The Hyper-Promise of Artificial Intelligence for Hyper-Personalization

The author proposes a paradigm shift: use AI to hyper-personalize development, treating beneficiaries as individual “investees” rather than members of broad target groups. In this model, algorithms select and support people based on rich data (with informed permission), changing donor-recipient dynamics toward autonomy, iterative communication, and outcome-linked support. The private sector already does this at scale (post-“Long Tail” era), monetizing personal data and using AI for customer targeting and credit scoring; development can adapt similar practices with guardrails. An enabling environment exists: widespread social/mobile data, data portability services (Data Transfer Project, digi.me), and a strong case for leaning on call detail records (CDRs) over noisier social data because CDRs mirror real actions and are analytically accessible (e.g., Flowminder/FlowKit).

The article catalogs dozen-plus concerns—from bias and privacy to legality, coercion, selection transparency, multiple SIMs/accounts, and the risk of techno-utopianism—and stresses that approaches must be consensual, explainable, and context-aware, especially in information-poor settings.

The call to action: build internal AI expertise, collaborate with experienced firms, pilot responsibly, and evolve principles through practice rather than paralysis.