In this interview with JUSTICE OKAMGBA, Metaheuristic’s Chief Executive Officer and Founder, Toye Apampa, explains why Nigerian banks are more ready to adopt AI, while other sectors trail behind due to weak data governance and inadequate foundational systems You monitor Nigeria’s digital governance progress.
What key gaps are currently holding the country back? I’ve spent the last fourteen years inside some of Britain’s most data-heavy organisations: Lloyds, British Airways, Marks & Spencer, WorldRemit, and Rank Group. In environments like those, data governance isn’t a nice-to-have.
It is how you keep regulators at bay and the business running, whether you are deciding if someone qualifies for credit or ensuring a gambling platform isn’t serving a vulnerable customer. Most of my conversations with senior executives across those organisations came back to the same question: can we trust the numbers we are seeing?
Underneath that was almost always the same problem. User data lived in one system, transactional data in another, and marketing data in a third. When the time came to stitch it all together for a single view of the customer, the identifiers didn’t match. Sometimes the data wasn’t even there at the point of stitching.
Processing had to be rerun to unpick legacy joins that had been feeding incorrect insights into the boardroom for months. By the time you fix it, decisions have already been made. That is the reality of data governance at scale. It is not glamorous, but it is the foundation every downstream decision rests on.
When I look at Nigeria through that lens, the frameworks are more impressive than people give them credit for. The Nigeria Data Protection Act and the NITDA AI Code of Practice are serious, well-drafted documents. The UNDP Digital Development Compass scores Nigeria’s Data and Privacy framework at 4.17 out of 5.
Metaheuristic, my data and AI consultancy (mheuristic.com), built the Nigeria Digital Governance Tracker on top of that Compass framework specifically because the underlying data were too important to leave in a global dashboard. Decision-makers here needed it surfaced in a form they could actually use.
The gap lies in the plumbing underneath the policy. Where the policy scores high, implementation capacity sits at 2.5. The laws exist, but the operational muscle to comply with them, the ability to actually execute these mandates across ministries and private enterprises, is the next frontier for the Nigerian digital economy.
How ready are African organisations for AI adoption beyond the hype? Across the board, readiness is often lower than the noise suggests, and I say that as a technologist, not a critic. I have helped UK institutions prepare for AI. It is slow, unglamorous work: cataloguing data, documenting its origin, and defining what a customer means across departments that have used different names for twenty years.
None of that makes the news, yet it is the entire difference between an AI model you can trust and one you cannot defend. The risk for many emerging markets is that the conversation about AI has arrived before the conversation about data foundations has had a chance to finish.