AI for Decision Making Across Scales: Why Ecosystem Conditions, Not Algorithms, Determine Who Benefits

Many of humanity's hardest problems, from flying spacecraft on Mars to advising farmers facing climate shocks, are at their core problems of turning signals into decisions. AI is fast becoming the technology that does this translation, turning radio signals, satellite images, environmental measurements, and human language into insights that people and institutions can act on. In this way, AI is becoming a web that links fields once kept apart by scale, geography, and discipline. Yet this web does not reach everyone. It is held back by overlapping gaps in electricity, connectivity, computing power, language coverage, and the hidden human labor that keeps AI safe. Drawing on talks from the AI for Decision Making session of the Fourth U.S.--Africa Frontiers of Science, Engineering, and Medicine Symposium (Dakar, Senegal, February 2026), this commentary shows how investment in AI can connect data, expertise, and communities to address shared global challenges. It points to four conditions that cut across every case: closing gaps in infrastructure, ending the linguistic and cultural exclusion built into large language models, closing the gap between accuracy in the lab and impact in the field, and fixing the structural contradictions in the human supply chain behind responsible AI. Meeting these conditions is not optional; they should be treated as core design requirements.