Across Africa and the Middle East, interest in artificial intelligence has never been higher. Boards are asking for AI strategies, and almost every organisation we meet is running at least one experiment. Yet only a small share of those experiments reach production and deliver measurable value.
The pattern behind the failures is remarkably consistent: the project starts with a model or a tool, not with a business process. A team gets access to a generative AI platform, builds an impressive demo, and then discovers that the data it needs is scattered, the process it is meant to improve is undocumented, and nobody owns the outcome.
Start with a painful, measurable process
The organisations that succeed pick a process that is high-volume, rules-heavy and already measured — claims processing, customer enquiries, invoice matching, report preparation. They document how it works today, agree the baseline, and only then ask where AI can remove effort or improve decisions.
Fix the data you actually need
You do not need a perfect enterprise data platform before you start. You need clean, accessible data for the first use case — and a plan to reuse that work for the next one.
Design for adoption from day one
An AI assistant that staff do not trust will not be used. Involve the people who do the work, train them, and make it easy to give feedback. Put governance in place early: what data can be used, who approves outputs, and how you will monitor quality.
Adopting AI is ultimately an operating-model change, not a software installation. Treat it that way and the results follow.