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By Sunday Ani
A product manager with expertise in digital operations, product merchandising and strategic project management, Babalola Oladeji, has said that AI literacy, not coding models, but understanding failure, bias, drift and evaluation, is important for a successful product manager.
In an interview, Babalola said for an AI product manager to stay relevant, he has to function in three layers; product fundamentals, discovery, prioritization, storytelling and metrics.
“Secondly, AI literacy, not coding models, but understanding failure, bias, drift and evaluation.
“And thirdly, leadership working with ML teams, setting expectations for AI behavior as well as making ethical calls when it’s uncomfortable.
‘That is where the future PM sits, especially in Africa where the stakes are high and trust matters deeply,” he stated.
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He noted that what separates an AI-powered PM from someone building AI products is that an AI-powered PM can build any product fintech, health, e-commerce and logistics, usimg AI to work smarter, while a PM building AI products is responsible for a product where AI must work correctly or people get hurt, misled or lose trust.
“In Africa, this distinction matters a lot. For example, if an AI healthcare product gives advice without explanation, users may simply not trust it or worse, misuse it.
“In my healthcare research, I saw this clearly. People hesitated because they didn’t understand why the system was recommending something.
“That’s not a “ChatGPT problem. That’s AI product management trust, explainability and responsibility,” he said.
He emphasised that AI should be the second brain, not a decision-maker. “For discovery, use AI to cluster feedback from calls, chats, surveys, especially in markets like Nigeria where feedback is noisy and informal.
“For strategy, he uses AI to pressure-test ideas pricing, positioning, personas. But he never ship without validating with real users.
“For experimentation, faster hypotheses, faster copy variants, faster onboarding and education content.
“For execution, this is where AI saves time. The key is this: clear problem, measurable outcome and trust built into the experience. That is how AI actually improves products, not just productivity,” he stated.
