When Smart Tech Meets Skeptical Users: Victor Ologun’s Research on AI in Low-Income Markets

Monday, May 26, 2025

Before he began publishing research, Victor Oluwatosin Ologun was on the frontlines of digital adoption in Nigeria. As Marketing Manager at Newedge Finance Limited, he led the company’s pioneering “buy now, pay later” (BNPL) campaign for smartphones. At the time, it was a new concept to most Nigerians.

The launch succeeded with Easybuy expanding to nearly 30 states within two years. But Victor noticed something beyond the numbers: even when the product clearly solved a need, many customers hesitated. They asked countless questions, doubted repayment systems, and only signed up after trust was earned.

That lesson stayed with him. Technology doesn’t spread simply because it works. Instead, it spreads when people believe it works for them. This realization would shape Victor’s next chapter as he pursued a Master’s in Information Systems at Le Moyne College in the United States, where he now also researches the human side of digital transformation.

Artificial Intelligence (AI) is transforming industries worldwide, but in low-income countries, adoption rarely follows the same smooth path seen in wealthier economies. This is the central theme of Smart Tech, Scared Users, a study co-authored by Victor, which examines how AI-powered solutions for handling cyberthreat-related customer complaints are received in these markets.

The research highlights a paradox: while AI tools can dramatically improve the speed and accuracy of complaint resolution, many users remain hesitant to embrace them. “Technology alone doesn’t guarantee acceptance,” Victor explains. “In places where trust in digital systems is fragile, people often prefer human interaction over automated efficiency.”

The study identifies reasons for this hesitation: concerns about data privacy, fear of impersonal or inaccurate responses, and limited understanding of how AI systems function. These factors fuel suspicion, especially in areas like digital banking and e-commerce, where financial risks feel high.

Instead of recommending blind adoption, Victor’s work suggests hybrid models: AI tools can handle early complaint screening while trained human agents resolve sensitive or complex issues. “It’s not about replacing people,” he says. “It’s about making sure technology works within the trust realities of each market.”

The paper also offers practical strategies for businesses: communicate clearly about how AI handles data, train both staff and customers, and roll out AI features gradually to build confidence.

While the findings are rooted in low-income contexts, their relevance is global. As digital transformation accelerates everywhere, even advanced markets face the challenge of balancing automation with trust. Victor’s research is a reminder that strategy is as much about psychology and culture as it is about algorithms.

For him, the mission is clear: “Tech adoption isn’t just about installing tools. It’s about earning confidence. That’s the bridge between innovation and impact.”