.
The right design principle is humility about the rail, ambition about the outcome. Build for the woman shopkeeper as she actually is, not as the deck assumes.