In this episode of AI Conversations: Africa in Focus, we tackle a fundamental but often overlooked question: Why are you building that ML system in the first place?

Segun explores the critical distinction between building machine learning models for research versus deploying them to solve real business problems. Too often, teams celebrate marginal gains in accuracy while ignoring whether their models are moving the needle on key business metrics like customer retention, conversions, or cost reduction.

You’ll learn why success in a business setting demands more than a high-performing model—it demands alignment with business objectives, reliable and scalable systems, and a clear experimental approach, such as A/B testing, to measure actual impact.

Whether you're a data scientist, ML engineer, or business leader interested in AI, this episode offers practical insights on how to build ML solutions that matter.

Tune in to discover how to move from model obsession to business impact.