Dr. Sunny Ndubuisi Okonkwo holds a Professional Doctorate in Data Science and AI from the University of East London. He has served as Head of Data Analytics and Information Management at Ikeja Electric Plc, Nigeria's largest electricity distribution company, since 2015, where he has led AI strategy, predictive infrastructure programs, and large-scale data governance initiatives. His earlier career spans data leadership roles at Sterling Bank Plc and Globacom Limited.A published author, award-winning film producer, and international keynote speaker, Dr. Okonkwo is also the founder of Libra Scale World, a data science and AI education platform built to serve African and global learners. He is a member of AAAI, ACM, and IEEE, a recipient of the AI Leadership Award at the Global Big Data Summit 2023, and a regular contributor to conversations at the intersection of AI, equity, and institutionaltransformation.

From Data Scarcity to Decision Intelligence: How Emerging Markets Are Redefining Smart Systems and Intelligent TransformationThe dominant narrative around smart systems and intelligent transformation has been written almost entirely from the vantage point of data-rich environments. Mature sensor networks, decades of clean labeled datasets, stable regulatory frameworks, and predictable infrastructure have shaped how the world defines what intelligence in a system actually looks like. But that is not how most of the world operates. Across emerging markets in Africa, Southeast Asia, and Latin America, organizations are building decision-critical AI systems under conditions that the standard playbook was never designed to handle: fragmented data pipelines, inconsistent connectivity, legacy infrastructure running alongside modern machine learning models, and millions of end users whose needs have been systematically excluded from the training corpora of leading AI systems. The result is not inferior transformation. It is a fundamentally different kind of intelligence, built from necessity, calibrated to survive constraint, and surprisingly generative in its outcomes. Drawing from fifteen years of applied AI and data science work across Nigeria's energy distribution sector, financial services, and telecommunications, this presentation makes the case that the most instructive breakthroughs in intelligent transformation are not happening in the best-resourced environments. They are happening at the edges, where data is incomplete, where the cost of a wrong prediction carries real human consequences, and where organizations have had to develop decision intelligence frameworks that work before the conditions are ideal. The presentation introduces the Decision Intelligence Gap, a structural concept that measures the distance between what an organization knows and what it needs to know to act with confidence. It then traces how organizations in high-constraint environments have closed that gap through predictive inference under uncertainty, federated data architectures, and human-in-the-loop validation systems that combine machine learning outputs with institutional knowledge in ways that neither pure AI nor pure human judgment could achieve alone. Attendees will leave with a reframed understanding of what smart systems look like in practice, a set of transferable principles drawn from real deployments, and a stronger appreciation for why the next wave of AI and data science leadership may well come from the places the field has historically overlooked. Key Themes▸ Decision intelligence under data scarcity and infrastructure constraint▸ Federated and hybrid AI architectures in low-connectivity environments▸ Human-in-the-loop systems and institutional knowledge integration▸ Equity, fairness, and representation in AI systems built for global deployment▸ Lessons from emerging market transformation for the global AI community