Eniola should pitch a computational study on AI automation risk in Nigeria's pharmaceutical and healthcare workforce — a sector she can speak to with genuine insider authority as a PCN-licensed pharmacist and former clinical pharmacist who has already built IMPRINT (AI-driven drug-liability screening) and studied AMR genomics. The concrete proposal: apply the Acemoglu-Restrepo occupation-task-exposure framework to Nigerian Pharmacists Council workforce data using Python/ODE/Bayesian modelling — roles she already codes fluently — to quantify which pharmaceutical tasks (dispensing, drug review, pharmacovigilance) are most vulnerable to AI substitution and model the distributional shock across a low-income LMIC economy. This directly addresses the gap the reviewer identified in the prior Africa submission ('no actual analysis') with tools Eniola genuinely possesses, closes the AI-safety loop (AI displacing pharmacovigilance creates drug safety blind spots → catastrophic outcome potential), and is unique: no prior sprint entry has examined healthcare/pharmaceutical labour markets in Nigeria specifically.