Building AI/ML Products That Users Actually Want

By Kayode Olayiwola · July 2026 · 8 min read

I've been building AI products for over a decade, and here's the uncomfortable truth: most AI products fail not because of technical limitations, but because they solve the wrong problems.

The AI Product Trap

It's easy to fall in love with the technology. Machine learning is fascinating—you can build models that learn patterns, make predictions, and automate decisions. But here's what I've learned building AI products at Prembly, Cuoral, and across 30+ countries in Africa: the technology is the easy part.

The hard part? Understanding what problem you're actually solving and whether AI is even the right solution.

Start With the Problem, Not the Solution

When we started building Cuoral's Silent Churn Intelligence Platform, we didn't start with "how can we use AI?" We started with a real business problem: businesses lose customers and don't know why.

Customers don't always churn loudly—they just fade away. They stop engaging, reduce usage, and eventually leave. By the time you notice, it's too late. That's the problem. AI was just the tool we used to solve it.

Three Questions Before Building Any AI Product

1. Can you solve this without AI?

Seriously. If you can solve it with a simple rule-based system, do that first. AI adds complexity—model training, data pipelines, monitoring, retraining. Only use it when the problem genuinely requires learning from data.

2. Do you have the right data?

You can't build an AI product without data. And not just any data—you need labeled, clean, and representative data. At Prembly, we process millions of identity verification requests. That data is gold for building fraud detection models. Without it, you're guessing.

3. Will users trust the AI's decisions?

This is where most AI products fail. Users need to understand why the AI made a decision. In fraud detection, we can't just say "this transaction is fraudulent." We need to explain why—and give users the ability to override when necessary.

Build for the User, Not the Demo

AI demos are impressive. You show a model that can predict churn with 95% accuracy, and investors love it. But here's what matters in production: does it help users make better decisions?

At Cuoral, we don't just predict which customers will churn. We tell businesses:

Which specific friction points are causing the issue
What actions they can take to prevent it
How confident we are in the prediction

That's actionable intelligence. That's what users actually want.

The Africa Factor

Building AI products in Africa comes with unique challenges. Infrastructure isn't always reliable. Internet connectivity can be spotty. Users have different expectations and constraints.

This forces you to build better products. You can't rely on heavy models that require constant internet connections. You need lightweight, efficient solutions that work in low-bandwidth environments. Ironically, these constraints make your product better for everyone—not just African users.

Final Thoughts

AI is a tool, not a product. The best AI products are the ones where users barely notice the AI is there—they just notice that the product works better than anything else they've tried.

Focus on the problem. Build for real users in real environments. And only use AI when it's genuinely the best solution.

That's how you build AI products that users actually want.

Have thoughts on AI product development? Connect with me on LinkedIn or Twitter.